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Category articles

Cardiology Cardiology

A deep learning model for early risk prediction of heart failure with preserved ejection fraction by DNA methylation profiles combined with clinical features.

In Clinical epigenetics

BACKGROUND : Heart failure with preserved ejection fraction (HFpEF), affected collectively by genetic and environmental factors, is the common subtype of chronic heart failure. Although the available risk assessment methods for HFpEF have achieved some progress, they were based on clinical or genetic features alone. Here, we have developed a deep learning framework, HFmeRisk, using both 5 clinical features and 25 DNA methylation loci to predict the early risk of HFpEF in the Framingham Heart Study Cohort.

RESULTS : The framework incorporates Least Absolute Shrinkage and Selection Operator and Extreme Gradient Boosting-based feature selection, as well as a Factorization-Machine based neural network-based recommender system. Model discrimination and calibration were assessed using the AUC and Hosmer-Lemeshow test. HFmeRisk, including 25 CpGs and 5 clinical features, have achieved the AUC of 0.90 (95% confidence interval 0.88-0.92) and Hosmer-Lemeshow statistic was 6.17 (P = 0.632), which outperformed models with clinical characteristics or DNA methylation levels alone, published chronic heart failure risk prediction models and other benchmark machine learning models. Out of them, the DNA methylation levels of two CpGs were significantly correlated with the paired transcriptome levels (R < -0.3, P < 0.05). Besides, DNA methylation locus in HFmeRisk were associated with intercellular signaling and interaction, amino acid metabolism, transport and activation and the clinical variables were all related with the mechanism of occurrence of HFpEF. Together, these findings give new evidence into the HFmeRisk model.

CONCLUSION : Our study proposes an early risk assessment framework for HFpEF integrating both clinical and epigenetic features, providing a promising path for clinical decision making.

Zhao Xuetong, Sui Yang, Ruan Xiuyan, Wang Xinyue, He Kunlun, Dong Wei, Qu Hongzhu, Fang Xiangdong

2022-Jan-19

DNA methylation, Deep learning, Early risk prediction, Heart failure with preserved ejection fraction

Internal Medicine Internal Medicine

Integrated Tuberculosis and COVID-19 Activities in Karachi and Tuberculosis Case Notifications.

In Tropical medicine and infectious disease

As the COVID-19 pandemic surged, lockdowns led to the cancellation of essential health services. As part of our Zero TB activities in Karachi, we adapted our approach to integrate activities for TB and COVID-19 to decrease the impact on diagnosis and linkage to care for TB treatment. We implemented the following: (1) integrated COVID-19 screening and testing within existing TB program activities, along with the use of an artificial intelligence (AI) software reader on digital chest X-rays; (2) home delivery of medication; (3) use of telehealth and mental health counseling; (4) provision of PPE; (5) burnout monitoring of health workers; and (6) patient safety and disinfectant protocol. We used programmatic data for six districts of Karachi from January 2018 to March 2021 to explore the time trends in case notifications, the impact of the COVID-19 pandemic, and service adaptations in the city. The case notifications in all six districts in Karachi were over 80% of the trend-adjusted expected notifications with three districts having over 90% of the expected case notifications. Overall, Karachi reached 90% of the expected case notifications during the COVID-19 pandemic. The collaborative efforts by the provincial TB program and private sector partners facilitated this reduced loss in case notifications.

Malik Amyn A, Hussain Hamidah, Maniar Rabia, Safdar Nauman, Mohiuddin Amal, Riaz Najam, Pasha Aneeta, Khan Salman, Kazmi Syed Saleem Hasan, Kazmi Ershad, Khowaja Saira

2022-Jan-15

COVID-19, active case finding, case notification, screening, tuberculosis

General General

Speech- and Language-Based Classification of Alzheimer's Disease: A Systematic Review.

In Bioengineering (Basel, Switzerland)

BACKGROUND : Alzheimer's disease (AD) has paramount importance due to its rising prevalence, the impact on the patient and society, and the related healthcare costs. However, current diagnostic techniques are not designed for frequent mass screening, delaying therapeutic intervention and worsening prognoses. To be able to detect AD at an early stage, ideally at a pre-clinical stage, speech analysis emerges as a simple low-cost non-invasive procedure.

OBJECTIVES : In this work it is our objective to do a systematic review about speech-based detection and classification of Alzheimer's Disease with the purpose of identifying the most effective algorithms and best practices.

METHODS : A systematic literature search was performed from Jan 2015 up to May 2020 using ScienceDirect, PubMed and DBLP. Articles were screened by title, abstract and full text as needed. A manual complementary search among the references of the included papers was also performed. Inclusion criteria and search strategies were defined a priori.

RESULTS : We were able: to identify the main resources that can support the development of decision support systems for AD, to list speech features that are correlated with the linguistic and acoustic footprint of the disease, to recognize the data models that can provide robust results and to observe the performance indicators that were reported.

DISCUSSION : A computational system with the adequate elements combination, based on the identified best-practices, can point to a whole new diagnostic approach, leading to better insights about AD symptoms and its disease patterns, creating conditions to promote a longer life span as well as an improvement in patient quality of life. The clinically relevant results that were identified can be used to establish a reference system and help to define research guidelines for future developments.

Vigo Inês, Coelho Luis, Reis Sara

2022-Jan-11

Alzheimer’s disease (AD), classification, features, machine learning (ML), mild cognitive impairment (MCI), speech

General General

A Novel Hybrid Parametric and Non-Parametric Optimisation Model for Average Technical Efficiency Assessment in Public Hospitals during and Post-COVID-19 Pandemic.

In Bioengineering (Basel, Switzerland)

The COVID-19 pandemic has had a significant impact on hospitals and healthcare systems around the world. The cost of business disruption combined with lingering COVID-19 costs has placed many public hospitals on a course to insolvency. To quickly return to financial stability, hospitals should implement efficiency measure. An average technical efficiency (ATE) model made up of data envelopment analysis (DEA) and stochastic frontier analysis (SFA) for assessing efficiency in public hospitals during and after the COVID-19 pandemic is offered. The DEA method is a non-parametric method that requires no information other than the input and output quantities. SFA is a parametric method that considers stochastic noise in data and allows statistical testing of hypotheses about production structure and degree of inefficiency. The rationale for using these two competing approaches is to balance each method's strengths, weaknesses and introduce a novel integrated approach. To show the applicability and efficacy of the proposed hybrid VRS-CRS-SFA (VCS) model, a case study is presented.

Mirmozaffari Mirpouya, Yazdani Reza, Shadkam Elham, Khalili Seyed Mohammad, Tavassoli Leyla Sadat, Boskabadi Azam

2021-Dec-27

COVID-19, artificial intelligence, average technical efficiency, data envelopment analysis, parametric and non-parametric models, public hospitals

General General

A Multibranch of Convolutional Neural Network Models for Electroencephalogram-Based Motor Imagery Classification.

In Biosensors

Automatic high-level feature extraction has become a possibility with the advancement of deep learning, and it has been used to optimize efficiency. Recently, classification methods for Convolutional Neural Network (CNN)-based electroencephalography (EEG) motor imagery have been proposed, and have achieved reasonably high classification accuracy. These approaches, however, use the CNN single convolution scale, whereas the best convolution scale varies from subject to subject. This limits the precision of classification. This paper proposes multibranch CNN models to address this issue by effectively extracting the spatial and temporal features from raw EEG data, where the branches correspond to different filter kernel sizes. The proposed method's promising performance is demonstrated by experimental results on two public datasets, the BCI Competition IV 2a dataset and the High Gamma Dataset (HGD). The results of the technique show a 9.61% improvement in the classification accuracy of multibranch EEGNet (MBEEGNet) from the fixed one-branch EEGNet model, and 2.95% from the variable EEGNet model. In addition, the multibranch ShallowConvNet (MBShallowConvNet) improved the accuracy of a single-scale network by 6.84%. The proposed models outperformed other state-of-the-art EEG motor imagery classification methods.

Altuwaijri Ghadir Ali, Muhammad Ghulam

2022-Jan-03

Convolutional Neural Networks (CNN), brain computer interfaces (BCI), deep learning (DL), electroencephalography (EEG), motor imagery (MI)

Radiology Radiology

Neuroimaging-based brain-age prediction of first-episode schizophrenia and the alteration of brain age after early medication.

In The British journal of psychiatry : the journal of mental science

BACKGROUND : Neuroimaging- and machine-learning-based brain-age prediction of schizophrenia is well established. However, the diagnostic significance and the effect of early medication on first-episode schizophrenia remains unclear.

AIMS : To explore whether predicted brain age can be used as a biomarker for schizophrenia diagnosis, and the relationship between clinical characteristics and brain-predicted age difference (PAD), and the effects of early medication on predicted brain age.

METHOD : The predicted model was built on 523 diffusion tensor imaging magnetic resonance imaging scans from healthy controls. First, the brain-PAD of 60 patients with first-episode schizophrenia, 60 healthy controls and 21 follow-up patients from the principal data-set and 40 pairs of individuals in the replication data-set were calculated. Next, the brain-PAD between groups were compared and the correlations between brain-PAD and clinical measurements were analysed.

RESULTS : The patients showed a significant increase in brain-PAD compared with healthy controls. After early medication, the brain-PAD of patients decreased significantly compared with baseline (P < 0.001). The fractional anisotropy value of 31/33 white matter tract features, which related to the brain-PAD scores, had significantly statistical differences before and after measurements (P < 0.05, false discovery rate corrected). Correlation analysis showed that the age gap was negatively associated with the positive score on the Positive and Negative Syndrome Scale in the principal data-set (r = -0.326, P = 0.014).

CONCLUSIONS : The brain age of patients with first-episode schizophrenia may be older than their chronological age. Early medication holds promise for improving the patient's brain ageing. Neuroimaging-based brain-age prediction can provide novel insights into the understanding of schizophrenia.

Xi Yi-Bin, Wu Xu-Sha, Cui Long-Biao, Bai Li-Jun, Gan Shuo-Qiu, Jia Xiao-Yan, Li Xuan, Xu Yong-Qiang, Kang Xiao-Wei, Guo Fan, Yin Hong

2021-Dec-02

First-episode schizophrenia, brain-PAD, cognitive, medication, neuroimaging

General General

On Algorithmic Fairness in Medical Practice.

In Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees

The application of machine-learning technologies to medical practice promises to enhance the capabilities of healthcare professionals in the assessment, diagnosis, and treatment, of medical conditions. However, there is growing concern that algorithmic bias may perpetuate or exacerbate existing health inequalities. Hence, it matters that we make precise the different respects in which algorithmic bias can arise in medicine, and also make clear the normative relevance of these different kinds of algorithmic bias for broader questions about justice and fairness in healthcare. In this paper, we provide the building blocks for an account of algorithmic bias and its normative relevance in medicine.

Grote Thomas, Keeling Geoff

2022-Jan

algorithmic bias, discrimination, fairness, machine learning, medical practice

General General

Predict initial subthalamic nucleus stimulation outcome in Parkinson's disease with brain morphology.

In CNS neuroscience & therapeutics

AIM : Subthalamic nucleus deep brain stimulation (STN-DBS) has been reported to be effective in treating motor symptoms in Parkinson's disease (PD), which may be attributed to changes in the brain network. However, the association between brain morphology and initial STN-DBS efficacy, as well as the performance of prediction using neuroimaging, has not been well illustrated. Therefore, we aim to investigate these issues.

METHODS : In the present study, 94 PD patients underwent bilateral STN-DBS, and the initial stimulation efficacy was evaluated. Brain morphology was examined by magnetic resonance imaging (MRI). The volume of tissue activated in the motor STN was measured with MRI and computed tomography. The prediction of stimulation efficacy was achieved with a support vector machine, using brain morphology and other features, after feature selection and hyperparameter optimization.

RESULTS : A higher stimulation efficacy was correlated with a thicker right precentral cortex. No association with subcortical gray or white matter volumes was observed. These morphological features could estimate the individual stimulation response with an r value of 0.5678, an R2 of 0.3224, and an average error of 11.4%. The permutation test suggested these predictions were not based on chance.

CONCLUSION : Our results indicate that changes in morphology are associated with the initial stimulation motor response and could be used to predict individual initial stimulation-related motor responses.

Chen Yingchuan, Zhu Guanyu, Liu Yuye, Liu Defeng, Yuan Tianshuo, Zhang Xin, Jiang Yin, Du Tingting, Zhang Jianguo

2022-Jan-20

brain morphology, efficacy, machine learning, subthalamic nucleus deep brain stimulation

General General

Incorporating genome-based phylogeny and functional similarity into diversity assessments helps to resolve a global collection of human gut metagenomes.

In Environmental microbiology

Tree-based diversity measures incorporate phylogenetic or functional relatedness into comparisons of microbial communities. This can improve the identification of explanatory factors compared to tree-agnostic diversity measures. However, applying tree-based diversity measures to metagenome data is more challenging than for single-locus sequencing (e.g., 16S rRNA gene). Utilizing the Genome Taxonomy Database (GTDB) for species-level metagenome profiling allows for functional diversity measures based on genomic content or traits inferred from it. Still, it is unclear how metagenome-based assessments of microbiome diversity benefit from incorporating phylogeny or function into measures of diversity. We assessed this by measuring phylogeny-based, function-based, and tree-agnostic diversity measures from a large, global collection of human gut metagenomes composed of 30 studies and 2943 samples. We found tree-based measures to explain phenotypic variation (e.g., westernization, disease status, and gender) better or equivalent to tree-agnostic measures. Ecophylogenetic and functional diversity measures provided unique insight into how microbiome diversity was partitioned by phenotype. Tree-based measures greatly improved machine learning model performance for predicting westernization, disease status, and gender, relative to models trained solely on tree-agnostic measures. Our findings illustrate the usefulness of tree- and function-based measures for metagenomic assessments of microbial diversity, which is a fundamental component of microbiome science. This article is protected by copyright. All rights reserved.

Youngblut Nicholas D, de la Cuesta-Zuluaga Jacobo, Ley Ruth E

2022-Jan-20

General General

Composition identification and functional verification of bacterial community in disease-suppressive soils by machine learning.

In Environmental microbiology

It has been widely reported that probiotic consortia in the rhizosphere can enhance the plant resistance to pathogens. However, the general composition and functional profiles of bacterial community in soils which suppress multiple diseases for various plants remain largely unknown. Here, we combined metadata analysis with machine learning to identify the general patterns of bacterial-community composition in disease-suppressive soils. Disease-suppressive soils significantly enriched Firmicutes and Actinobacteria but showed a decrease in Proteobacteria and Bacteroidetes. Our machine-learning models accurately identified the disease-conducive and -suppressive soils with 54 biomarker genera, 28 of which were potentially beneficial. We further carried out a successive passaging experiment with the susceptible rps2 mutant of Arabidopsis thaliana invaded by Pseudomonas syringae pv. tomato DC3000 (avrRpt2) for functional verification of potential beneficial bacteria. The disease-suppressive ability of Kribbella, Nocardioides and Bacillus were confirmed, and they positively activated the pathogen-associated molecular patterns-triggered immunity pathway. Results also showed that chemical control by pesticides in agricultural production decreased the disease-suppressive ability of soil. This study provides a method for accurately predicting the occurrence of multiple disease in soil and identified potential beneficial bacteria to guide a wide range of multiple-strain biological control strategies in agricultural management. This article is protected by copyright. All rights reserved.

Zhang Zhenyan, Zhang Qi, Cui Hengzheng, Li Yan, Xu Nuohan, Lu Tao, Chen Jian, Penuelas Josep, Hu Baolan, Qian Haifeng

2022-Jan-20

General General

Natural language processing in psychiatry: the promises and perils of a transformative approach.

In The British journal of psychiatry : the journal of mental science

A person's everyday language can indicate patterns of thought and emotion predictive of mental illness. Here, we discuss how natural language processing methods can be used to extract indicators of mental health from language to help address long-standing problems in psychiatry, along with the potential hazards of this new technology.

Rezaii Neguine, Wolff Phillip, Price Bruce H

2022-Jan-07

Natural language processing, machine learning, precision medicine, prediction and classification of diseases, privacy

General General

Relationships between global functioning and neuropsychological predictors in subjects at high risk of psychosis or with a recent onset of depression.

In The world journal of biological psychiatry : the official journal of the World Federation of Societies of Biological Psychiatry

OBJECTIVE : Psychotic disorders are frequently associated with decline in functioning and cognitive difficulties are observed in subjects at clinical high risk (CHR) for psychosis. In this work, we applied automatic approaches to neurocognitive and functioning measures, with the aim of investigating the link between global, social and occupational functioning, and cognition.

METHODS : 102 CHR subjects and 110 patients with recent onset depression (ROD) were recruited. Global assessment of functioning (GAF) related to symptoms (GAF-S) and disability (GAF-D). and global functioning social (GF-S) and role (GF-R), at baseline and of the previous month and year, and a set of neurocognitive measures, were used for classification and regression.

RESULTS : Neurocognitive measures related to GF-R at baseline (r = 0.20, p = 0.004), GF-S at present (r = 0.14, p = 0.042) and of the past year (r = 0.19, p = 0.005), for GAF-F of the past month (r = 0.24, p < 0.001) and GAF-D of the past year (r = 0.28, p = 0.002). Classification reached values of balanced accuracy of 61% for GF-R and GAF-D.

CONCLUSION : We found that neurocognition was related to psychosocial functioning. More specifically, a deficit in executive functions was associated to poor social and occupational functioning.

Squarcina Letizia, Kambeitz-Ilankovic Lana, Bonivento Carolina, Prunas Cecilia, Oldani Lucio, Wenzel Julian, Ruef Anne, Dwyer Dominic, Ferro Adele, Borgwardt Stefan, Kambeitz Joseph, Lichtenstein Theresa Katharina, Meisenzahl Eva, Pantelis Christos, Rosen Marlene, Upthegrove Rachel, Antonucci Linda A, Bertolino Alessandro, Lencer Rebekka, Ruhrmann Stephan, Salokangas Raimo R K, Schultze-Lutter Frauke, Chisholm Katharine, Stainton Alexandra, Wood Stephen J, Koutsouleris Nikolaos, Brambilla Paolo

2022-Jan-20

Cognition, classification, machine learning, neuropsychology

Public Health Public Health

Reconciling public health common good and individual privacy: new methods and issues in geoprivacy.

In International journal of health geographics

This article provides a state-of-the-art summary of location privacy issues and geoprivacy-preserving methods in public health interventions and health research involving disaggregate geographic data about individuals. Synthetic data generation (from real data using machine learning) is discussed in detail as a promising privacy-preserving approach. To fully achieve their goals, privacy-preserving methods should form part of a wider comprehensive socio-technical framework for the appropriate disclosure, use and dissemination of data containing personal identifiable information. Select highlights are also presented from a related December 2021 AAG (American Association of Geographers) webinar that explored ethical and other issues surrounding the use of geospatial data to address public health issues during challenging crises, such as the COVID-19 pandemic.

Kamel Boulos Maged N, Kwan Mei-Po, El Emam Khaled, Chung Ada Lai-Ling, Gao Song, Richardson Douglas B

2022-Jan-19

Geoprivacy, Location privacy, Machine learning, Privacy enhancing technology, Public health, Synthetic data

Public Health Public Health

Forecasting the COVID-19 vaccine uptake rate: an infodemiological study in the US.

In Human vaccines & immunotherapeutics ; h5-index 43.0

A year following the initial COVID-19 outbreak in China, many countries have approved emergency vaccines. Public-health practitioners and policymakers must understand the predicted populational willingness for vaccines and implement relevant stimulation measures. This study developed a framework for predicting vaccination uptake rate based on traditional clinical data - involving an autoregressive model with autoregressive integrated moving average (ARIMA) - and innovative web search queries - involving a linear regression with ordinary least squares/least absolute shrinkage and selection operator, and machine-learning with boost and random forest. For accuracy, we implemented a stacking regression for the clinical data and web search queries. The stacked regression of ARIMA (1,0,8) for clinical data and boost with support vector machine for web data formed the best model for forecasting vaccination speed in the US. The stacked regression provided a more accurate forecast. These results can help governments and policymakers predict vaccine demand and finance relevant programs.

Zhou Xingzuo, Li Yiang

2022-Jan-20

Public health, forecast, infodemiology, machine-learning, vaccine

Public Health Public Health

A Research Ethics Framework for the Clinical Translation of Healthcare Machine Learning.

In The American journal of bioethics : AJOB

The application of artificial intelligence and machine learning (ML) technologies in healthcare have immense potential to improve the care of patients. While there are some emerging practices surrounding responsible ML as well as regulatory frameworks, the traditional role of research ethics oversight has been relatively unexplored regarding its relevance for clinical ML. In this paper, we provide a comprehensive research ethics framework that can apply to the systematic inquiry of ML research across its development cycle. The pathway consists of three stages: (1) exploratory, hypothesis-generating data access; (2) silent period evaluation; (3) prospective clinical evaluation. We connect each stage to its literature and ethical justification and suggest adaptations to traditional paradigms to suit ML while maintaining ethical rigor and the protection of individuals. This pathway can accommodate a multitude of research designs from observational to controlled trials, and the stages can apply individually to a variety of ML applications.

McCradden Melissa D, Anderson James A, A Stephenson Elizabeth, Drysdale Erik, Erdman Lauren, Goldenberg Anna, Zlotnik Shaul Randi

2022-Jan-20

Ethics committees, IRB (Institutional Review Board), health care delivery, human subjects research, informed consent, research ethics

General General

[Advances in gastroenterology and hepatology 2021].

In Revue medicale suisse

Among the recent advances in gastroenterology, colonoscopy with artificial intelligence is associated with a better quality of screening. In refractory UC, Ozanimod seems to be an interesting salvage treatment, which still needs to be validated by Swissmedic. Among the direct-acting anticoagulants, Rivaroxaban is more frequently associated with GI bleeding. The classification of oesophageal motor disorders has been recently revised, the Chicago v4.0 classification should be applied in diagnostic management. The use of Semaglutide seems to show very promising results in the management of metabolic steatosis. SARS-CoV-2 infection can be complicated by biliary tract disease, which can progress to hepatocellular failure.

Bastid Caroline, Bronstein Nathan, Ghassem-Zadeh Sahar, Flattet Yves, Gressot Pablo, Mathys Philippe, Spahr Laurent, Frossard Jean-Louis

2022-Jan-19

General General

Memristive Behaviors Dominated by Reversible Nucleation Dynamics of Phase-Change Nanoclusters.

In Small (Weinheim an der Bergstrasse, Germany)

One of the important steps for realizing artificial intelligence is identifying elementary units that are beneficial for neural network construction. A type of memristive behavior in which phase-change nanoclusters nucleate adaptively in two adjacent dielectric layers with distinct distribution patterns is demonstrated. This memristive system responds in potentiation to increased stimulation strength and fire action potential after threshold stimulation. Reversible nucleation of phase-change nanoclusters is confirmed after both in situ and ex situ examinations using high-resolution transmission electron microscopy. The dynamics at the nanoscale level dominates the actions of the two dielectric layers. The oscillation response over a long period is due to the competition between crystalline and amorphous phases in the layer near the bottom electrode. Weight mutation, that is, action potential firing, is caused by the blockage of the filament in the layer near the top electrode. The memristive system is compact and able to execute complicated functions of a complete neuron and performs an important role in neuromorphic computing.

Wan Qin, Zeng Fei, Sun Yiming, Chen Tongjin, Yu Junwei, Wu Huaqiang, Zhao Zhen, Cao Jiangli, Pan Feng

2022-Jan-20

memristive behavior, nanoclusters, nucleation dynamics, phase change

Radiology Radiology

Comparison of artificial intelligence to the veterinary radiologist's diagnosis of canine cardiogenic pulmonary edema.

In Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association

Application of artificial intelligence (AI) to improve clinical diagnosis is a burgeoning field in human and veterinary medicine. The objective of this prospective, diagnostic accuracy study was to determine the accuracy, sensitivity, and specificity of an AI-based software for diagnosing canine cardiogenic pulmonary edema from thoracic radiographs, using an American College of Veterinary Radiology-certified veterinary radiologist's interpretation as the reference standard. Five hundred consecutive canine thoracic radiographs made after-hours by a veterinary Emergency Department were retrieved. A total of 481 of 500 cases were technically analyzable. Based on the radiologist's assessment, 46 (10.4%) of these 481 dogs were diagnosed with cardiogenic pulmonary edema (CPE+). Of these cases, the AI software designated 42 of 46 as CPE+ and four of 46 as cardiogenic pulmonary edema negative (CPE-). Accuracy, sensitivity, and specificity of the AI-based software compared to radiologist diagnosis were 92.3%, 91.3%, and 92.4%, respectively (positive predictive value, 56%; negative predictive value, 99%). Findings supported using AI software screening for thoracic radiographs of dogs with suspected cardiogenic pulmonary edema to assist with short-term decision-making when a radiologist is unavailable.

Kim Eunbee, Fischetti Anthony J, Sreetharan Pratheev, Weltman Joel G, Fox Philip R

2022-Jan-19

Artificial intelligence, congestive heart failure, convolutional neural network, myxomatous mitral valve disease, thoracic radiograph

General General

Interpretability analysis for thermal sensation machine learning models: An exploration based on the SHAP approach.

In Indoor air

Machine learning models have been widely used for studying thermal sensations. However, the black-box properties of machine learning models lead to the lack of model transparency, and existing explanations for the thermal sensation models are generally flawed in terms of the perspectives of interpretable methods. In this study, we perform an interpretability analysis using the "SHapley Additive exPlanation" (SHAP) from game theory for thermal sensation machine learning models. The effects of different features on thermal sensations and typical decision routes in the models are investigated from both local and global perspectives, and the properties of correlation between features and thermal sensations and decision routes within machine learning models are summarized. The differences in the effects of features across samples reflect the effects of features on thermal sensations not only can be demonstrated by significant magnitudes but also by differentiation. The effects of features on thermal sensations often appear in the form of combinations of two to four features, which determine the final thermal sensation in most cases. Therefore, the neutral environment may actually be a dynamic high-dimensional space consisting of certain combinations of features in certain ranges with changing shapes.

Yang Yuren, Yuan Ye, Han Zhen, Liu Gang

2022-Jan-19

SHAP, interpretability analysis, local explanations, machine learning, neutral environment, thermal sensation

General General

Application of classic and soft computing for modeling yield and environmental final impact in vegetable production (a case study: transplanting onion in Isfahan province, Iran).

In Environmental science and pollution research international

This study aimed to develop a precision model between inputs and yield, and also between inputs (indirect emissions) and environmental final index (EFI) in onion farms through regression models (classic computing) and artificial intelligence models (soft computing). Required data were collected through direct measurement and questionnaire. To this end, 85 and 70 questionnaires were distributed among onion farmers in Fereydan and Falavarjan regions (Isfahan province, center of Iran), respectively. In the Fereydan region, the total energy input, onion yield, and water use efficiency (WUE) were obtained as 239496 MJ.ha-1, 97658 kg.ha-1, and 9.08 kg.m-3, respectively, while for Falavarjan region, these were obtained as 232221 MJ.ha-1, 94485 kg.ha-1, and 10.8 kg m-3, respectively. Electricity and diesel fuel were the most widely used inputs in the study areas. Based on the results related to the environmental indices, EFI was obtained as 547.38 and 363.54 pPt.t-1 for Fereydan and Falavarjan regions, respectively. The contribution of direct (such as CO2 and NH3) and indirect emissions (especially electricity) to the total EFI was 74 and 26% in Fereydan and 63 and 37% in Falavarjan region, respectively. Results related to the Cobb-Douglas regression model (CDR) showed that the effects of seed, manure, and labor on the onion yield were significant at 1% level of confidence. However, despite meeting the regression assumptions, the CDR model has predicted the yield and EFI with lower accuracy and higher error compared to artificial neural network models (ANNs), multi-layer perceptron (MLP), and adaptive neuro-fuzzy inference system (ANFIS). Soft computing (artificial intelligence) modeling showed that the ANFIS model (Grid Partitioning (GP)) has higher computational speed an lower error compared to multi-layer perceptron (MLP) models. Therefore, the comparison of the best GP and MLP models showed that the root-mean-square-error (RMSE) was obtained as 10.649 and 52.321 kg.ha-1 for yield and 25.08 and 40.94 pPt.ha-1 for EFI, respectively.

Elhami Behzad, Ghasemi Nejad Raeini Mahmoud, Taki Morteza, Marzban Afshin, Heidarisoltanabadi Mohsen

2022-Jan-20

Electricity, Environmental final index, Onion yield, Soft computing

General General

A Hybrid CNN-GLCM Classifier For Detection And Grade Classification Of Brain Tumor.

In Brain imaging and behavior

A supervised CNN Deep net classifier is proposed for the detection, classification and diagnosis of meningioma brain tumor using deep learning approach. This proposed method includes preprocessing, classification, and segmentation of the primary occurring brain tumor in adults. The proposed CNN Deep Net classifier extracts the features internally from the enhanced image and classifies them into normal and abnormal tumor images. The segmentation of tumor region is performed by global thresholding along with an area morphological function. This proposed method of fully automated classification and segmentation of brain tumor preserves the spatial invariance and inheritance. Furthermore, based on its feature attributes the proposed CNN Deep net classifier, classifies the detected tumor image either as (low grade) benign or (high grade) malignant. This proposed CNN Deep net classification approach with grading system is evaluated both quantitatively and qualitatively. The quantitative measures such as sensitivity, specificity, accuracy, Dice similarity coefficient, precision, F-score of the proposed classifier states a better segmentation accuracy and classification rate of 99.4% and 99.5% with respect to ground truth images.

Gurunathan Akila, Krishnan Batri

2022-Jan-19

Classification, Convolutional neural network (CNN), Meningioma, Segmentation

Radiology Radiology

Prognostic findings for ICU admission in patients with COVID-19 pneumonia: baseline and follow-up chest CT and the added value of artificial intelligence.

In Emergency radiology

Infection with SARS-CoV-2 has dominated discussion and caused global healthcare and economic crisis over the past 18 months. Coronavirus disease 19 (COVID-19) causes mild-to-moderate symptoms in most individuals. However, rapid deterioration to severe disease with or without acute respiratory distress syndrome (ARDS) can occur within 1-2 weeks from the onset of symptoms in a proportion of patients. Early identification by risk stratifying such patients who are at risk of severe complications of COVID-19 is of great clinical importance. Computed tomography (CT) is widely available and offers the potential for fast triage, robust, rapid, and minimally invasive diagnosis: Ground glass opacities (GGO), crazy-paving pattern (GGO with superimposed septal thickening), and consolidation are the most common chest CT findings in COVID pneumonia. There is growing interest in the prognostic value of baseline chest CT since an early risk stratification of patients with COVID-19 would allow for better resource allocation and could help improve outcomes. Recent studies have demonstrated the utility of baseline chest CT to predict intensive care unit (ICU) admission in patients with COVID-19. Furthermore, developments and progress integrating artificial intelligence (AI) with computer-aided design (CAD) software for diagnostic imaging allow for objective, unbiased, and rapid assessment of CT images.

Laino Maria Elena, Ammirabile Angela, Lofino Ludovica, Lundon Dara Joseph, Chiti Arturo, Francone Marco, Savevski Victor

2022-Jan-20

Artificial intelligence, COVID-19, Chest CT, ICU admission

General General

Fine human genetic map based on UK10K data set.

In Human genetics

Recombination is a major force that shapes genetic diversity. Determination of recombination rate is important and can theoretically be improved by increasing the sample size. However, it is nearly impossible to estimate recombination rates using traditional population genetics methods when the sample size is large because these methods are highly computationally demanding. In this study, we used a refined machine learning approach to estimate the recombination rate of the human genome using the UK10K human genomic dataset with 7,562 genomic sequences and its three subsets with 200, 400 and 2,000 genomic sequences. The estimation was performed under the human Out-of-Africa demographic model. We not only obtained an accurate human genetic map, but also found that the fluctuation of estimated recombination rate is reduced along the human genome when the sample size increases. The estimated UK10K recombination rate heterogeneity is less than that estimated from its subsets. Our results demonstrate how the sample size affects the estimated recombination rate, and analyses of a larger number of genomes result in a more precise estimation of recombination rate. The accurate genetic map based on UK10K data set is also expected to benefit other human biology researches.

Hao Ziqian, Du Pengyuan, Pan Yi-Hsuan, Li Haipeng

2022-Jan-20

Radiology Radiology

Prognosis of a second clinical event from baseline MRI in patients with a CIS: a multicenter study using a machine learning approach.

In Neuroradiology

PURPOSE : To predict the occurrence of a second clinical event in patients with a CIS suggestive of MS, from baseline magnetic resonance imaging (MRI), by means of a pattern recognition approach.

METHODS : Two hundred sixty-six patients with a CIS were recruited from four participating centers. Over a follow-up of 3 years, 130 patients had a second clinical episode and 136 did not. Grey matter and white matter T1-hypointensities masks segmented from 3D T1-weighted images acquired on 3 T scanners were used as features for the classification approach. Differences between CIS that remained CIS and those that developed a second event were assessed at a global level and at a regional level, arranging the regions according to their contribution to the classification model.

RESULTS : All classification metrics were around or even below 50% for both global and regional approaches. Accuracies did not change when T1-hypointensity maps were added to the model; just the specificity was increased up to 80%. Among the 30 regions with the largest contribution, 26 were grey matter and 4 were white matter regions. For grey matter, regions contributing showed either a larger or a smaller volume in the group of patients that remained CIS, compared to those with a second event. The volume of T1-hypointensities was always larger for the group that presented a second event.

CONCLUSIONS : Prediction of a second clinical event in CIS patients from baseline MRI seems to present a highly heterogeneous pattern, leading to very low classification accuracies. Adding the T1-hypointensity maps does not seem to improve the accuracy of the classification model.

Pareto Deborah, Garcia-Vidal Aran, Groppa Sergiu, Gonzalez-Escamilla Gabriel, Rocca Mara, Filippi Massimo, Enzinger Christian, Khalil Michael, Llufriu Sara, Tintoré Mar, Sastre-Garriga Jaume, Rovira Àlex

2022-Jan-20

Clinically isolated syndrome, Machine learning, Magnetic resonance imaging, Multiple sclerosis, Prognosis, Support vector machine

General General

Machine learning model for predicting acute kidney injury progression in critically ill patients.

In BMC medical informatics and decision making ; h5-index 38.0

BACKGROUND : Acute kidney injury (AKI) is a serve and harmful syndrome in the intensive care unit. Comparing to the patients with AKI stage 1/2, the patients with AKI stage 3 have higher in-hospital mortality and risk of progression to chronic kidney disease. The purpose of this study is to develop a prediction model that predict whether patients with AKI stage 1/2 will progress to AKI stage 3.

METHODS : Patients with AKI stage 1/2, when they were first diagnosed with AKI in the Medical Information Mart for Intensive Care, were included. We used the Logistic regression and machine learning extreme gradient boosting (XGBoost) to build two models which can predict patients who will progress to AKI stage 3. Established models were evaluated by cross-validation, receiver operating characteristic curve, and precision-recall curves.

RESULTS : We included 25,711 patients, of whom 2130 (8.3%) progressed to AKI stage 3. Creatinine, multiple organ failure syndromes were the most important in AKI progression prediction. The XGBoost model has a better performance than the Logistic regression model on predicting AKI stage 3 progression. Thus, we build a software based on our data which can predict AKI progression in real time.

CONCLUSIONS : The XGboost model can better identify patients with AKI progression than Logistic regression model. Machine learning techniques may improve predictive modeling in medical research.

Wei Canzheng, Zhang Lifan, Feng Yunxia, Ma Aijia, Kang Yan

2022-Jan-19

Acute kidney injury, Critical care, Extreme gradient boosting, Logistic Models

General General

A Novel Hybrid Parametric and Non-Parametric Optimisation Model for Average Technical Efficiency Assessment in Public Hospitals during and Post-COVID-19 Pandemic.

In Bioengineering (Basel, Switzerland)

The COVID-19 pandemic has had a significant impact on hospitals and healthcare systems around the world. The cost of business disruption combined with lingering COVID-19 costs has placed many public hospitals on a course to insolvency. To quickly return to financial stability, hospitals should implement efficiency measure. An average technical efficiency (ATE) model made up of data envelopment analysis (DEA) and stochastic frontier analysis (SFA) for assessing efficiency in public hospitals during and after the COVID-19 pandemic is offered. The DEA method is a non-parametric method that requires no information other than the input and output quantities. SFA is a parametric method that considers stochastic noise in data and allows statistical testing of hypotheses about production structure and degree of inefficiency. The rationale for using these two competing approaches is to balance each method's strengths, weaknesses and introduce a novel integrated approach. To show the applicability and efficacy of the proposed hybrid VRS-CRS-SFA (VCS) model, a case study is presented.

Mirmozaffari Mirpouya, Yazdani Reza, Shadkam Elham, Khalili Seyed Mohammad, Tavassoli Leyla Sadat, Boskabadi Azam

2021-Dec-27

COVID-19, artificial intelligence, average technical efficiency, data envelopment analysis, parametric and non-parametric models, public hospitals

Surgery Surgery

Assessment of an Artificial Intelligence Mandibular Osteotomy Design System: A Retrospective Study.

In Aesthetic plastic surgery ; h5-index 26.0

BACKGROUND : In this study, an AI osteotomy software was developed to design the presurgical plan of mandibular angle osteotomy, which is followed by the comparison between the software-designed presurgical plan and the traditional manual presurgical plan, thus assessing the practicability of applying the AI osteotomy software in clinical practices.

METHODS : (1) Develop an AI osteotomy software: design an algorithm based on convolutional neural networks capable of learning feature point and processing clustering segmentation; then, select 2296 cases of successful 3D mandibular angle osteotomy presurgical plans, followed by using those 2296 cases to train the deep learning algorithm; (2) compare the osteotomy presurgical plan of AI osteotomy software and that of manual: first step: randomly selecting 80 cases of typical female head 3D CTs, and designing those 80 cases by means of AI osteotomy software designing (group A) and manually designing (group B), respectively; second step: comparing several indexes of group A and those of group B, including the efficiency index (time from input original CT data to osteotomy presurgical plan output), the safety index (the minimum distance from the osteotomy plane to the mandibular canal), the symmetry indexes (bilateral difference of mandibular angle, mandibular ramus height and mandibular valgus angle) and aesthetic indexes (width ratio between middle and lower faces (M/L), mandibular angle and mandibular valgus angle).

RESULTS : The efficiency index: the design time of group A is 1.768 ± 0.768 min and that of group B is 26.108 ± 1.137 min, with P = 0.000; the safety index: The minimum distances from the osteotomy plane to the mandibular canal are 3.908 ± 0.361mm and 3.651 ± 0.437mm, p = 0.117 in groups A and B, respectively; The symmetry indexes: Bilateral differences of mandibular angle are 1.824 ± 1.834° and 1.567 ± 1.059° in groups A and B, respectively, with P = 0.278; bilateral differences of mandibular ramus height are 2.083 ± 1.263 and 2.965 ± 1.433, respectively, with P = 0.119 in groups A and B; Aesthetic indexes: M/L in groups A and B is 1.364 ± 0.074 and 1.371 ± 0.067, respectively, with P = 0.793; mandibular angles in groups A and B are 127.724 ± 5.800° and 127.242 ± 5.545°, respectively, with P = 0.681; Valgus angles in groups A and B are 11.474 ± 5.380 and 9.743 ± 4.620, respectively, with P = 0.273.

CONCLUSIONS : With high efficiency, as well as safety, symmetry and aesthetics equivalent to those of a manual design, the AI osteotomy software designing can be used as an alternative method for manual osteotomy designing.

LEVEL OF EVIDENCE IV : This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

Qiu Xiaohui, Han Wenqing, Dai Lu, Zhang Yan, Zhang Jie, Chai Gang, Lin Li, Zhou Jianda

2022-Jan-20

Artificial intelligence (AI), Convolutional neural network, Mandibular osteotomy

General General

A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings.

In Briefings in bioinformatics

Advancement in single-cell RNA sequencing leads to exponential accumulation of single-cell expression data. However, there is still lack of tools that could integrate these unlimited accumulations of single-cell expression data. Here, we presented a universal approach iSEEEK for integrating super large-scale single-cell expression via exploring expression rankings of top-expressing genes. We developed iSEEEK with 11.9 million single cells. We demonstrated the efficiency of iSEEEK with canonical single-cell downstream tasks on five heterogenous datasets encompassing human and mouse samples. iSEEEK achieved good clustering performance benchmarked against well-annotated cell labels. In addition, iSEEEK could transfer its knowledge learned from large-scale expression data on new dataset that was not involved in its development. iSEEEK enables identification of gene-gene interaction networks that are characteristic of specific cell types. Our study presents a simple and yet effective method to integrate super large-scale single-cell transcriptomes and would facilitate translational single-cell research from bench to bedside.

Shen Hongru, Shen Xilin, Feng Mengyao, Wu Dan, Zhang Chao, Yang Yichen, Yang Meng, Hu Jiani, Liu Jilei, Wang Wei, Li Yang, Zhang Qiang, Yang Jilong, Chen Kexin, Li Xiangchun

2022-Jan-20

deep learning, gene ranking, single-cell transcriptomes

Public Health Public Health

Digital Ageism: Challenges and Opportunities in Artificial Intelligence for Older Adults.

In The Gerontologist

Artificial intelligence (AI) and machine learning are changing our world through their impact on sectors including health care, education, employment, finance, and law. AI systems are developed using data that reflect the implicit and explicit biases of society, and there are significant concerns about how the predictive models in AI systems amplify inequity, privilege, and power in society. The widespread applications of AI have led to mainstream discourse about how AI systems are perpetuating racism, sexism, and classism; yet, concerns about ageism have been largely absent in the AI bias literature. Given the globally aging population and proliferation of AI, there is a need to critically examine the presence of age-related bias in AI systems. This forum article discusses ageism in AI systems and introduces a conceptual model that outlines intersecting pathways of technology development that can produce and reinforce digital ageism in AI systems. We also describe the broader ethical and legal implications and considerations for future directions in digital ageism research to advance knowledge in the field and deepen our understanding of how ageism in AI is fostered by broader cycles of injustice.

Chu Charlene H, Nyrup Rune, Leslie Kathleen, Shi Jiamin, Bianchi Andria, Lyn Alexandra, McNicholl Molly, Khan Shehroz, Rahimi Samira, Grenier Amanda

2022-Jan-20

Bias, Gerontology, Machine learning, Technology

Cardiology Cardiology

Non-invasive imaging as the cornerstone of cardiovascular precision medicine.

In European heart journal. Cardiovascular Imaging

AIMS : To provide an overview of the role of cardiovascular (CV) imaging in facilitating and advancing the field of precision medicine in CV disease.

METHODS AND RESULTS : Non-invasive CV imaging is essential to accurately and efficiently phenotype patients with heart disease, including coronary artery disease (CAD) and heart failure (HF). Various modalities, such as echocardiography, nuclear cardiology, cardiac computed tomography (CT), cardiovascular magnetic resonance (CMR), and invasive coronary angiography, and in some cases a combination, can be required to provide sufficient information for diagnosis and management. Taking CAD as an example, imaging is essential for the detection and functional assessment of coronary stenoses, as well as for the quantification of cardiac function and ischaemic myocardial damage. Furthermore, imaging may detect and quantify coronary atherosclerosis, potentially identify plaques at increased risk of rupture, and guide coronary interventions. In patients with HF, imaging helps identify specific aetiologies, quantify damage, and assess its impact on cardiac function. Imaging plays a central role in individualizing diagnosis and management and to determine the optimal treatment for each patient to increase the likelihood of response and improve patient outcomes.

CONCLUSIONS : Advances in all imaging techniques continue to improve accuracy, sensitivity, and standardization of functional and prognostic assessments, and identify established and novel therapeutic targets. Combining imaging with artificial intelligence, machine learning and computer algorithms, as well as with genomic, transcriptomic, proteomic, and metabolomic approaches, will become state of the art in the future to understand pathologies of CAD and HF, and in the development of new, targeted therapies.

Achenbach Stephan, Fuchs Friedrich, Goncalves Alexandra, Kaiser-Albers Claudia, Ali Ziad A, Bengel Frank M, Dimmeler Stefanie, Fayad Zahi A, Mebazaa Alexandre, Meder Benjamin, Narula Jagat, Shah Amil, Sharma Sanjay, Voigt Jens-Uwe, Plein Sven

2022-Jan-20

cardiovascular imaging, cardiovascular magnetic resonance, coronary computed tomography angiography, echocardiography, heart disease, molecular imaging

General General

Visual Analytics: A Method to Explore Natural Histories of Oral Epithelial Dysplasia.

In Frontiers in oral health

Risk assessment and follow-up of oral potentially malignant disorders in patients with mild or moderate oral epithelial dysplasia is an ongoing challenge for improved oral cancer prevention. Part of the challenge is a lack of understanding of how observable features of such dysplasia, gathered as data by clinicians during follow-up, relate to underlying biological processes driving progression. Current research is at an exploratory phase where the precise questions to ask are not known. While traditional statistical and the newer machine learning and artificial intelligence methods are effective in well-defined problem spaces with large datasets, these are not the circumstances we face currently. We argue that the field is in need of exploratory methods that can better integrate clinical and scientific knowledge into analysis to iteratively generate viable hypotheses. In this perspective, we propose that visual analytics presents a set of methods well-suited to these needs. We illustrate how visual analytics excels at generating viable research hypotheses by describing our experiences using visual analytics to explore temporal shifts in the clinical presentation of epithelial dysplasia. Visual analytics complements existing methods and fulfills a critical and at-present neglected need in the formative stages of inquiry we are facing.

Nowak Stan, Rosin Miriam, Stuerzlinger Wolfgang, Bartram Lyn

2021

artificial intelligence, low-grade oral dysplasia, oral cancer, prevention, visual analytics

General General

Predicting Treatment Nonresponse in Hispanic/Latino Children Receiving Silver Diamine Fluoride for Caries Arrest: A Pilot Study Using Machine Learning.

In Frontiers in oral health

Objectives: Silver diamine fluoride (SDF) is a nonsurgical therapy for the arrest and prevention of dental caries with demonstrated clinical efficacy. Approximately 20% of children receiving SDF fail to respond to treatment. The objective of this study was to develop a predictive model of treatment non-response using machine learning. Methods: An observational pilot study (N = 20) consisting of children with and without active decay and who did and did not respond to silver diamine fluoride provided salivary samples and plaque from infected and contralateral sites. 16S rRNA genes from samples were amplified and sequenced on an Illumina Miseq and analyzed using QIIME. The association between operational taxonomic units and treatment non-response was assessed using lasso regression and artificial neural networks. Results: Bivariate group comparisons of bacterial abundance indicate a number of genera were significantly different between non-responders and those who responded to SDF therapy. No differences were found between non-responders and caries-active subjects. Prevotella pallens and Veillonella denticariosi were retained in full lasso models and combined with clinical variables in a six-input multilayer perceptron. Discussion: The acidogenic and acid-tolerant nature of retained bacterial species may overcome the antimicrobial effects of SDF. Further research to validate the model in larger external samples is needed.

Ruff Ryan Richard, Paul Bidisha, Sierra Maria A, Xu Fangxi, Li Xin, Crystal Yasmi O, Saxena Deepak

2021

dental caries-most common childhood diseases, machine learning, microbiome, silver diamine fluoride, treatment nonresponse

oncology Oncology

Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma-A Systematic Review.

In Frontiers in oral health

The application of deep machine learning, a subfield of artificial intelligence, has become a growing area of interest in predictive medicine in recent years. The deep machine learning approach has been used to analyze imaging and radiomics and to develop models that have the potential to assist the clinicians to make an informed and guided decision that can assist to improve patient outcomes. Improved prognostication of oral squamous cell carcinoma (OSCC) will greatly benefit the clinical management of oral cancer patients. This review examines the recent development in the field of deep learning for OSCC prognostication. The search was carried out using five different databases-PubMed, Scopus, OvidMedline, Web of Science, and Institute of Electrical and Electronic Engineers (IEEE). The search was carried time from inception until 15 May 2021. There were 34 studies that have used deep machine learning for the prognostication of OSCC. The majority of these studies used a convolutional neural network (CNN). This review showed that a range of novel imaging modalities such as computed tomography (or enhanced computed tomography) images and spectra data have shown significant applicability to improve OSCC outcomes. The average specificity, sensitivity, area under receiving operating characteristics curve [AUC]), and accuracy for studies that used spectra data were 0.97, 0.99, 0.96, and 96.6%, respectively. Conversely, the corresponding average values for these parameters for computed tomography images were 0.84, 0.81, 0.967, and 81.8%, respectively. Ethical concerns such as privacy and confidentiality, data and model bias, peer disagreement, responsibility gap, patient-clinician relationship, and patient autonomy have limited the widespread adoption of these models in daily clinical practices. The accumulated evidence indicates that deep machine learning models have great potential in the prognostication of OSCC. This approach offers a more generic model that requires less data engineering with improved accuracy.

Alabi Rasheed Omobolaji, Bello Ibrahim O, Youssef Omar, Elmusrati Mohammed, Mäkitie Antti A, Almangush Alhadi

2021

deep learning, machine learning, oral cancer, prognostication, systematic reveiw

Surgery Surgery

Deep Learning for Automatic Image Segmentation in Stomatology and Its Clinical Application.

In Frontiers in medical technology

Deep learning has become an active research topic in the field of medical image analysis. In particular, for the automatic segmentation of stomatological images, great advances have been made in segmentation performance. In this paper, we systematically reviewed the recent literature on segmentation methods for stomatological images based on deep learning, and their clinical applications. We categorized them into different tasks and analyze their advantages and disadvantages. The main categories that we explored were the data sources, backbone network, and task formulation. We categorized data sources into panoramic radiography, dental X-rays, cone-beam computed tomography, multi-slice spiral computed tomography, and methods based on intraoral scan images. For the backbone network, we distinguished methods based on convolutional neural networks from those based on transformers. We divided task formulations into semantic segmentation tasks and instance segmentation tasks. Toward the end of the paper, we discussed the challenges and provide several directions for further research on the automatic segmentation of stomatological images.

Luo Dan, Zeng Wei, Chen Jinlong, Tang Wei

2021

automatic segmentation, convolutional neural networks, deep learning, stomatological image, transformer

General General

Living With Assistive Robotics: Exploring the Everyday Use of Exoskeleton for Persons With Spinal Cord Injury.

In Frontiers in medical technology

Background: Recent advancements in sensor technology and artificial intelligence mechanisms have led to a rapid increase in research and development of robotic orthoses or "exoskeletons" to support people with mobility problems. The purpose of this case study was to provide insight into the lived reality of using the assistive robotic exoskeleton ReWalk. Method: We used ethnographic techniques to explore the everyday experience and use of the assistive robotic device. Results: We found that the appropriation and integration of the technology within the patient's everyday lives required a social and collaborative effort, which continued into use. The decisions to utilise the technology (or not) was closely tied to physical, social, cultural, environmental, and psychological factors. Consequently, there was much variation in patients' perception of the technology and opportunities for support. Four themes emerged: (a) Meaning of mobility-physical mobility represents more than functional ability. Its present socio-cultural meaning is associated with an individual's self-identity and life priorities. (b) Accomplishing body-technique-integration with the body requires a long process of skill acquisition and re-embodiment. (c) Adaptation and adjustment in use-successful use of the technology was characterised by ongoing adjustment and adaptation of the technology and ways of using it. (d) Human element-introduction and sustained use of the exoskeleton demand a social and collaborative effort across the user's professional and lay resources. Conclusions: This study highlights that the development and implementation of the technology need to be grounded in a deep understanding of the day-to-day lives and experiences of the people that use them.

Lusardi Roberto, Tomelleri Stefano, Wherton Joseph

2021

ReWalk, ethnography, exoskeletons, rehabilitation robotics, technology-in-use

General General

ViralFP: A Web Application of Viral Fusion Proteins.

In Frontiers in medical technology

Viral fusion proteins are attached to the membrane of enveloped viruses (a group that includes Coronaviruses, Dengue, HIV and Influenza) and catalyze fusion between the viral and host membranes, enabling the virus to insert its genetic material into the host cell. Given the importance of these biomolecules, this work presents a centralized database containing the most relevant information on viral fusion proteins, available through a free-to-use web server accessible through the URL https://viralfp.bio.di.uminho.pt/. This web application contains several bioinformatic tools, such as Clustal sequence alignment and Weblogo, including as well a machine learning-based tool capable of predicting the location of fusion peptides (the component of fusion proteins that inserts into the host's cell membrane) within the fusion protein sequence. Given the crucial role of these proteins in viral infection, their importance as natural targets of our immune system and their potential as therapeutic targets, this web application aims to foster our ability to fight pathogenic viruses.

Moreira Pedro, Sequeira Ana Marta, Pereira Sara, Rodrigues Rúben, Rocha Miguel, Lousa Diana

2021

database, fusion peptides, fusion proteins, machine learning, web application

General General

Can minimal clinically important differences in patient reported outcome measures be predicted by machine learning in patients with total knee or hip arthroplasty? A systematic review.

In BMC medical informatics and decision making ; h5-index 38.0

OBJECTIVES : To systematically review studies using machine learning (ML) algorithms to predict whether patients undergoing total knee or total hip arthroplasty achieve an improvement as high or higher than the minimal clinically important differences (MCID) in patient reported outcome measures (PROMs) (classification problem).

METHODS : Studies were eligible to be included in the review if they collected PROMs both pre- and postintervention, reported the method of MCID calculation and applied ML. ML was defined as a family of models which automatically learn from data when selecting features, identifying nonlinear relations or interactions. Predictive performance must have been assessed using common metrics. Studies were searched on MEDLINE, PubMed Central, Web of Science Core Collection, Google Scholar and Cochrane Library. Study selection and risk of bias assessment (ROB) was conducted by two independent researchers.

RESULTS : 517 studies were eligible for title and abstract screening. After screening title and abstract, 18 studies qualified for full-text screening. Finally, six studies were included. The most commonly applied ML algorithms were random forest and gradient boosting. Overall, eleven different ML algorithms have been applied in all papers. All studies reported at least fair predictive performance, with two reporting excellent performance. Sample size varied widely across studies, with 587 to 34,110 individuals observed. PROMs also varied widely across studies, with sixteen applied to TKA and six applied to THA. There was no single PROM utilized commonly in all studies. All studies calculated MCIDs for PROMs based on anchor-based or distribution-based methods or referred to literature which did so. Five studies reported variable importance for their models. Two studies were at high risk of bias.

DISCUSSION : No ML model was identified to perform best at the problem stated, nor can any PROM said to be best predictable. Reporting standards must be improved to reduce risk of bias and improve comparability to other studies.

Langenberger Benedikt, Thoma Andreas, Vogt Verena

2022-Jan-20

General General

[Advances in gastroenterology and hepatology 2021].

In Revue medicale suisse

Among the recent advances in gastroenterology, colonoscopy with artificial intelligence is associated with a better quality of screening. In refractory UC, Ozanimod seems to be an interesting salvage treatment, which still needs to be validated by Swissmedic. Among the direct-acting anticoagulants, Rivaroxaban is more frequently associated with GI bleeding. The classification of oesophageal motor disorders has been recently revised, the Chicago v4.0 classification should be applied in diagnostic management. The use of Semaglutide seems to show very promising results in the management of metabolic steatosis. SARS-CoV-2 infection can be complicated by biliary tract disease, which can progress to hepatocellular failure.

Bastid Caroline, Bronstein Nathan, Ghassem-Zadeh Sahar, Flattet Yves, Gressot Pablo, Mathys Philippe, Spahr Laurent, Frossard Jean-Louis

2022-Jan-19

Internal Medicine Internal Medicine

Calibration-Free Cuffless Blood Pressure Estimation Based on a Population With a Diverse Range of Age and Blood Pressure.

In Frontiers in medical technology

This study presents a new blood pressure (BP) estimation algorithm utilizing machine learning (ML). A cuffless device that can measure BP without calibration would be precious for portability, continuous measurement, and comfortability, but unfortunately, it does not currently exist. Conventional BP measurement with a cuff is standard, but this method has various problems like inaccurate BP measurement, poor portability, and painful cuff pressure. To overcome these disadvantages, many researchers have developed cuffless BP estimation devices. However, these devices are not clinically applicable because they require advanced preparation before use, such as calibration, do not follow international standards (81060-1:2007), or have been designed using insufficient data sets. The present study was conducted to combat these issues. We recruited 127 participants and obtained 878 raw datasets. According to international standards, our diverse data set included participants from different age groups with a wide variety of blood pressures. We utilized ML to formulate a BP estimation method that did not require calibration. The present study also conformed to the method required by international standards while calculating the level of error in BP estimation. Two essential methods were applied in this study: (a) grouping the participants into five subsets based on the relationship between the pulse transit time and systolic BP by a support vector machine ensemble with bagging (b) applying the information from the wavelet transformation of the pulse wave and the electrocardiogram to the linear regression BP estimation model for each group. For systolic BP, the standard deviation of error for the proposed BP estimation results with cross-validation was 7.74 mmHg, which was an improvement from 17.05 mmHg, as estimated by the conventional pulse-transit-time-based methods. For diastolic BP, the standard deviation of error was 6.42 mmHg for the proposed BP estimation, which was an improvement from 14.05mmHg. The purpose of the present study was to demonstrate and evaluate the performance of the newly developed BP estimation ML method that meets the international standard for non-invasive sphygmomanometers in a population with a diverse range of age and BP.

Yamanaka Syunsuke, Morikawa Koji, Morita Hiroshi, Huh Ji Young, Yamamura Osamu

2021

continuous blood pressure, cuffless, electrocardiogram, machine learning, pulse wave, wavelet transformation

General General

Neural network classifiers for images of genetic conditions with cutaneous manifestations.

In HGG advances

Neural networks have shown strong potential in research and in healthcare. Mainly due to the need for large datasets, these applications have focused on common medical conditions, where more data are typically available. Leveraging publicly available data, we trained a neural network classifier on images of rare genetic conditions with skin findings. We used approximately 100 images per condition to classify 6 different genetic conditions. We analyzed both preprocessed images that were cropped to show only the skin lesions as well as more complex images showing features such as the entire body segment, the person, and/or the background. The classifier construction process included attribution methods to visualize which pixels were most important for computer-based classification. Our classifier was significantly more accurate than pediatricians or medical geneticists for both types of images and suggests steps for further research involving clinical scenarios and other applications.

Duong Dat, Waikel Rebekah L, Hu Ping, Tekendo-Ngongang Cedrik, Solomon Benjamin D

2022-Jan-13

artificial intelligence, deep learning, genetic conditions, genetics, genomics, machine learning, medical genomics

Pathology Pathology

The Role of Idiothetic Signals, Landmarks, and Conjunctive Representations in the Development of Place and Head-Direction Cells: A Self-Organizing Neural Network Model.

In Cerebral cortex communications

Place and head-direction (HD) cells are fundamental to maintaining accurate representations of location and heading in the mammalian brain across sensory conditions, and are thought to underlie path integration-the ability to maintain an accurate representation of location and heading during motion in the dark. Substantial evidence suggests that both populations of spatial cells function as attractor networks, but their developmental mechanisms are poorly understood. We present simulations of a fully self-organizing attractor network model of this process using well-established neural mechanisms. We show that the differential development of the two cell types can be explained by their different idiothetic inputs, even given identical visual signals: HD cells develop when the population receives angular head velocity input, whereas place cells develop when the idiothetic input encodes planar velocity. Our model explains the functional importance of conjunctive "state-action" cells, implying that signal propagation delays and a competitive learning mechanism are crucial for successful development. Consequently, we explain how insufficiently rich environments result in pathology: place cell development requires proximal landmarks; conversely, HD cells require distal landmarks. Finally, our results suggest that both networks are instantiations of general mechanisms, and we describe their implications for the neurobiology of spatial processing.

St Clere Smithe Toby, Stringer Simon M

2022

attractor networks, developmental neurobiology, head-direction cell, path integration, place cell

General General

The Role of Self-Improving Tutoring Systems in Fostering Pre-Service Teacher Self-Regulated Learning.

In Frontiers in artificial intelligence

Computer-based learning environments serve as a valuable asset to help strengthen teacher preparation and preservice teacher self-regulated learning. One of the most important advantages is the opportunity to collect ambient data unobtrusively as observable indicators of cognitive, affective, metacognitive, and motivational processes that mediate learning and performance. Ambient data refers to teacher interactions with the user interface that include but are not limited to timestamped clickstream data, keystroke and navigation events, as well as document views. We review the claim that computers designed as metacognitive tools can leverage the data to serve not only teachers in attaining the aims of instruction, but also researchers in gaining insights into teacher professional development. In our presentation of this claim, we review the current state of research and development of a network-based tutoring system called nBrowser, designed to support teacher instructional planning and technology integration. Network-based tutors are self-improving systems that continually adjust instructional decision-making based on the collective behaviors of communities of learners. A large part of the artificial intelligence resides in semantic web mining, natural language processing, and network algorithms. We discuss the implications of our findings to advance research into preservice teacher self-regulated learning.

Huang Lingyun, Dias Laurel, Nelson Elizabeth, Liang Lauren, Lajoie Susanne P, Poitras Eric G

2021

intelligent tutoring systems, nBrowser, network-based tutors, preservice teachers, self-improving tutors, self-regulated learning

General General

Deep Learning Based Superconducting Radio-Frequency Cavity Fault Classification at Jefferson Laboratory.

In Frontiers in artificial intelligence

This work investigates the efficacy of deep learning (DL) for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a large, high-power continuous wave recirculating linac that utilizes 418 SRF cavities to accelerate electrons up to 12 GeV. Recent upgrades to CEBAF include installation of 11 new cryomodules (88 cavities) equipped with a low-level RF system that records RF time-series data from each cavity at the onset of an RF failure. Typically, subject matter experts (SME) analyze this data to determine the fault type and identify the cavity of origin. This information is subsequently utilized to identify failure trends and to implement corrective measures on the offending cavity. Manual inspection of large-scale, time-series data, generated by frequent system failures is tedious and time consuming, and thereby motivates the use of machine learning (ML) to automate the task. This study extends work on a previously developed system based on traditional ML methods (Tennant and Carpenter and Powers and Shabalina Solopova and Vidyaratne and Iftekharuddin, Phys. Rev. Accel. Beams, 2020, 23, 114601), and investigates the effectiveness of deep learning approaches. The transition to a DL model is driven by the goal of developing a system with sufficiently fast inference that it could be used to predict a fault event and take actionable information before the onset (on the order of a few hundred milliseconds). Because features are learned, rather than explicitly computed, DL offers a potential advantage over traditional ML. Specifically, two seminal DL architecture types are explored: deep recurrent neural networks (RNN) and deep convolutional neural networks (CNN). We provide a detailed analysis on the performance of individual models using an RF waveform dataset built from past operational runs of CEBAF. In particular, the performance of RNN models incorporating long short-term memory (LSTM) are analyzed along with the CNN performance. Furthermore, comparing these DL models with a state-of-the-art fault ML model shows that DL architectures obtain similar performance for cavity identification, do not perform quite as well for fault classification, but provide an advantage in inference speed.

Vidyaratne Lasitha, Carpenter Adam, Powers Tom, Tennant Chris, Iftekharuddin Khan M, Rahman Md Monibor, Shabalina Anna S

2021

LINAC, convolutional neural networks, deep recurrent learning, fault identification, particle accelerator, superconducting radio-frequency cavities, time-series classification

General General

Cloud-Based Lung Tumor Detection and Stage Classification Using Deep Learning Techniques.

In BioMed research international ; h5-index 102.0

Artificial intelligence (AI), Internet of Things (IoT), and the cloud computing have recently become widely used in the healthcare sector, which aid in better decision-making for a radiologist. PET imaging or positron emission tomography is one of the most reliable approaches for a radiologist to diagnosing many cancers, including lung tumor. In this work, we proposed stage classification of lung tumor which is a more challenging task in computer-aided diagnosis. As a result, a modified computer-aided diagnosis is being considered as a way to reduce the heavy workloads and second opinion to radiologists. In this paper, we present a strategy for classifying and validating different stages of lung tumor progression, as well as a deep neural model and data collection using cloud system for categorizing phases of pulmonary illness. The proposed system presents a Cloud-based Lung Tumor Detector and Stage Classifier (Cloud-LTDSC) as a hybrid technique for PET/CT images. The proposed Cloud-LTDSC initially developed the active contour model as lung tumor segmentation, and multilayer convolutional neural network (M-CNN) for classifying different stages of lung cancer has been modelled and validated with standard benchmark images. The performance of the presented technique is evaluated using a benchmark image LIDC-IDRI dataset of 50 low doses and also utilized the lung CT DICOM images. Compared with existing techniques in the literature, our proposed method achieved good result for the performance metrics accuracy, recall, and precision evaluated. Under numerous aspects, our proposed approach produces superior outcomes on all of the applied dataset images. Furthermore, the experimental result achieves an average lung tumor stage classification accuracy of 97%-99.1% and an average of 98.6% which is significantly higher than the other existing techniques.

Kasinathan Gopi, Jayakumar Selvakumar

2022

Internal Medicine Internal Medicine

Circulating MicroRNAs for Diagnosis of Acute Pulmonary Embolism: Still a Long Way to Go.

In BioMed research international ; h5-index 102.0

Venous thromboembolism (VTE) represents the third most frequent cause of acute cardiovascular syndrome. Among VTE, acute pulmonary embolism (APE) is the most life-threatening complication. Due to the low specificity of symptoms clinical diagnosis of APE may be sometimes very difficult. Accordingly, the latest European guidelines only suggest clinical prediction tests for diagnosis of APE, eventually associated with D-dimer, a biomarker burdened by a very low specificity. A growing body of evidence is highlighting the role of miRNAs in hemostasis and thrombosis. Due to their partial inheritance and susceptibility to the environmental factors, miRNAs are increasingly described as active modifiers of the classical Virchow's triad. Clinical evidence on deep venous thrombosis reported specific miRNA signatures associated to thrombosis development, organization, recanalization, and resolution. Conversely, data of miRNA profiling as a predictor/diagnostic marker of APE are still preliminary. Here, we have summarized clinical evidence on the potential role of miRNA in diagnosis of APE. Despite some intriguing insight, miRNA assay is still far from any potential clinical application. Especially, the small sample size of cohorts likely represents the major limitation of published studies, so that extensive analysis of miRNA profiles with a machine learning approach are warranted in the next future. In addition, the cost-benefit ratio of miRNA assay still has a negative impact on their clinical application and routinely test.

Sobrero Matteo, Montecucco Fabrizio, Carbone Federico

2022

General General

Calib-Net: Calibrating the Low-Cost IMU via Deep Convolutional Neural Network.

In Frontiers in robotics and AI

The low-cost Inertial Measurement Unit (IMU) can provide orientation information and is widely used in our daily life. However, IMUs with bad calibration will provide inaccurate angular velocity and lead to rapid drift of integral orientation in a short time. In this paper, we present the Calib-Net which can achieve the accurate calibration of low-cost IMU via a simple deep convolutional neural network. Following a carefully designed mathematical calibration model, Calib-Net can output compensation components for gyroscope measurements dynamically. Dilation convolution is adopted in Calib-Net for spatio-temporal feature extraction of IMU measurements. We evaluate our proposed system on public datasets quantitively and qualitatively. The experimental results demonstrate that our Calib-Net achieves better calibration performance than other methods, what is more, and the estimated orientation with our Calib-Net is even comparable with the results from visual inertial odometry (VIO) systems.

Li Ruihao, Fu Chunlian, Yi Wei, Yi Xiaodong

2021

IMU calibration, deep neural network, orientation estimation, spatio-temporal, visual inertial odometry

General General

A Survey of Human Gait-Based Artificial Intelligence Applications.

In Frontiers in robotics and AI

We performed an electronic database search of published works from 2012 to mid-2021 that focus on human gait studies and apply machine learning techniques. We identified six key applications of machine learning using gait data: 1) Gait analysis where analyzing techniques and certain biomechanical analysis factors are improved by utilizing artificial intelligence algorithms, 2) Health and Wellness, with applications in gait monitoring for abnormal gait detection, recognition of human activities, fall detection and sports performance, 3) Human Pose Tracking using one-person or multi-person tracking and localization systems such as OpenPose, Simultaneous Localization and Mapping (SLAM), etc., 4) Gait-based biometrics with applications in person identification, authentication, and re-identification as well as gender and age recognition 5) "Smart gait" applications ranging from smart socks, shoes, and other wearables to smart homes and smart retail stores that incorporate continuous monitoring and control systems and 6) Animation that reconstructs human motion utilizing gait data, simulation and machine learning techniques. Our goal is to provide a single broad-based survey of the applications of machine learning technology in gait analysis and identify future areas of potential study and growth. We discuss the machine learning techniques that have been used with a focus on the tasks they perform, the problems they attempt to solve, and the trade-offs they navigate.

Harris Elsa J, Khoo I-Hung, Demircan Emel

2021

artificial intelligence, biometrics, human gait analysis, machine learning, review

General General

EEG-Driven Prediction Model of Oxcarbazepine Treatment Outcomes in Patients With Newly-Diagnosed Focal Epilepsy.

In Frontiers in medicine

Objective: Antiseizure medicine (ASM) is the first choice for patients with epilepsy. The choice of ASM is determined by the type of epilepsy or epileptic syndrome, which may not be suitable for certain patients. This initial choice of a particular drug affects the long-term prognosis of patients, so it is critical to select the appropriate ASMs based on the individual characteristics of a patient at the early stage of the disease. The purpose of this study is to develop a personalized prediction model to predict the probability of achieving seizure control in patients with focal epilepsy, which will help in providing a more precise initial medication to patients. Methods: Based on response to oxcarbazepine (OXC), enrolled patients were divided into two groups: seizure-free (52 patients), not seizure-free (NSF) (22 patients). We created models to predict patients' response to OXC monotherapy by combining Electroencephalogram (EEG) complexities and 15 clinical features. The prediction models were gradient boosting decision tree-Kolmogorov complexity (GBDT-KC) and gradient boosting decision tree-Lempel-Ziv complexity (GBDT-LZC). We also constructed two additional prediction models, support vector machine-Kolmogorov complexity (SVM-KC) and SVM-LZC, and these two models were compared with the GBDT models. The performance of the models was evaluated by calculating the accuracy, precision, recall, F1-score, sensitivity, specificity, and area under the curve (AUC) of these models. Results: The mean accuracy, precision, recall, F1-score, sensitivity, specificity, AUC of GBDT-LZC model after five-fold cross-validation were 81%, 84%, 91%, 87%, 91%, 64%, 81%, respectively. The average accuracy, precision, recall, F1-score, sensitivity, specificity, AUC of GBDT-KC model with five-fold cross-validation were 82%, 84%, 92%, 88%, 83%, 92%, 83%, respectively. We used the rank of absolute weights to separately calculate the features that have the most significant impact on the classification of the two models. Conclusion: (1) The GBDT-KC model has the potential to be used in the clinic to predict seizure-free with OXC monotherapy. (2). Electroencephalogram complexity, especially Kolmogorov complexity (KC) may be a potential biomarker in predicting the treatment efficacy of OXC in newly diagnosed patients with focal epilepsy.

Wang Bin, Han Xiong, Zhao Zongya, Wang Na, Zhao Pan, Li Mingmin, Zhang Yue, Zhao Ting, Chen Yanan, Ren Zhe, Hong Yang

2021

EEG complexity, gradient boosting decision tree (GBDT) model, machine learning, precision medicine, prediction model

General General

Predicting Ca2+ and Mg2+ ligand binding sites by deep neural network algorithm.

In BMC bioinformatics

BACKGROUND : Alkaline earth metal ions are important protein binding ligands in human body, and it is of great significance to predict their binding residues.

RESULTS : In this paper, Mg2+ and Ca2+ ligands are taken as the research objects. Based on the characteristic parameters of protein sequences, amino acids, physicochemical characteristics of amino acids and predicted structural information, deep neural network algorithm is used to predict the binding sites of proteins. By optimizing the hyper-parameters of the deep learning algorithm, the prediction results by the fivefold cross-validation are better than those of the Ionseq method. In addition, to further verify the performance of the proposed model, the undersampling data processing method is adopted, and the prediction results on independent test are better than those obtained by the support vector machine algorithm.

CONCLUSIONS : An efficient method for predicting Mg2+ and Ca2+ ligand binding sites was presented.

Sun Kai, Hu Xiuzhen, Feng Zhenxing, Wang Hongbin, Lv Haotian, Wang Ziyang, Zhang Gaimei, Xu Shuang, You Xiaoxiao

2022-Jan-20

Binding residue, Deep learning algorithm, Metal ion ligand, Protein

Radiology Radiology

Prognostic findings for ICU admission in patients with COVID-19 pneumonia: baseline and follow-up chest CT and the added value of artificial intelligence.

In Emergency radiology

Infection with SARS-CoV-2 has dominated discussion and caused global healthcare and economic crisis over the past 18 months. Coronavirus disease 19 (COVID-19) causes mild-to-moderate symptoms in most individuals. However, rapid deterioration to severe disease with or without acute respiratory distress syndrome (ARDS) can occur within 1-2 weeks from the onset of symptoms in a proportion of patients. Early identification by risk stratifying such patients who are at risk of severe complications of COVID-19 is of great clinical importance. Computed tomography (CT) is widely available and offers the potential for fast triage, robust, rapid, and minimally invasive diagnosis: Ground glass opacities (GGO), crazy-paving pattern (GGO with superimposed septal thickening), and consolidation are the most common chest CT findings in COVID pneumonia. There is growing interest in the prognostic value of baseline chest CT since an early risk stratification of patients with COVID-19 would allow for better resource allocation and could help improve outcomes. Recent studies have demonstrated the utility of baseline chest CT to predict intensive care unit (ICU) admission in patients with COVID-19. Furthermore, developments and progress integrating artificial intelligence (AI) with computer-aided design (CAD) software for diagnostic imaging allow for objective, unbiased, and rapid assessment of CT images.

Laino Maria Elena, Ammirabile Angela, Lofino Ludovica, Lundon Dara Joseph, Chiti Arturo, Francone Marco, Savevski Victor

2022-Jan-20

Artificial intelligence, COVID-19, Chest CT, ICU admission

General General

Automatic Segmentation of Novel Coronavirus Pneumonia Lesions in CT Images Utilizing Deep-Supervised Ensemble Learning Network.

In Frontiers in medicine

Background: The novel coronavirus disease 2019 (COVID-19) has been spread widely in the world, causing a huge threat to the living environment of people. Objective: Under CT imaging, the structure features of COVID-19 lesions are complicated and varied greatly in different cases. To accurately locate COVID-19 lesions and assist doctors to make the best diagnosis and treatment plan, a deep-supervised ensemble learning network is presented for COVID-19 lesion segmentation in CT images. Methods: Since a large number of COVID-19 CT images and the corresponding lesion annotations are difficult to obtain, a transfer learning strategy is employed to make up for the shortcoming and alleviate the overfitting problem. Based on the reality that traditional single deep learning framework is difficult to extract complicated and varied COVID-19 lesion features effectively that may cause some lesions to be undetected. To overcome the problem, a deep-supervised ensemble learning network is presented to combine with local and global features for COVID-19 lesion segmentation. Results: The performance of the proposed method was validated in experiments with a publicly available dataset. Compared with manual annotations, the proposed method acquired a high intersection over union (IoU) of 0.7279 and a low Hausdorff distance (H) of 92.4604. Conclusion: A deep-supervised ensemble learning network was presented for coronavirus pneumonia lesion segmentation in CT images. The effectiveness of the proposed method was verified by visual inspection and quantitative evaluation. Experimental results indicated that the proposed method has a good performance in COVID-19 lesion segmentation.

Peng Yuanyuan, Zhang Zixu, Tu Hongbin, Li Xiong

2021

COVID-19 lesion segmentation, deep learning, deep-supervised ensemble learning network, local and global features, transfer learning, under CT imaging

General General

BCNet: A Novel Network for Blood Cell Classification.

In Frontiers in cell and developmental biology

Aims: Most blood diseases, such as chronic anemia, leukemia (commonly known as blood cancer), and hematopoietic dysfunction, are caused by environmental pollution, substandard decoration materials, radiation exposure, and long-term use certain drugs. Thus, it is imperative to classify the blood cell images. Most cell classification is based on the manual feature, machine learning classifier or the deep convolution network neural model. However, manual feature extraction is a very tedious process, and the results are usually unsatisfactory. On the other hand, the deep convolution neural network is usually composed of massive layers, and each layer has many parameters. Therefore, each deep convolution neural network needs a lot of time to get the results. Another problem is that medical data sets are relatively small, which may lead to overfitting problems. Methods: To address these problems, we propose seven models for the automatic classification of blood cells: BCARENet, BCR5RENet, BCMV2RENet, BCRRNet, BCRENet, BCRSNet, and BCNet. The BCNet model is the best model among the seven proposed models. The backbone model in our method is selected as the ResNet-18, which is pre-trained on the ImageNet set. To improve the performance of the proposed model, we replace the last four layers of the trained transferred ResNet-18 model with the three randomized neural networks (RNNs), which are RVFL, ELM, and SNN. The final outputs of our BCNet are generated by the ensemble of the predictions from the three randomized neural networks by the majority voting. We use four multi-classification indexes for the evaluation of our model. Results: The accuracy, average precision, average F1-score, and average recall are 96.78, 97.07, 96.78, and 96.77%, respectively. Conclusion: We offer the comparison of our model with state-of-the-art methods. The results of the proposed BCNet model are much better than other state-of-the-art methods.

Zhu Ziquan, Lu Siyuan, Wang Shui-Hua, Górriz Juan Manuel, Zhang Yu-Dong

2021

ResNet-18, blood cells, convolutional neural network, randomized neural network, transfer learning

General General

Modeling the COVID-19 Epidemic With Multi-Population and Control Strategies in the United States.

In Frontiers in public health

As of January 19, 2021, the cumulative number of people infected with coronavirus disease-2019 (COVID-19) in the United States has reached 24,433,486, and the number is still rising. The outbreak of the COVID-19 epidemic has not only affected the development of the global economy but also seriously threatened the lives and health of human beings around the world. According to the transmission characteristics of COVID-19 in the population, this study established a theoretical differential equation mathematical model, estimated model parameters through epidemiological data, obtained accurate mathematical models, and adopted global sensitivity analysis methods to screen sensitive parameters that significantly affect the development of the epidemic. Based on the established precise mathematical model, we calculate the basic reproductive number of the epidemic, evaluate the transmission capacity of the COVID-19 epidemic, and predict the development trend of the epidemic. By analyzing the sensitivity of parameters and finding sensitive parameters, we can provide effective control strategies for epidemic prevention and control. After appropriate modifications, the model can also be used for mathematical modeling of epidemics in other countries or other infectious diseases.

Sun Deshun, Long Xiaojun, Liu Jingxiang

2021

COVID-19, control strategies, mathematical model, parameter estimate, sensitive analysis

General General

Learning From Biological and Computational Machines: Importance of SARS-CoV-2 Genomic Surveillance, Mutations and Risk Stratification.

In Frontiers in cellular and infection microbiology ; h5-index 53.0

The global coronavirus disease 2019 (COVID-19) pandemic has demonstrated the range of disease severity and pathogen genomic diversity emanating from a singular virus (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2). This diversity in disease manifestations and genomic mutations has challenged healthcare management and resource allocation during the pandemic, especially for countries such as India with a bigger population base. Here, we undertake a combinatorial approach toward scrutinizing the diagnostic and genomic diversity to extract meaningful information from the chaos of COVID-19 in the Indian context. Using methods of statistical correlation, machine learning (ML), and genomic sequencing on a clinically comprehensive patient dataset with corresponding with/without respiratory support samples, we highlight specific significant diagnostic parameters and ML models for assessing the risk of developing severe COVID-19. This information is further contextualized in the backdrop of SARS-CoV-2 genomic features in the cohort for pathogen genomic evolution monitoring. Analysis of the patient demographic features and symptoms revealed that age, breathlessness, and cough were significantly associated with severe disease; at the same time, we found no severe patient reporting absence of physical symptoms. Observing the trends in biochemical/biophysical diagnostic parameters, we noted that the respiratory rate, total leukocyte count (TLC), blood urea levels, and C-reactive protein (CRP) levels were directly correlated with the probability of developing severe disease. Out of five different ML algorithms tested to predict patient severity, the multi-layer perceptron-based model performed the best, with a receiver operating characteristic (ROC) score of 0.96 and an F1 score of 0.791. The SARS-CoV-2 genomic analysis highlighted a set of mutations with global frequency flips and future inculcation into variants of concern (VOCs) and variants of interest (VOIs), which can be further monitored and annotated for functional significance. In summary, our findings highlight the importance of SARS-CoV-2 genomic surveillance and statistical analysis of clinical data to develop a risk assessment ML model.

Bhat Shikha, Pandey Anuradha, Kanakan Akshay, Maurya Ranjeet, Vasudevan Janani Srinivasa, Devi Priti, Chattopadhyay Partha, Sharma Shimpa, Khyalappa Rajesh J, Joshi Meghnad G, Pandey Rajesh

2021

COVID-19, SARS-CoV-2, genomic surveillance, healthcare, machine learning, risk stratification

oncology Oncology

Multidisciplinary Tumor Board Smart Virtual Assistant in Locally Advanced Cervical Cancer: A Proof of Concept.

In Frontiers in oncology

Aim : The first prototype of the "Multidisciplinary Tumor Board Smart Virtual Assistant" is presented, aimed to (i) Automated classification of clinical stage starting from different free-text diagnostic reports; (ii) Resolution of inconsistencies by identifying controversial cases drawing the clinician's attention to particular cases worthy for multi-disciplinary discussion; (iii) Support environment for education and knowledge transfer to junior staff; (iv) Integrated data-driven decision making and standardized language and interpretation.

Patients and Method : Data from patients affected by Locally Advanced Cervical Cancer (LACC), FIGO stage IB2-IVa, treated between 2015 and 2018 were extracted. Magnetic Resonance (MR), Gynecologic examination under general anesthesia (EAU), and Positron Emission Tomography-Computed Tomography (PET-CT) performed at the time of diagnosis were the items from the Electronic Health Records (eHRs) considered for analysis. An automated extraction of eHR that capture the patient's data before the diagnosis and then, through Natural Language Processing (NLP), analysis and categorization of all data to transform source information into structured data has been performed.

Results : In the first round, the system has been used to retrieve all the eHR for the 96 patients with LACC. The system has been able to classify all patients belonging to the training set and - through the NLP procedures - the clinical features were analyzed and classified for each patient. A second important result was the setup of a predictive model to evaluate the patient's staging (accuracy of 94%). Lastly, we created a user-oriented operational tool targeting the MTB who are confronted with the challenge of large volumes of patients to be diagnosed in the most accurate way.

Conclusion : This is the first proof of concept concerning the possibility of creating a smart virtual assistant for the MTB. A significant benefit could come from the integration of these automated methods in the collaborative, crucial decision stages.

Macchia Gabriella, Ferrandina Gabriella, Patarnello Stefano, Autorino Rosa, Masciocchi Carlotta, Pisapia Vincenzo, Calvani Cristina, Iacomini Chiara, Cesario Alfredo, Boldrini Luca, Gui Benedetta, Rufini Vittoria, Gambacorta Maria Antonietta, Scambia Giovanni, Valentini Vincenzo

2021

artificial intelligence, chemoradiation (CRT), locally advanced cervical cancer, multidisciplinary tumor board smart virtual assistant, virtual medicine support

General General

Initial application of deep learning to borescope detection of endoscope working channel damage and residue.

In Endoscopy international open

Background and study aims  Outbreaks of endoscopy-related infections have prompted evaluation for potential contributing factors. We and others have demonstrated the utility of borescope inspection of endoscope working channels to identify occult damage that may impact the adequacy of endoscope reprocessing. The time investment and training necessary for borescope inspection have been cited as barriers preventing implementation. We investigated the utility of artificial intelligence (AI) for streamlining and enhancing the value of borescope inspection of endoscope working channels. Methods  We applied a deep learning AI approach to borescope inspection videos of the working channels of 20 endoscopes in use at our academic institution. We evaluated the sensitivity, accuracy, and reliability of this software for detection of endoscope working channel findings. Results  Overall sensitivity for AI-based detection of borescope inspection findings identified by gold standard endoscopist inspection was 91.4 %. Labels were accurate for 67 % of these working channel findings and accuracy varied by endoscope segment. Read-to-read variability was noted to be minimal, with test-retest correlation value of 0.986. Endoscope type did not predict accuracy of the AI system ( P  = 0.26). Conclusions  Harnessing the power of AI for detection of endoscope working channel damage and residue could enable sterile processing department technicians to feasibly assess endoscopes for working channel damage and perform endoscope reprocessing surveillance. Endoscopes that accumulate an unacceptable level of damage may be flagged for further manual evaluation and consideration for manufacturer evaluation/repair.

Barakat Monique T, Girotra Mohit, Banerjee Subhas

2022-Jan

General General

A decision support system for primary headache developed through machine learning.

In PeerJ

Background : Primary headache is a disorder with a high incidence and low diagnostic accuracy; the incidence of migraine and tension-type headache ranks first among primary headaches. Artificial intelligence (AI) decision support systems have shown great potential in the medical field. Therefore, we attempt to use machine learning to build a clinical decision-making system for primary headaches.

Methods : The demographic data and headache characteristics of 173 patients were collected by questionnaires. Decision tree, random forest, gradient boosting algorithm and support vector machine (SVM) models were used to construct a discriminant model and a confusion matrix was used to calculate the evaluation indicators of the models. Furthermore, we have carried out feature selection through univariate statistical analysis and machine learning.

Results : In the models, the accuracy, F1 score were calculated through the confusion matrix. The logistic regression model has the best discrimination effect, with the accuracy reaching 0.84 and the area under the ROC curve also being the largest at 0.90. Furthermore, we identified the most important factors for distinguishing the two disorders through statistical analysis and machine learning: nausea/vomiting and photophobia/phonophobia. These two factors represent potential independent factors for the identification of migraines and tension-type headaches, with the accuracy reaching 0.74 and the area under the ROC curve being at 0.74.

Conclusions : Applying machine learning to the decision-making system for primary headaches can achieve a high diagnostic accuracy. Among them, the discrimination effect obtained by the integrated algorithm is significantly better than that of a single learner. Second, nausea/vomiting, photophobia/phonophobia may be the most important factors for distinguishing migraine from tension-type headaches.

Liu Fangfang, Bao Guanshui, Yan Mengxia, Lin Guiming

2022

Discriminant model, Feature selection, Machine learning, Migraine, Primary headache, Tension-type headache

Surgery Surgery

Multimodal Imaging of Target Detection Algorithm under Artificial Intelligence in the Diagnosis of Early Breast Cancer.

In Journal of healthcare engineering

This study aimed to analyze the diagnostic value of multimodal images based on artificial intelligence target detection algorithms for early breast cancer, so as to provide help for clinical imaging examinations of breast cancer. This article combined residual block with inception block, constructed a new target detection algorithm to detect breast lumps, used deep convolutional neural network and ultrasound imaging in diagnosing benign and malignant breast lumps, took breast density grading with mammography, compared the convolutional neural network (CNN) algorithm with the proposed algorithm, and then applied the proposed algorithm to the diagnosis of 120 female patients with breast lumps. According to the results, accuracy rates of breast lump detection (94.76%), benign and malignant breast lumps diagnosis (98.22%), and breast grading (93.65%) with the algorithm applied in this study were significantly higher than those (75.67%, 87.23%, and 79.54%) with CNN algorithm, and the difference was statistically significant (P < 0.05); among 62 patients with malignant breast lumps of the 120 patients with breast lumps, 37 were patients with invasive ductal carcinoma, 8 with lobular carcinoma in situ, 16 with intraductal carcinoma, and 4 with mucinous carcinoma; among the remaining 58 patients with benign breast lumps, 28 were patients with fibrocystic breast disease, 17 with intraductal papilloma, 4 with breast hyperplasia, and 9 with adenopathy; the differences in shape, growth direction, edge, and internal echo of multimodal ultrasound imaging of patients with benign and malignant breast lumps had statistical significance (P < 0.05); the malignant constituent ratios of patients with breast density grades I to IV were 0%, 7.10%, 80.40%, and 100%, respectively. In short, the multimodal imaging diagnosis under the algorithm in this article was superior to CNN algorithm in all aspects; according to the judgment on benign and malignant breast lumps and breast density with multimodal imaging features, the higher the breast density, the higher the probability of breast cancer.

Jiang Meiping, Lei Sanlin, Zhang Junhui, Hou Liqiong, Zhang Meixiang, Luo Yingchun

2022

General General

Metaheuristics with Deep Learning-Enabled Parkinson's Disease Diagnosis and Classification Model.

In Journal of healthcare engineering

Parkinson's disease (PD) affects the movement of people, including the differences in writing skill, speech, tremor, and stiffness in muscles. It is significant to detect the PD at the initial stages so that the person can live a peaceful life for a longer time period. The serious levels of PD are highly risky as the patients get progressive stiffness, which results in the inability of standing or walking. Earlier studies have focused on the detection of PD effectively using voice and speech exams and writing exams. In this aspect, this study presents an improved sailfish optimization algorithm with deep learning (ISFO-DL) model for PD diagnosis and classification. The presented ISFO-DL technique uses the ISFO algorithm and DL model to determine PD and thereby enhances the survival rate of the person. The presented ISFO is a metaheuristic algorithm, which is inspired by a group of hunting sailfish to determine the optimum solution to the problem. Primarily, the ISFO algorithm is applied to derive an optimal subset of features with a fitness function of maximum classification accuracy. At the same time, the rat swarm optimizer (RSO) with the bidirectional gated recurrent unit (BiGRU) is employed as a classifier to determine the existence of PD. The performance validation of the IFSO-DL model takes place using a benchmark Parkinson's dataset, and the results are inspected under several dimensions. The experimental results highlighted the enhanced classification performance of the ISFO-DL technique, and therefore, the proposed model can be employed for the earlier identification of PD.

Bahaddad Adel A, Ragab Mahmoud, Ashary Ehab Bahaudien, Khalil Eied M

2022

General General

Data Mining in Employee Healthcare Detection Using Intelligence Techniques for Industry Development.

In Journal of healthcare engineering

Background : Even in today's environment, when there is a plethora of information accessible, it may be difficult to make appropriate choices for one's well-being. Data mining, machine learning, and computational statistics are among the most popular arenas of training today, and they are all aimed at secondary empowered person in making good decisions that will maximize the outcome of whatever working area they are involved with. Because the degree of rise in the number of patient roles is directly related to the rate of people growth and lifestyle variations, the healthcare sector has a significant need for data processing services. When it comes to cancer, the prognosis is an expression that relates to the possibility of the patient surviving in general, but it may also be used to describe the severity of the sickness as it will present itself in the patient's future timeline. Methodology. The proposed technique consists of three stages: input data acquisition, preprocessing, and classification. Data acquisition consists of input raw data which is followed by preprocessing to eliminate the missed data and the classification is carried out using ensemble classifier to analyze the stages of cancer. This study explored the combined influence of the prominent labels in conjunction with one another utilizing the multilabel classifier approach, which is successful. Finally, an ensemble classifier model has been constructed and experimentally validated to increase the accuracy of the classifier model, which has been previously shown. The entire performance of the recommended and tested models demonstrates a steady development of 2% to 6% over the baseline presentation on the baseline performance.

Results : Providing a good contribution to the general health welfare of noncommercial potential workers in the healthcare sector is an opportunity provided by this recommended job outcome. It is anticipated that alternative solutions to these constraints, as well as automation of the whole process flow of all five phases, will be the key focus of the work to be carried out shortly. Predicting health status of employee in industry or information trends is made easier by these data patterns. The proposed classifier achieves the accuracy rate of 93.265%.

Mehbodniya Abolfazl, Khan Ihtiram Raza, Chakraborty Sudeshna, Karthik M, Mehta Kamakshi, Ali Liaqat, Nuagah Stephen Jeswinde

2022

General General

Revealing potential diagnostic gene biomarkers of septic shock based on machine learning analysis.

In BMC infectious diseases ; h5-index 58.0

BACKGROUND : Sepsis is an inflammatory response caused by infection with pathogenic microorganisms. The body shock caused by it is called septic shock. In view of this, we aimed to identify potential diagnostic gene biomarkers of the disease.

MATERIAL AND METHODS : Firstly, mRNAs expression data sets of septic shock were retrieved and downloaded from the GEO (Gene Expression Omnibus) database for differential expression analysis. Functional enrichment analysis was then used to identify the biological function of DEmRNAs (differentially expressed mRNAs). Machine learning analysis was used to determine the diagnostic gene biomarkers for septic shock. Thirdly, RT-PCR (real-time polymerase chain reaction) verification was performed. Lastly, GSE65682 data set was utilized to further perform diagnostic and prognostic analysis of identified superlative diagnostic gene biomarkers.

RESULTS : A total of 843 DEmRNAs, including 458 up-regulated and 385 down-regulated DEmRNAs were obtained in septic shock. 15 superlative diagnostic gene biomarkers (such as RAB13, KIF1B, CLEC5A, FCER1A, CACNA2D3, DUSP3, HMGN3, MGST1 and ARHGEF18) for septic shock were identified by machine learning analysis. RF (random forests), SVM (support vector machine) and DT (decision tree) models were used to construct classification models. The accuracy of the DT, SVM and RF models were very high. Interestingly, the RF model had the highest accuracy. It is worth mentioning that ARHGEF18 and FCER1A were related to survival. CACNA2D3 and DUSP3 participated in MAPK signaling pathway to regulate septic shock.

CONCLUSION : Identified diagnostic gene biomarkers may be helpful in the diagnosis and therapy of patients with septic shock.

Fan Yonghua, Han Qiufeng, Li Jinfeng, Ye Gaige, Zhang Xianjing, Xu Tengxiao, Li Huaqing

2022-Jan-19

DEmRNAs (differentially expressed mRNAs), Diagnostic gene biomarkers, Machine learning analysis, Prognostic, Septic shock

General General

Automatic Segmentation of Novel Coronavirus Pneumonia Lesions in CT Images Utilizing Deep-Supervised Ensemble Learning Network.

In Frontiers in medicine

Background: The novel coronavirus disease 2019 (COVID-19) has been spread widely in the world, causing a huge threat to the living environment of people. Objective: Under CT imaging, the structure features of COVID-19 lesions are complicated and varied greatly in different cases. To accurately locate COVID-19 lesions and assist doctors to make the best diagnosis and treatment plan, a deep-supervised ensemble learning network is presented for COVID-19 lesion segmentation in CT images. Methods: Since a large number of COVID-19 CT images and the corresponding lesion annotations are difficult to obtain, a transfer learning strategy is employed to make up for the shortcoming and alleviate the overfitting problem. Based on the reality that traditional single deep learning framework is difficult to extract complicated and varied COVID-19 lesion features effectively that may cause some lesions to be undetected. To overcome the problem, a deep-supervised ensemble learning network is presented to combine with local and global features for COVID-19 lesion segmentation. Results: The performance of the proposed method was validated in experiments with a publicly available dataset. Compared with manual annotations, the proposed method acquired a high intersection over union (IoU) of 0.7279 and a low Hausdorff distance (H) of 92.4604. Conclusion: A deep-supervised ensemble learning network was presented for coronavirus pneumonia lesion segmentation in CT images. The effectiveness of the proposed method was verified by visual inspection and quantitative evaluation. Experimental results indicated that the proposed method has a good performance in COVID-19 lesion segmentation.

Peng Yuanyuan, Zhang Zixu, Tu Hongbin, Li Xiong

2021

COVID-19 lesion segmentation, deep learning, deep-supervised ensemble learning network, local and global features, transfer learning, under CT imaging

Pathology Pathology

Ultrasonic Intelligent Diagnosis of Papillary Thyroid Carcinoma Based on Machine Learning.

In Journal of healthcare engineering

Thyroid diseases are divided into papillary carcinoma and nodular diseases, which are very harmful to the human body. Ultrasound is a common diagnostic method for thyroid diseases. In the process of diagnosis, doctors need to observe the characteristics of ultrasound images, combined with professional knowledge and clinical experience, to give the disease situation of patients. However, different doctors have different clinical experience and professional backgrounds, and the diagnosis results lack objectivity and consistency, so an intelligent diagnosis technology for thyroid diseases based on the ultrasound image is needed in clinic, which can give objective and reliable diagnosis opinions on thyroid diseases by extracting the texture, shape, and other information of the image and assist doctors in clinical diagnosis. This paper mainly studies the intelligent ultrasonic diagnosis of papillary thyroid cancer based on machine learning, compares the ultrasonic characteristics of PTMC diagnosed by using the new ultrasound technology (CEUS and UE), and summarizes the differential diagnosis effect and clinical application value of the two technology methods for PTMC. In this paper, machine learning, diffuse thyroid image features, and RBM learning methods are used to study the ultrasonic intelligent diagnosis of papillary thyroid cancer based on machine learning. At the same time, the new contrast-enhanced ultrasound (CEUS) technology and ultrasound elastography (UE) technology are used to obtain the experimental phenomena in the experiment of ultrasonic intelligent diagnosis of papillary thyroid cancer. The results showed that 90% of the cases were diagnosed by contrast-enhanced ultrasound and confirmed by postoperative pathology. CEUS and UE have reliable practical value in the diagnosis of PTMC, and the combined application of CEUS and UE can improve the sensitivity and accuracy of PTMC diagnosis.

Zhou Heng, Liu Bin, Liu Yang, Huang Qunan, Yan Wei

2022

General General

Design of Resources Allocation in 6G Cybertwin Technology Using the Fuzzy Neuro Model in Healthcare Systems.

In Journal of healthcare engineering

In 6G edge communication networks, the machine learning models play a major role in enabling intelligent decision-making in case of optimal resource allocation in case of the healthcare system. However, it causes a bottleneck, in the form of sophisticated memory calculations, between the hidden layers and the cost of communication between the edge devices/edge nodes and the cloud centres, while transmitting the data from the healthcare management system to the cloud centre via edge nodes. In order to reduce these hurdles, it is important to share workloads to further eliminate the problems related to complicated memory calculations and transmission costs. The effort aims mainly to reduce storage costs and cloud computing associated with neural networks as the complexity of the computations increases with increasing numbers of hidden layers. This study modifies federated teaching to function with distributed assignment resource settings as a distributed deep learning model. It improves the capacity to learn from the data and assigns an ideal workload depending on the limited available resources, slow network connection, and more edge devices. Current network status can be sent to the cloud centre by the edge devices and edge nodes autonomously using cybertwin, meaning that local data are often updated to calculate global data. The simulation shows how effective resource management and allocation is better than standard approaches. It is seen from the results that the proposed method achieves higher resource utilization and success rate than existing methods. Index Terms are fuzzy, healthcare, bioinformatics, 6G wireless communication, cybertwin, machine learning, neural network, and edge.

Syed Salman Ali, Sheela Sobana Rani K, Mohammad Gouse Baig, Anil Kumar G, Chennam Krishna Keerthi, Jaikumar R, Natarajan Yuvaraj, Srihari K, Barakkath Nisha U, Sundramurthy Venkatesa Prabhu

2022

General General

Multiscale U-Net with Spatial Positional Attention for Retinal Vessel Segmentation.

In Journal of healthcare engineering

Retinal vessel segmentation is essential for the detection and diagnosis of eye diseases. However, it is difficult to accurately identify the vessel boundary due to the large variations of scale in the retinal vessels and the low contrast between the vessel and the background. Deep learning has a good effect on retinal vessel segmentation since it can capture representative and distinguishing features for retinal vessels. An improved U-Net algorithm for retinal vessel segmentation is proposed in this paper. To better identify vessel boundaries, the traditional convolutional operation CNN is replaced by a global convolutional network and boundary refinement in the coding part. To better divide the blood vessel and background, the improved position attention module and channel attention module are introduced in the jumping connection part. Multiscale input and multiscale dense feature pyramid cascade modules are used to better obtain feature information. In the decoding part, convolutional long and short memory networks and deep dilated convolution are used to extract features. In public datasets, DRIVE and CHASE_DB1, the accuracy reached 96.99% and 97.51%. The average performance of the proposed algorithm is better than that of existing algorithms.

Liu Congjun, Gu Penghui, Xiao Zhiyong

2022

Radiology Radiology

Brain Tumor Detection and Classification by MRI Using Biologically Inspired Orthogonal Wavelet Transform and Deep Learning Techniques.

In Journal of healthcare engineering

Radiology is a broad subject that needs more knowledge and understanding of medical science to identify tumors accurately. The need for a tumor detection program, thus, overcomes the lack of qualified radiologists. Using magnetic resonance imaging, biomedical image processing makes it easier to detect and locate brain tumors. In this study, a segmentation and detection method for brain tumors was developed using images from the MRI sequence as an input image to identify the tumor area. This process is difficult due to the wide variety of tumor tissues in the presence of different patients, and, in most cases, the similarity within normal tissues makes the task difficult. The main goal is to classify the brain in the presence of a brain tumor or a healthy brain. The proposed system has been researched based on Berkeley's wavelet transformation (BWT) and deep learning classifier to improve performance and simplify the process of medical image segmentation. Significant features are extracted from each segmented tissue using the gray-level-co-occurrence matrix (GLCM) method, followed by a feature optimization using a genetic algorithm. The innovative final result of the approach implemented was assessed based on accuracy, sensitivity, specificity, coefficient of dice, Jaccard's coefficient, spatial overlap, AVME, and FoM.

Arif Muhammad, Ajesh F, Shamsudheen Shermin, Geman Oana, Izdrui Diana, Vicoveanu Dragos

2022

General General

Integrating Incompatible Assay Data Sets with Deep Preference Learning.

In ACS medicinal chemistry letters ; h5-index 37.0

A large amount of bioactivity assay data is already accumulated in public databases, but the integration of these data sets for quantitative structure-activity relationship (QSAR) studies is not straightforward due to differences in experimental methods and settings. We present an efficient deep-learning-based approach called Deep Preference Data Integration (DPDI). For integrating outcome variables of different assay types, a surrogate variable is introduced, and a neural network is trained such that the total order induced by the surrogate variable is maximally consistent with given data sets. In a task of predicting efficacy of factor Xa inhibitors, DPDI successfully integrated 2959 molecules distributed in 129 assay data sets. In most of our experiments, data integration improved prediction accuracy strongly in interpolation and extrapolation tasks, indicating that DPDI is an effective tool for QSAR studies.

Sun Xiaolin, Tamura Ryo, Sumita Masato, Mori Kenichi, Terayama Kei, Tsuda Koji

2022-Jan-13

General General

Neural Matrix Factorization Recommendation for User Preference Prediction Based on Explicit and Implicit Feedback.

In Computational intelligence and neuroscience

Explicit feedback and implicit feedback are two important types of heterogeneous data for constructing a recommendation system. The combination of the two can effectively improve the performance of the recommendation system. However, most of the current deep learning recommendation models fail to fully exploit the complementary advantages of two types of data combined and usually only use binary implicit feedback data. Thus, this paper proposes a neural matrix factorization recommendation algorithm (EINMF) based on explicit-implicit feedback. First, neural network is used to learn nonlinear feature of explicit-implicit feedback of user-item interaction. Second, combined with the traditional matrix factorization, explicit feedback is used to accurately reflect the explicit preference and the potential preferences of users to build a recommendation model; a new loss function is designed based on explicit-implicit feedback to obtain the best parameters through the neural network training to predict the preference of users for items; finally, according to prediction results, personalized recommendation list is pushed to the user. The feasibility, validity, and robustness are fully demonstrated in comparison with multiple baseline models on two real datasets.

Liu Huazhen, Wang Wei, Zhang Yihan, Gu Renqian, Hao Yaqi

2022

General General

Telemetry Data Compression Algorithm Using Balanced Recurrent Neural Network and Deep Learning.

In Computational intelligence and neuroscience

Telemetric information is great in size, requiring extra room and transmission time. There is a significant obstruction of storing or sending telemetric information. Lossless data compression (LDC) algorithms have evolved to process telemetric data effectively and efficiently with a high compression ratio and a short processing time. Telemetric information can be packed to control the extra room and association data transmission. In spite of the fact that different examinations on the pressure of telemetric information have been conducted, the idea of telemetric information makes pressure incredibly troublesome. The purpose of this study is to offer a subsampled and balanced recurrent neural lossless data compression (SB-RNLDC) approach for increasing the compression rate while decreasing the compression time. This is accomplished through the development of two models: one for subsampled averaged telemetry data preprocessing and another for BRN-LDC. Subsampling and averaging are conducted at the preprocessing stage using an adjustable sampling factor. A balanced compression interval (BCI) is used to encode the data depending on the probability measurement during the LDC stage. The aim of this research work is to compare differential compression techniques directly. The final output demonstrates that the balancing-based LDC can reduce compression time and finally improve dependability. The final experimental results show that the model proposed can enhance the computing capabilities in data compression compared to the existing methodologies.

Ramalingam Parameshwaran, Mehbodniya Abolfazl, Webber Julian L, Shabaz Mohammad, Gopalakrishnan Lakshminarayanan

2022

General General

Risk Analysis of Textile Industry Foreign Investment Based on Deep Learning.

In Computational intelligence and neuroscience

With the decline of China's economic growth rate and the uproar of antiglobalization, the textile industry, one of the business cards of China's globalization, is facing a huge impact. When the economic model is undergoing transformation, it is more important to prevent enterprises from falling into financial distress. So, the financial risk early warning is one of the important means to prevent enterprises from falling into financial distress. Aiming at the risk analysis of the textile industry's foreign investment, this paper proposes an analysis method based on deep learning. This method combines residual network (ResNet) and long short-term memory (LSTM) risk prediction model. This method first establishes a risk indicator system for the textile industry and then uses ResNet to complete deep feature extraction, which are further used for LSTM training and testing. The performance of the proposed method is tested based on part of the measured data, and the results show the effectiveness of the proposed method.

Liu Jingyi, Li Jiaolong

2022

General General

Analysis of the Major Investment Object by Using a Novel Approach Based on Neutrosophic Information.

In Computational intelligence and neuroscience

Neutrosophic set (NS) is an extensively used framework whenever the imprecision and uncertainty of an event is described based on three possible aspects. The association, neutral, and nonassociation degrees are the three unique aspects of an NS. More importantly, these degrees are independent which is a great plus point. On the contrary, neutrosophic graphs (NGs) and single-valued NGs (SVNGs) are applicable to deal with events that contain bulks of information. However, the concept of degrees in NGs is a handful tool for solving the problems of decision-making (DM), pattern recognition, social network, and communication network. This manuscript develops various forms of edge irregular SVNG (EISVNG), highly edge irregular SVNG (HEISVNG), strongly (EISVNG), strongly (ETISVNG), and edge irregularity on a cycle and a path in SVNGs. All these novel notions are supported by definitions, theorems, mathematical proofs, and illustrative examples. Moreover, two types of DM problems are modelled using the proposed framework. Furthermore, the computational processes are used to confirm the validity of the proposed graphs. Furthermore, the results approve that the decision-making problems can be addressed by the edge irregular neutrosophic graphical structures. In addition, the comparison between proposed and the existing methodologies is carried out.

Farooq Muhammad Umar, Anjum Rukhshanda, Gaffar Abdul, Bashir Huma, Al-Aidroos Naziha, Alsanad Ahmed

2022

General General

Using a Selective Ensemble Support Vector Machine to Fuse Multimodal Features for Human Action Recognition.

In Computational intelligence and neuroscience

The traditional human action recognition (HAR) method is based on RGB video. Recently, with the introduction of Microsoft Kinect and other consumer class depth cameras, HAR based on RGB-D (RGB-Depth) has drawn increasing attention from scholars and industry. Compared with the traditional method, the HAR based on RGB-D has high accuracy and strong robustness. In this paper, using a selective ensemble support vector machine to fuse multimodal features for human action recognition is proposed. The algorithm combines the improved HOG feature-based RGB modal data, the depth motion map-based local binary pattern features (DMM-LBP), and the hybrid joint features (HJF)-based joints modal data. Concomitantly, a frame-based selective ensemble support vector machine classification model (SESVM) is proposed, which effectively integrates the selective ensemble strategy with the selection of SVM base classifiers, thus increasing the differences between the base classifiers. The experimental results have demonstrated that the proposed method is simple, fast, and efficient on public datasets in comparison with other action recognition algorithms.

Tang Chao, Tong Anyang, Zheng Aihua, Peng Hua, Li Wei

2022

Internal Medicine Internal Medicine

Vessel segmentation for X-ray coronary angiography using ensemble methods with deep learning and filter-based features.

In BMC medical imaging

BACKGROUND : Automated segmentation of coronary arteries is a crucial step for computer-aided coronary artery disease (CAD) diagnosis and treatment planning. Correct delineation of the coronary artery is challenging in X-ray coronary angiography (XCA) due to the low signal-to-noise ratio and confounding background structures.

METHODS : A novel ensemble framework for coronary artery segmentation in XCA images is proposed, which utilizes deep learning and filter-based features to construct models using the gradient boosting decision tree (GBDT) and deep forest classifiers. The proposed method was trained and tested on 130 XCA images. For each pixel of interest in the XCA images, a 37-dimensional feature vector was constructed based on (1) the statistics of multi-scale filtering responses in the morphological, spatial, and frequency domains; and (2) the feature maps obtained from trained deep neural networks. The performance of these models was compared with those of common deep neural networks on metrics including precision, sensitivity, specificity, F1 score, AUROC (the area under the receiver operating characteristic curve), and IoU (intersection over union).

RESULTS : With hybrid under-sampling methods, the best performing GBDT model achieved a mean F1 score of 0.874, AUROC of 0.947, sensitivity of 0.902, and specificity of 0.992; while the best performing deep forest model obtained a mean F1 score of 0.867, AUROC of 0.95, sensitivity of 0.867, and specificity of 0.993. Compared with the evaluated deep neural networks, both models had better or comparable performance for all evaluated metrics with lower standard deviations over the test images.

CONCLUSIONS : The proposed feature-based ensemble method outperformed common deep convolutional neural networks in most performance metrics while yielding more consistent results. Such a method can be used to facilitate the assessment of stenosis and improve the quality of care in patients with CAD.

Gao Zijun, Wang Lu, Soroushmehr Reza, Wood Alexander, Gryak Jonathan, Nallamothu Brahmajee, Najarian Kayvan

2022-Jan-19

Deep learning, Ensemble learning, Medical image segmentation, X-ray coronary angiography

General General

Modeling the COVID-19 Epidemic With Multi-Population and Control Strategies in the United States.

In Frontiers in public health

As of January 19, 2021, the cumulative number of people infected with coronavirus disease-2019 (COVID-19) in the United States has reached 24,433,486, and the number is still rising. The outbreak of the COVID-19 epidemic has not only affected the development of the global economy but also seriously threatened the lives and health of human beings around the world. According to the transmission characteristics of COVID-19 in the population, this study established a theoretical differential equation mathematical model, estimated model parameters through epidemiological data, obtained accurate mathematical models, and adopted global sensitivity analysis methods to screen sensitive parameters that significantly affect the development of the epidemic. Based on the established precise mathematical model, we calculate the basic reproductive number of the epidemic, evaluate the transmission capacity of the COVID-19 epidemic, and predict the development trend of the epidemic. By analyzing the sensitivity of parameters and finding sensitive parameters, we can provide effective control strategies for epidemic prevention and control. After appropriate modifications, the model can also be used for mathematical modeling of epidemics in other countries or other infectious diseases.

Sun Deshun, Long Xiaojun, Liu Jingxiang

2021

COVID-19, control strategies, mathematical model, parameter estimate, sensitive analysis

General General

Smart Heart Disease Prediction System with IoT and Fog Computing Sectors Enabled by Cascaded Deep Learning Model.

In Computational intelligence and neuroscience

Chronic illnesses like chronic respiratory disease, cancer, heart disease, and diabetes are threats to humans around the world. Among them, heart disease with disparate features or symptoms complicates diagnosis. Because of the emergence of smart wearable gadgets, fog computing and "Internet of Things" (IoT) solutions have become necessary for diagnosis. The proposed model integrates Edge-Fog-Cloud computing for the accurate and fast delivery of outcomes. The hardware components collect data from different patients. The heart feature extraction from signals is done to get significant features. Furthermore, the feature extraction of other attributes is also gathered. All these features are gathered and subjected to the diagnostic system using an Optimized Cascaded Convolution Neural Network (CCNN). Here, the hyperparameters of CCNN are optimized by the Galactic Swarm Optimization (GSO). Through the performance analysis, the precision of the suggested GSO-CCNN is 3.7%, 3.7%, 3.6%, 7.6%, 67.9%, 48.4%, 33%, 10.9%, and 7.6% more advanced than PSO-CCNN, GWO-CCNN, WOA-CCNN, DHOA-CCNN, DNN, RNN, LSTM, CNN, and CCNN, respectively. Thus, the comparative analysis of the suggested system ensures its efficiency over the conventional models.

Raju K Butchi, Dara Suresh, Vidyarthi Ankit, Gupta V Mnssvkr, Khan Baseem

2022

Radiology Radiology

VTP-Identifier: Vesicular Transport Proteins Identification Based on PSSM Profiles and XGBoost.

In Frontiers in genetics ; h5-index 62.0

Vesicular transport proteins are related to many human diseases, and they threaten human health when they undergo pathological changes. Protein function prediction has been one of the most in-depth topics in bioinformatics. In this work, we developed a useful tool to identify vesicular transport proteins. Our strategy is to extract transition probability composition, autocovariance transformation and other information from the position-specific scoring matrix as feature vectors. EditedNearesNeighbours (ENN) is used to address the imbalance of the data set, and the Max-Relevance-Max-Distance (MRMD) algorithm is adopted to reduce the dimension of the feature vector. We used 5-fold cross-validation and independent test sets to evaluate our model. On the test set, VTP-Identifier presented a higher performance compared with GRU. The accuracy, Matthew's correlation coefficient (MCC) and area under the ROC curve (AUC) were 83.6%, 0.531 and 0.873, respectively.

Gong Yue, Dong Benzhi, Zhang Zixiao, Zhai Yixiao, Gao Bo, Zhang Tianjiao, Zhang Jingyu

2021

XGBoost, machine learning, position-specific scoring matrix, protein function prediction, vesicular transport proteins

General General

Machine Learning Identifies Six Genetic Variants and Alterations in the Heart Atrial Appendage as Key Contributors to PD Risk Predictivity.

In Frontiers in genetics ; h5-index 62.0

Parkinson's disease (PD) is a complex neurodegenerative disease with a range of causes and clinical presentations. Over 76 genetic loci (comprising 90 SNPs) have been associated with PD by the most recent GWAS meta-analysis. Most of these PD-associated variants are located in non-coding regions of the genome and it is difficult to understand what they are doing and how they contribute to the aetiology of PD. We hypothesised that PD-associated genetic variants modulate disease risk through tissue-specific expression quantitative trait loci (eQTL) effects. We developed and validated a machine learning approach that integrated tissue-specific eQTL data on known PD-associated genetic variants with PD case and control genotypes from the Wellcome Trust Case Control Consortium. In so doing, our analysis ranked the tissue-specific transcription effects for PD-associated genetic variants and estimated their relative contributions to PD risk. We identified roles for SNPs that are connected with INPP5P, CNTN1, GBA and SNCA in PD. Ranking the variants and tissue-specific eQTL effects contributing most to the machine learning model suggested a key role in the risk of developing PD for two variants (rs7617877 and rs6808178) and eQTL associated transcriptional changes of EAF1-AS1 within the heart atrial appendage. Similarly, effects associated with eQTLs located within the Brain Cerebellum were also recognized to confer major PD risk. These findings were replicated in two additional, independent cohorts (the UK Biobank, and NeuroX) and thus warrant further mechanistic investigations to determine if these transcriptional changes could act as early contributors to PD risk and disease development.

Ho Daniel, Schierding William, Farrow Sophie L, Cooper Antony A, Kempa-Liehr Andreas W, O’Sullivan Justin M

2021

Brain Cerebellum, GBA, PD-SNPs, Parkinson’s disease, SNCA, heart atrial appendage, machine leaning, tissue specific eQTL

General General

Using Machine Learning to Predict Obesity Based on Genome-Wide and Epigenome-Wide Gene-Gene and Gene-Diet Interactions.

In Frontiers in genetics ; h5-index 62.0

Obesity is associated with many chronic diseases that impair healthy aging and is governed by genetic, epigenetic, and environmental factors and their complex interactions. This study aimed to develop a model that predicts an individual's risk of obesity by better characterizing these complex relations and interactions focusing on dietary factors. For this purpose, we conducted a combined genome-wide and epigenome-wide scan for body mass index (BMI) and up to three-way interactions among 402,793 single nucleotide polymorphisms (SNPs), 415,202 DNA methylation sites (DMSs), and 397 dietary and lifestyle factors using the generalized multifactor dimensionality reduction (GMDR) method. The training set consisted of 1,573 participants in exam 8 of the Framingham Offspring Study (FOS) cohort. After identifying genetic, epigenetic, and dietary factors that passed statistical significance, we applied machine learning (ML) algorithms to predict participants' obesity status in the test set, taken as a subset of independent samples (n = 394) from the same cohort. The quality and accuracy of prediction models were evaluated using the area under the receiver operating characteristic curve (ROC-AUC). GMDR identified 213 SNPs, 530 DMSs, and 49 dietary and lifestyle factors as significant predictors of obesity. Comparing several ML algorithms, we found that the stochastic gradient boosting model provided the best prediction accuracy for obesity with an overall accuracy of 70%, with ROC-AUC of 0.72 in test set samples. Top predictors of the best-fit model were 21 SNPs, 230 DMSs in genes such as CPT1A, ABCG1, SLC7A11, RNF145, and SREBF1, and 26 dietary factors, including processed meat, diet soda, French fries, high-fat dairy, artificial sweeteners, alcohol intake, and specific nutrients and food components, such as calcium and flavonols. In conclusion, we developed an integrated approach with ML to predict obesity using omics and dietary data. This extends our knowledge of the drivers of obesity, which can inform precision nutrition strategies for the prevention and treatment of obesity. Clinical Trial Registration: [www.ClinicalTrials.gov], the Framingham Heart Study (FHS), [NCT00005121].

Lee Yu-Chi, Christensen Jacob J, Parnell Laurence D, Smith Caren E, Shao Jonathan, McKeown Nicola M, Ordovás José M, Lai Chao-Qiang

2021

DNA methylation, GxE interaction, diet, genomics, machine learning, obesity, precision nutrition

General General

A Novel Computational Framework for Precision Diagnosis and Subtype Discovery of Plant With Lesion.

In Frontiers in plant science

Plants are often attacked by various pathogens during their growth, which may cause environmental pollution, food shortages, or economic losses in a certain area. Integration of high throughput phenomics data and computer vision (CV) provides a great opportunity to realize plant disease diagnosis in the early stage and uncover the subtype or stage patterns in the disease progression. In this study, we proposed a novel computational framework for plant disease identification and subtype discovery through a deep-embedding image-clustering strategy, Weighted Distance Metric and the t-stochastic neighbor embedding algorithm (WDM-tSNE). To verify the effectiveness, we applied our method on four public datasets of images. The results demonstrated that the newly developed tool is capable of identifying the plant disease and further uncover the underlying subtypes associated with pathogenic resistance. In summary, the current framework provides great clustering performance for the root or leave images of diseased plants with pronounced disease spots or symptoms.

Xia Fei, Xie Xiaojun, Wang Zongqin, Jin Shichao, Yan Ke, Ji Zhiwei

2021

deep learning, disease diagnosis, image clustering, plant, subtype discovery, t-SNE

General General

Application of Machine Learning for Cytometry Data.

In Frontiers in immunology ; h5-index 100.0

Modern cytometry technologies present opportunities to profile the immune system at a single-cell resolution with more than 50 protein markers, and have been widely used in both research and clinical settings. The number of publicly available cytometry datasets is growing. However, the analysis of cytometry data remains a bottleneck due to its high dimensionality, large cell numbers, and heterogeneity between datasets. Machine learning techniques are well suited to analyze complex cytometry data and have been used in multiple facets of cytometry data analysis, including dimensionality reduction, cell population identification, and sample classification. Here, we review the existing machine learning applications for analyzing cytometry data and highlight the importance of publicly available cytometry data that enable researchers to develop and validate machine learning methods.

Hu Zicheng, Bhattacharya Sanchita, Butte Atul J

2021

cyTOF, cytometry, flow cytometry, machine learning, predictive modeling

Radiology Radiology

Radiomics Assessment of the Tumor Immune Microenvironment to Predict Outcomes in Breast Cancer.

In Frontiers in immunology ; h5-index 100.0

Background : The immune microenvironment of tumors provides information on prognosis and prediction. A prior validation of the immunoscore for breast cancer (ISBC) was made on the basis of a systematic assessment of immune landscapes extrapolated from a large number of neoplastic transcripts. Our goal was to develop a non-invasive radiomics-based ISBC predictive factor.

Methods : Immunocell fractions of 22 different categories were evaluated using CIBERSORT on the basis of a large, open breast cancer cohort derived from comprehensive information on gene expression. The ISBC was constructed using the LASSO Cox regression model derived from the Immunocell type scores, with 479 quantified features in the intratumoral and peritumoral regions as observed from DCE-MRI. A radiomics signature [radiomics ImmunoScore (RIS)] was developed for the prediction of ISBC using a random forest machine-learning algorithm, and we further evaluated its relationship with prognosis.

Results : An ISBC consisting of seven different immune cells was established through the use of a LASSO model. Multivariate analyses showed that the ISBC was an independent risk factor in prognosis (HR=2.42, with a 95% CI of 1.49-3.93; P<0.01). A radiomic signature of 21 features of the ISBC was then exploited and validated (the areas under the curve [AUC] were 0.899 and 0.815). We uncovered statistical associations between the RIS signature with recurrence-free and overall survival rates (both P<0.05).

Conclusions : The RIS is a valuable instrument with which to assess the immunoscore, and offers important implications for the prognosis of breast cancer.

Han Xiaorui, Cao Wuteng, Wu Lei, Liang Changhong

2021

DCE-MRI, breast cancer, immune microenvironment, immunoscore, radiomics

General General

Enhancing Robustness of Machine Learning Integration With Routine Laboratory Blood Tests to Predict Inpatient Mortality After Intracerebral Hemorrhage.

In Frontiers in neurology

Objective: The accurate evaluation of outcomes at a personalized level in patients with intracerebral hemorrhage (ICH) is critical clinical implications. This study aims to evaluate how machine learning integrates with routine laboratory tests and electronic health records (EHRs) data to predict inpatient mortality after ICH. Methods: In this machine learning-based prognostic study, we included 1,835 consecutive patients with acute ICH between October 2010 and December 2018. The model building process incorporated five pre-implant ICH score variables (clinical features) and 13 out of 59 available routine laboratory parameters. We assessed model performance according to a range of learning metrics, such as the mean area under the receiver operating characteristic curve [AUROC]. We also used the Shapley additive explanation algorithm to explain the prediction model. Results: Machine learning models using laboratory data achieved AUROCs of 0.71-0.82 in a split-by-year development/testing scheme. The non-linear eXtreme Gradient Boosting model yielded the highest prediction accuracy. In the held-out validation set of development cohort, the predictive model using comprehensive clinical and laboratory parameters outperformed those using clinical alone in predicting in-hospital mortality (AUROC [95% bootstrap confidence interval], 0.899 [0.897-0.901] vs. 0.875 [0.872-0.877]; P <0.001), with over 81% accuracy, sensitivity, and specificity. We observed similar performance in the testing set. Conclusions: Machine learning integrated with routine laboratory tests and EHRs could significantly promote the accuracy of inpatient ICH mortality prediction. This multidimensional composite prediction strategy might become an intelligent assistive prediction for ICH risk reclassification and offer an example for precision medicine.

Chen Wei, Li Xiangkui, Ma Lu, Li Dong

2021

intracerebral hemorrhage, laboratory profiles, machine learning, prediction, prognostication

General General

Predicting 1-Hour Thrombolysis Effect of r-tPA in Patients With Acute Ischemic Stroke Using Machine Learning Algorithm.

In Frontiers in pharmacology

Background: Thrombolysis with r-tPA is recommended for patients after acute ischemic stroke (AIS) within 4.5 h of symptom onset. However, only a few patients benefit from this therapeutic regimen. Thus, we aimed to develop an interpretable machine learning (ML)-based model to predict the thrombolysis effect of r-tPA at the super-early stage. Methods: A total of 353 patients with AIS were divided into training and test data sets. We then used six ML algorithms and a recursive feature elimination (RFE) method to explore the relationship among the clinical variables along with the NIH stroke scale score 1 h after thrombolysis treatment. Shapley additive explanations and local interpretable model-agnostic explanation algorithms were applied to interpret the ML models and determine the importance of the selected features. Results: Altogether, 353 patients with an average age of 63.0 (56.0-71.0) years were enrolled in the study. Of these patients, 156 showed a favorable thrombolysis effect and 197 showed an unfavorable effect. A total of 14 variables were enrolled in the modeling, and 6 ML algorithms were used to predict the thrombolysis effect. After RFE screening, seven variables under the gradient boosting decision tree (GBDT) model (area under the curve = 0.81, specificity = 0.61, sensitivity = 0.9, and F1 score = 0.79) demonstrated the best performance. Of the seven variables, activated partial thromboplastin clotting time (time), B-type natriuretic peptide, and fibrin degradation products were the three most important clinical characteristics that might influence r-tPA efficiency. Conclusion: This study demonstrated that the GBDT model with the seven variables could better predict the early thrombolysis effect of r-tPA.

Zhu Bin, Zhao Jianlei, Cao Mingnan, Du Wanliang, Yang Liuqing, Su Mingliang, Tian Yue, Wu Mingfen, Wu Tingxi, Wang Manxia, Zhao Xingquan, Zhao Zhigang

2021

acute ischemic stroke, machine learning algorithms, models, r-tPA, thrombolysis

General General

Deep Learning Algorithm Trained on Brain Magnetic Resonance Images and Clinical Data to Predict Motor Outcomes of Patients With Corona Radiata Infarct.

In Frontiers in neuroscience ; h5-index 72.0

The early and accurate prediction of the extent of long-term motor recovery is important for establishing specific rehabilitation strategies for stroke patients. Using clinical parameters and brain magnetic resonance images as inputs, we developed a deep learning algorithm to increase the prediction accuracy of long-term motor outcomes in patients with corona radiata (CR) infarct. Using brain magnetic resonance images and clinical data obtained soon after CR infarct, we developed an integrated algorithm to predict hand function and ambulatory outcomes of the patient 6 months after onset. To develop and evaluate the algorithm, we retrospectively recruited 221 patients with CR infarct. The area under the curve of the validation set of the integrated modified Brunnstrom classification prediction model was 0.891 with 95% confidence interval (0.814-0.967) and that of the integrated functional ambulatory category prediction model was 0.919, with 95% confidence interval (0.842-0.995). We demonstrated that an integrated algorithm trained using patients' clinical data and brain magnetic resonance images obtained soon after CR infarct can promote the accurate prediction of long-term hand function and ambulatory outcomes. Future efforts will be devoted to finding more appropriate input variables to further increase the accuracy of deep learning models in clinical applications.

Kim Jeoung Kun, Chang Min Cheol, Park Donghwi

2021

artificial intelligence, cerebral infarction, corona radiate, deep learning, motor outcome

General General

A support vector machine algorithm can successfully classify running ability when trained with wearable sensor data from anatomical locations typical of consumer technology.

In Sports biomechanics

Greater understanding of differences in technique between runners may allow more beneficial feedback related to improving performance and decreasing injury risk. The purpose of this study was to develop and test a support vector machine classifier, which could automatically differentiate running technique between experienced and novice participants using only wearable sensor data. Three-dimensional linear accelerations and angular velocities were collected from six wearable sensors secured to current common smart device locations. Cross-validation was used to test the classification accuracy of models trained with a variety of combinations of sensor locations, with participants running at different speeds. Average classification accuracies ranged from 71.3% to 98.4% across the sensor combinations and running speeds tested. Models trained with only a single sensor location still showed effective classification. With the models trained with only upper arm data achieving an average accuracy of 96.4% across all tested running speeds. A post-hoc comparison of biomechanical variables between the two subgroups showed significant differences in upper body biomechanics throughout the stride. Both the methodology used to perform the classifications and the biomechanical differences identified could prove useful when aiming to shift a novice runner's technique towards movement patterns more akin to those with greater experience.

Carter Joshua Autton, Rivadulla Adrian Rodriguez, Preatoni Ezio

2022-Jan-20

Running biomechanics, gait analysis, inertial measurement unit, machine learning

General General

Learning From Biological and Computational Machines: Importance of SARS-CoV-2 Genomic Surveillance, Mutations and Risk Stratification.

In Frontiers in cellular and infection microbiology ; h5-index 53.0

The global coronavirus disease 2019 (COVID-19) pandemic has demonstrated the range of disease severity and pathogen genomic diversity emanating from a singular virus (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2). This diversity in disease manifestations and genomic mutations has challenged healthcare management and resource allocation during the pandemic, especially for countries such as India with a bigger population base. Here, we undertake a combinatorial approach toward scrutinizing the diagnostic and genomic diversity to extract meaningful information from the chaos of COVID-19 in the Indian context. Using methods of statistical correlation, machine learning (ML), and genomic sequencing on a clinically comprehensive patient dataset with corresponding with/without respiratory support samples, we highlight specific significant diagnostic parameters and ML models for assessing the risk of developing severe COVID-19. This information is further contextualized in the backdrop of SARS-CoV-2 genomic features in the cohort for pathogen genomic evolution monitoring. Analysis of the patient demographic features and symptoms revealed that age, breathlessness, and cough were significantly associated with severe disease; at the same time, we found no severe patient reporting absence of physical symptoms. Observing the trends in biochemical/biophysical diagnostic parameters, we noted that the respiratory rate, total leukocyte count (TLC), blood urea levels, and C-reactive protein (CRP) levels were directly correlated with the probability of developing severe disease. Out of five different ML algorithms tested to predict patient severity, the multi-layer perceptron-based model performed the best, with a receiver operating characteristic (ROC) score of 0.96 and an F1 score of 0.791. The SARS-CoV-2 genomic analysis highlighted a set of mutations with global frequency flips and future inculcation into variants of concern (VOCs) and variants of interest (VOIs), which can be further monitored and annotated for functional significance. In summary, our findings highlight the importance of SARS-CoV-2 genomic surveillance and statistical analysis of clinical data to develop a risk assessment ML model.

Bhat Shikha, Pandey Anuradha, Kanakan Akshay, Maurya Ranjeet, Vasudevan Janani Srinivasa, Devi Priti, Chattopadhyay Partha, Sharma Shimpa, Khyalappa Rajesh J, Joshi Meghnad G, Pandey Rajesh

2021

COVID-19, SARS-CoV-2, genomic surveillance, healthcare, machine learning, risk stratification

General General

Toward a Multimodal Computer-Aided Diagnostic Tool for Alzheimer's Disease Conversion.

In Frontiers in neuroscience ; h5-index 72.0

Alzheimer's disease (AD) is a progressive neurodegenerative disorder. It is one of the leading sources of morbidity and mortality in the aging population AD cardinal symptoms include memory and executive function impairment that profoundly alters a patient's ability to perform activities of daily living. People with mild cognitive impairment (MCI) exhibit many of the early clinical symptoms of patients with AD and have a high chance of converting to AD in their lifetime. Diagnostic criteria rely on clinical assessment and brain magnetic resonance imaging (MRI). Many groups are working to help automate this process to improve the clinical workflow. Current computational approaches are focused on predicting whether or not a subject with MCI will convert to AD in the future. To our knowledge, limited attention has been given to the development of automated computer-assisted diagnosis (CAD) systems able to provide an AD conversion diagnosis in MCI patient cohorts followed longitudinally. This is important as these CAD systems could be used by primary care providers to monitor patients with MCI. The method outlined in this paper addresses this gap and presents a computationally efficient pre-processing and prediction pipeline, and is designed for recognizing patterns associated with AD conversion. We propose a new approach that leverages longitudinal data that can be easily acquired in a clinical setting (e.g., T1-weighted magnetic resonance images, cognitive tests, and demographic information) to identify the AD conversion point in MCI subjects with AUC = 84.7. In contrast, cognitive tests and demographics alone achieved AUC = 80.6, a statistically significant difference (n = 669, p < 0.05). We designed a convolutional neural network that is computationally efficient and requires only linear registration between imaging time points. The model architecture combines Attention and Inception architectures while utilizing both cross-sectional and longitudinal imaging and clinical information. Additionally, the top brain regions and clinical features that drove the model's decision were investigated. These included the thalamus, caudate, planum temporale, and the Rey Auditory Verbal Learning Test. We believe our method could be easily translated into the healthcare setting as an objective AD diagnostic tool for patients with MCI.

Pena Danilo, Suescun Jessika, Schiess Mya, Ellmore Timothy M, Giancardo Luca

2021

ADNI, clinical features, deep learning, longitudinal, mild cognitive impairment, multimodal, neuroimaging

Cardiology Cardiology

Deep Learning in Prediction of Late Major Bleeding After Transcatheter Aortic Valve Replacement.

In Clinical epidemiology ; h5-index 34.0

Purpose : Late major bleeding is one of the main complications after transcatheter aortic valve replacement (TAVR). We aimed to develop a risk prediction model based on deep learning to predict major or life-threatening bleeding complications (MLBCs) after TAVR.

Patients and Methods : This was a retrospective study including TAVR patients from West China Hospital of Sichuan University Transcatheter Aortic Valve Replacement Registry (ChiCTR2000033419) between April 17, 2012 and May 27, 2020. A deep learning-based model named BLeNet was developed with 56 features covering baseline, procedural, and post-procedural characteristics. The model was validated with the bootstrap method and evaluated using Harrell's concordance index (c-index), receiver operating characteristics (ROC) curve, calibration curve, and Kaplan-Meier estimate. Captum interpretation library was applied to identify feature importance. The BLeNet model was compared with the traditional Cox proportional hazard (Cox-PH) model and the random survival forest model in the metrics mentioned above.

Results : The BLeNet model outperformed the Cox-PH and random survival forest models significantly in discrimination [optimism-corrected c-index of BLeNet vs Cox-PH vs random survival forest: 0.81 (95% CI: 0.79-0.92) vs 0.72 (95% CI: 0.63-0.77) vs 0.70 (95% CI: 0.61-0.74)] and calibration (integrated calibration index of BLeNet vs Cox-PH vs random survival forest: 0.007 vs 0.015 vs 0.019). In Kaplan-Meier analysis, BLeNet model had great performance in stratifying high- and low-bleeding risk patients (p < 0.0001).

Conclusion : Deep learning is a feasible way to build prediction models concerning TAVR prognosis. A dedicated bleeding risk prediction model was developed for TAVR patients to facilitate well-informed clinical decisions.

Jia Yuheng, Luosang Gaden, Li Yiming, Wang Jianyong, Li Pengyu, Xiong Tianyuan, Li Yijian, Liao Yanbiao, Zhao Zhengang, Peng Yong, Feng Yuan, Jiang Weili, Li Wenjian, Zhang Xinpei, Yi Zhang, Chen Mao

2022

deep learning, major or life-threatening bleeding complications, prediction model, transcatheter aortic valve replacement

oncology Oncology

Central Nervous System Metastases from Triple-Negative Breast Cancer: Current Treatments and Future Prospective.

In Breast cancer (Dove Medical Press)

It is estimated that approximately one-third of patients with triple-negative breast cancer (TNBC) will develop brain metastases. The prognosis for patients with breast cancer brain metastasis has improved in the recent past, especially for hormone receptor and human epidermal growth factor receptor 2 (HER) positive subtypes. However, the overall survival rate for patients with triple-negative subtype remains poor. The development of newer treatment options, including antibody-drug conjugates such as Sacituzumab govitecan, is particularly encouraging. This article reviews the clinical outcomes, challenges, and current approach to the treatment of brain metastasis in TNBC. We have also briefly discussed newer treatment options and ongoing clinical trials. The development of brain metastasis significantly decreases the quality of life of patients with TNBC, and newer treatment strategies and therapeutics are the need of the hour for this disease subgroup.

Kadamkulam Syriac Arun, Nandu Nitish Singh, Leone Jose Pablo

2022

antibody–drug conjugate, brain metastasis, clinical outcomes, immunotherapy, individualized treatment algorithms, machine learning, metastatic disease, multi-omics, oligometastatic brain metastasis, patient benefits, personalization, prediction, prognosis, prognosis clinical outcomes, stereotactic radiosurgery, triple-negative breast cancer, whole-brain radiation therapy

Public Health Public Health

GWAS-associated bacteria and their metabolites appear to be causally related to the development of inflammatory bowel disease.

In European journal of clinical nutrition ; h5-index 46.0

BACKGROUND : Accumulating evidence has suggested that the imbalance of gut microbiota is commonly observed in patients with inflammatory bowel disease (IBD). However, it remains unclear whether dysbiosis is a cause or consequence of chronic intestinal inflammation. We aimed to investigate the causal relationships of gut microbiota and metabolites with IBD, including ulcerative colitis (UC) and Crohn's disease (CD).

METHODS : We applied two-sample Mendelian randomization using summary statistics from the gut microbiota genetic consortium (n = 1812), the Framingham Heart Study (n = 2076) and the International IBD Genetics Consortium (n = 86,640).

RESULTS : Using the genetic approach, the increase in OTU10032 unclassified Enterobacteriaceae was associated with higher risks of IBD (OR, 1.03; 95% CI, 1.00-1.06; P = 0.033) and CD (1.04; 1.01-1.08; P = 0.015). Importantly, an Enterobacteriaceae-related metabolite taurine was positively associated with risks of IBD (1.04; 1.01-1.08; P = 0.016) and UC (1.05; 1.01-1.10; P = 0.024). Notably, we also found betaine, a downstream product of Enterobacteriaceae metabolism, was causally associated with a higher risk of CD (1.10; 1.02-1.18; P = 0.008). In addition, increased Erysipelotrichaceae family were causally related to lower risks of IBD (0.88; 0.78-0.98; P = 0.026) and UC (0.86; 0.75-0.99; P = 0.042), and Actinobacteria class (0.80; 0.65-0.98; P = 0.028) and Unclassified Erysipelotrichaceae (0.79; 0.64-0.98; P = 0.036) were associated with lower risks of UC and CD, respectively.

CONCLUSIONS : Our finding provided new insights into the key role of gut metabolites such as taurine and betaine in host-microbiota interactions of IBD pathogenesis, indicating that host-microbe balance strongly influences inflammatory conditions.

Zhuang Zhenhuang, Li Nan, Wang Jiayi, Yang Ruotong, Wang Wenxiu, Liu Zhonghua, Huang Tao

2022-Jan-19

Ophthalmology Ophthalmology

Thickness of retinal pigment epithelium-Bruch's membrane complex in adult Chinese using optical coherence tomography.

In Eye (London, England) ; h5-index 41.0

PURPOSE : To study thickness of RPE-BM complex in adult Chinese subjects and its correlation with systemic and ocular biometric parameters.

DESIGN : Population-based longitudinal study. Cross-sectional study.

PARTICIPANTS : The population-based Beijing Eye Study 2011 included 3468 individuals with a mean age of 64.6 ± 9.8 years (range: 50-93 years).

METHODS : A detailed ophthalmic examination was performed including spectral-domain optical coherence tomography (SD OCT) for measurement of the thickness of RPE-BM complex. Use Heidelberg software "Heidelberg Eye Explorer" for segmentation and measurements.

MAIN OUTCOME MEASURE : Thickness of RPE-BM complex.

RESULTS : In total, 3276 people (6530 eyes) were included in the study. In total, 1844 (56.3%) subjects were female. The mean age was 64.3 ± 9.6 years (range: 50-93 years). The mean refractive error (spherical equivalent) was -0.18 ± 2.04 diopters (range: -22.0 to +7.50 diopters). Mean thickness of the RPE-BM complex at the foveal center was 25.09 ± 3.98 μm (range: 17-37 μm). In multiple regression analysis, subfoveal thickness of the RPE-BM complex was associated with age (p = 0.039; beta: 0.22; B: 0.10 (95% CI: 0.01, 0.20)) and hypertension history (p = 0.038; beta: 0.23; B: 1.96 (95% CI: 0.12, 3.81)).

CONCLUSION : Mean subfoveal thickness of the RPE-BM complex was 25.09 ± 3.98 μm in elderly subjects with a mean age of 64.3 years increased with age and hypertension history. The increase in the thickness of RPE-BM complex may play a role in the pathophysiologic features of various age-related ocular conditions.

Shao Lei, Zhang Qing Lin, Zhang Chuan, Dong Li, Zhou Wen Da, Zhang Rui Heng, Wu Hao Tian, Wei Wen Bin

2022-Jan-20

Public Health Public Health

Machine learning in vascular surgery: a systematic review and critical appraisal.

In NPJ digital medicine

Machine learning (ML) is a rapidly advancing field with increasing utility in health care. We conducted a systematic review and critical appraisal of ML applications in vascular surgery. MEDLINE, Embase, and Cochrane CENTRAL were searched from inception to March 1, 2021. Study screening, data extraction, and quality assessment were performed by two independent reviewers, with a third author resolving discrepancies. All original studies reporting ML applications in vascular surgery were included. Publication trends, disease conditions, methodologies, and outcomes were summarized. Critical appraisal was conducted using the PROBAST risk-of-bias and TRIPOD reporting adherence tools. We included 212 studies from a pool of 2235 unique articles. ML techniques were used for diagnosis, prognosis, and image segmentation in carotid stenosis, aortic aneurysm/dissection, peripheral artery disease, diabetic foot ulcer, venous disease, and renal artery stenosis. The number of publications on ML in vascular surgery increased from 1 (1991-1996) to 118 (2016-2021). Most studies were retrospective and single center, with no randomized controlled trials. The median area under the receiver operating characteristic curve (AUROC) was 0.88 (range 0.61-1.00), with 79.5% [62/78] studies reporting AUROC ≥ 0.80. Out of 22 studies comparing ML techniques to existing prediction tools, clinicians, or traditional regression models, 20 performed better and 2 performed similarly. Overall, 94.8% (201/212) studies had high risk-of-bias and adherence to reporting standards was poor with a rate of 41.4%. Despite improvements over time, study quality and reporting remain inadequate. Future studies should consider standardized tools such as PROBAST and TRIPOD to improve study quality and clinical applicability.

Li Ben, Feridooni Tiam, Cuen-Ojeda Cesar, Kishibe Teruko, de Mestral Charles, Mamdani Muhammad, Al-Omran Mohammed

2022-Jan-19

Cardiology Cardiology

Point-of-care artificial intelligence-enabled ECG for dyskalemia: a retrospective cohort analysis for accuracy and outcome prediction.

In NPJ digital medicine

Dyskalemias are common electrolyte disorders associated with high cardiovascular risk. Artificial intelligence (AI)-assisted electrocardiography (ECG) has been evaluated as an early-detection approach for dyskalemia. The aims of this study were to determine the clinical accuracy of AI-assisted ECG for dyskalemia and prognostic ability on clinical outcomes such as all-cause mortality, hospitalizations, and ED revisits. This retrospective cohort study was done at two hospitals within a health system from May 2019 to December 2020. In total, 26,499 patients with 34,803 emergency department (ED) visits to an academic medical center and 6492 ED visits from 4747 patients to a community hospital who had a 12-lead ECG to estimate ECG-K+ and serum laboratory potassium measurement (Lab-K+) within 1 h were included. ECG-K+ had mean absolute errors (MAEs) of ≤0.365 mmol/L. Area under receiver operating characteristic curves for ECG-K+ to predict moderate-to-severe hypokalemia (Lab-K+ ≤3 mmol/L) and moderate-to-severe hyperkalemia (Lab-K+ ≥ 6 mmol/L) were >0.85 and >0.95, respectively. The U-shaped relationships between K+ concentration and adverse outcomes were more prominent for ECG-K+ than for Lab-K+. ECG-K+ and Lab-K+ hyperkalemia were associated with high HRs for 30-day all-cause mortality. Compared to hypokalemic Lab-K+, patients with hypokalemic ECG-K+ had significantly higher risk for adverse outcomes after full confounder adjustment. In addition, patients with normal Lab-K+ but dyskalemic ECG-K+ (pseudo-positive) also exhibited more co-morbidities and had worse outcomes. Point-of-care bloodless AI ECG-K+ not only rapidly identified potentially severe hypo- and hyperkalemia, but also may serve as a biomarker for medical complexity and an independent predictor for adverse outcomes.

Lin Chin, Chau Tom, Lin Chin-Sheng, Shang Hung-Sheng, Fang Wen-Hui, Lee Ding-Jie, Lee Chia-Cheng, Tsai Shi-Hung, Wang Chih-Hung, Lin Shih-Hua

2022-Jan-19

General General

Exploiting time series of Sentinel-1 and Sentinel-2 to detect grassland mowing events using deep learning with reject region.

In Scientific reports ; h5-index 158.0

Governments pay agencies to control the activities of farmers who receive governmental support. Field visits are costly and highly time-consuming; hence remote sensing is widely used for monitoring farmers' activities. Nowadays, a vast amount of available data from the Sentinel mission significantly boosted research in agriculture. Estonia is among the first countries to take advantage of this data source to automate mowing and ploughing events detection across the country. Although techniques that rely on optical data for monitoring agriculture events are favourable, the availability of such data during the growing season is limited. Thus, alternative data sources have to be evaluated. In this paper, we developed a deep learning model with an integrated reject option for detecting grassland mowing events using time series of Sentinel-1 and Sentinel-2 optical images acquired from 2000 fields in Estonia in 2018 during the vegetative season. The rejection mechanism is based on a threshold over the prediction confidence of the proposed model. The proposed model significantly outperforms the state-of-the-art technique and achieves event accuracy of 73.3% and end of season accuracy of 94.8%.

Komisarenko Viacheslav, Voormansik Kaupo, Elshawi Radwa, Sakr Sherif

2022-Jan-19

General General

Blood cancer prediction using leukemia microarray gene data and hybrid logistic vector trees model.

In Scientific reports ; h5-index 158.0

Blood cancer has been a growing concern during the last decade and requires early diagnosis to start proper treatment. The diagnosis process is costly and time-consuming involving medical experts and several tests. Thus, an automatic diagnosis system for its accurate prediction is of significant importance. Diagnosis of blood cancer using leukemia microarray gene data and machine learning approach has become an important medical research today. Despite research efforts, desired accuracy and efficiency necessitate further enhancements. This study proposes an approach for blood cancer disease prediction using the supervised machine learning approach. For the current study, the leukemia microarray gene dataset containing 22,283 genes, is used. ADASYN resampling and Chi-squared (Chi2) features selection techniques are used to resolve imbalanced and high-dimensional dataset problems. ADASYN generates artificial data to make the dataset balanced for each target class, and Chi2 selects the best features out of 22,283 to train learning models. For classification, a hybrid logistics vector trees classifier (LVTrees) is proposed which utilizes logistic regression, support vector classifier, and extra tree classifier. Besides extensive experiments on the datasets, performance comparison with the state-of-the-art methods has been made for determining the significance of the proposed approach. LVTrees outperform all other models with ADASYN and Chi2 techniques with a significant 100% accuracy. Further, a statistical significance T-test is also performed to show the efficacy of the proposed approach. Results using k-fold cross-validation prove the supremacy of the proposed model.

Rupapara Vaibhav, Rustam Furqan, Aljedaani Wajdi, Shahzad Hina Fatima, Lee Ernesto, Ashraf Imran

2022-Jan-19

General General

Self-directed online machine learning for topology optimization.

In Nature communications ; h5-index 260.0

Topology optimization by optimally distributing materials in a given domain requires non-gradient optimizers to solve highly complicated problems. However, with hundreds of design variables or more involved, solving such problems would require millions of Finite Element Method (FEM) calculations whose computational cost is huge and impractical. Here we report Self-directed Online Learning Optimization (SOLO) which integrates Deep Neural Network (DNN) with FEM calculations. A DNN learns and substitutes the objective as a function of design variables. A small number of training data is generated dynamically based on the DNN's prediction of the optimum. The DNN adapts to the new training data and gives better prediction in the region of interest until convergence. The optimum predicted by the DNN is proved to converge to the true global optimum through iterations. Our algorithm was tested by four types of problems including compliance minimization, fluid-structure optimization, heat transfer enhancement and truss optimization. It reduced the computational time by 2 ~ 5 orders of magnitude compared with directly using heuristic methods, and outperformed all state-of-the-art algorithms tested in our experiments. This approach enables solving large multi-dimensional optimization problems.

Deng Changyu, Wang Yizhou, Qin Can, Fu Yun, Lu Wei

2022-Jan-19

General General

Machine Learning-Driven, Sensor-Integrated Microfluidic Device for Monitoring and Control of Supersaturation for Automated Screening of Crystalline Materials.

In ACS sensors

Integrating sensors in miniaturized devices allow for fast and sensitive detection and precise control of experimental conditions. One of the potential applications of a sensor-integrated microfluidic system is to measure the solute concentration during crystallization. In this study, a continuous-flow microfluidic mixer is paired with an electrochemical sensor to enable in situ measurement of the supersaturation. This sensor is investigated as the predictive measurement of the supersaturation during the antisolvent crystallization of l-histidine in the water-ethanol mixture. Among the various metals tested in a batch system for their sensitivity toward l-histidine, Pt showed the highest sensitivity. A Pt-printed electrode was inserted in the continuous-flow microfluidic mixer, and the cyclic voltammograms of the system were obtained for different concentrations of l-histidine and different water-to-ethanol ratios. The sensor was calibrated for different ratios of antisolvent and concentrations of l-histidine with respect to the change of the measured anodic slope. Additionally, a machine-learning algorithm using neural networks was developed to predict the supersaturation of l-histidine from the measured anodic slope. The electrochemical sensors have shown sensitivity toward l-histidine, l-glutamic acid, and o-aminobenzoic acid, which consist of functional groups present in almost 80% of small-molecule drugs on the market. The machine learning-guided electrochemical sensors can be applied to other small molecules with similar functional groups for automated screening of crystallization conditions in microfluidic devices.

Coliaie Paria, Prajapati Aditya, Ali Rabia, Korde Akshay, Kelkar Manish S, Nere Nandkishor K, Singh Meenesh R

2022-Jan-19

continuous-flow crystallization, electrochemical sensor, machine learning, sensor-integrated microfluidics, supersaturation measurements

Public Health Public Health

Reconciling public health common good and individual privacy: new methods and issues in geoprivacy.

In International journal of health geographics

This article provides a state-of-the-art summary of location privacy issues and geoprivacy-preserving methods in public health interventions and health research involving disaggregate geographic data about individuals. Synthetic data generation (from real data using machine learning) is discussed in detail as a promising privacy-preserving approach. To fully achieve their goals, privacy-preserving methods should form part of a wider comprehensive socio-technical framework for the appropriate disclosure, use and dissemination of data containing personal identifiable information. Select highlights are also presented from a related December 2021 AAG (American Association of Geographers) webinar that explored ethical and other issues surrounding the use of geospatial data to address public health issues during challenging crises, such as the COVID-19 pandemic.

Kamel Boulos Maged N, Kwan Mei-Po, El Emam Khaled, Chung Ada Lai-Ling, Gao Song, Richardson Douglas B

2022-Jan-19

Geoprivacy, Location privacy, Machine learning, Privacy enhancing technology, Public health, Synthetic data

Surgery Surgery

Enhancing Pseudo Label Quality for Semi-SupervisedDomain-Generalized Medical Image Segmentation

ArXiv Preprint

Generalizing the medical image segmentation algorithms tounseen domains is an important research topic for computer-aided diagnosis and surgery. Most existing methods requirea fully labeled dataset in each source domain. Although (Liuet al. 2021b) developed a semi-supervised domain general-ized method, it still requires the domain labels. This paperpresents a novel confidence-aware cross pseudo supervisionalgorithm for semi-supervised domain generalized medicalimage segmentation. The main goal is to enhance the pseudolabel quality for unlabeled images from unknown distribu-tions. To achieve it, we perform the Fourier transformationto learn low-level statistic information across domains andaugment the images to incorporate cross-domain information.With these augmentations as perturbations, we feed the inputto a confidence-aware cross pseudo supervision network tomeasure the variance of pseudo labels and regularize the net-work to learn with more confident pseudo labels. Our methodsets new records on public datasets,i.e., M&Ms and SCGM.Notably, without using domain labels, our method surpassesthe prior art that even uses domain labels by 11.67% on Diceon M&Ms dataset with 2% labeled data. Code will be avail-able after the conference.

Huifeng Yao, Xiaowei Hu, Xiaomeng Li

2022-01-21

Pathology Pathology

Diffusion kernel-based predictive modeling of KRAS dependency in KRAS wild type cancer cell lines.

In NPJ systems biology and applications

Recent progress in clinical development of KRAS inhibitors has raised interest in predicting the tumor dependency on frequently mutated RAS-pathway oncogenes. However, even without such activating mutations, RAS proteins represent core components in signal integration of several membrane-bound kinases. This raises the question of applications of specific inhibitors independent from the mutational status. Here, we examined CRISPR/RNAi data from over 700 cancer cell lines and identified a subset of cell lines without KRAS gain-of-function mutations (KRASwt) which are dependent on KRAS expression. Combining machine learning-based modeling and whole transcriptome data with prior variable selection through protein-protein interaction network analysis by a diffusion kernel successfully predicted KRAS dependency in the KRASwt subgroup and in all investigated cancer cell lines. In contrast, modeling by RAS activating events (RAE) or previously published RAS RNA-signatures did not provide reliable results, highlighting the heterogeneous distribution of RAE in KRASwt cell lines and the importance of methodological references for expression signature modeling. Furthermore, we show that predictors of KRASwt models contain non-substitutable information signals, indicating a KRAS dependency phenotype in the KRASwt subgroup. Our data suggest that KRAS dependent cancers harboring KRAS wild type status could be targeted by directed therapeutic approaches. RNA-based machine learning models could help in identifying responsive and non-responsive tumors.

Ulmer Bastian, Odenthal Margarete, Buettner Reinhard, Roth Wilfried, Kloth Michael

2022-Jan-19

oncology Oncology

Automated quantification of levels of breast terminal duct lobular (TDLU) involution using deep learning.

In NPJ breast cancer

Convolutional neural networks (CNNs) offer the potential to generate comprehensive quantitative analysis of histologic features. Diagnostic reporting of benign breast disease (BBD) biopsies is usually limited to subjective assessment of the most severe lesion in a sample, while ignoring the vast majority of tissue features, including involution of background terminal duct lobular units (TDLUs), the structures from which breast cancers arise. Studies indicate that increased levels of age-related TDLU involution in BBD biopsies predict lower breast cancer risk, and therefore its assessment may have potential value in risk assessment and management. However, assessment of TDLU involution is time-consuming and difficult to standardize and quantitate. Accordingly, we developed a CNN to enable automated quantitative measurement of TDLU involution and tested its performance in 174 specimens selected from the pathology archives at Mayo Clinic, Rochester, MN. The CNN was trained and tested on a subset of 33 biopsies, delineating important tissue types. Nine quantitative features were extracted from delineated TDLU regions. Our CNN reached an overall dice-score of 0.871 (±0.049) for tissue classes versus reference standard annotation. Consensus of four reviewers scoring 705 images for TDLU involution demonstrated substantial agreement with the CNN method (unweighted κappa = 0.747 ± 0.01). Quantitative involution measures showed anticipated associations with BBD histology, breast cancer risk, breast density, menopausal status, and breast cancer risk prediction scores (p < 0.05). Our work demonstrates the potential to improve risk prediction for women with BBD biopsies by applying CNN approaches to generate automated quantitative evaluation of TDLU involution.

de Bel Thomas, Litjens Geert, Ogony Joshua, Stallings-Mann Melody, Carter Jodi M, Hilton Tracy, Radisky Derek C, Vierkant Robert A, Broderick Brendan, Hoskin Tanya L, Winham Stacey J, Frost Marlene H, Visscher Daniel W, Allers Teresa, Degnim Amy C, Sherman Mark E, van der Laak Jeroen A W M

2022-Jan-19

General General

[Evaluation of Radiograph Accuracy in Skull X-ray Images Using Deep Learning].

In Nihon Hoshasen Gijutsu Gakkai zasshi

PURPOSE : Accurate positioning is essential for radiography, and it is especially important to maintain image reproducibility in follow-up observations. The decision on re-taking radiographs is entrusting to the individual radiological technologist. The evaluation is a visual and qualitative evaluation and there are individual variations in the acceptance criteria. In this study, we propose a method of image evaluation using a deep convolutional neural network (DCNN) for skull X-ray images.

METHOD : The radiographs were obtained from 5 skull phantoms and were classified by simple network and VGG16. The discrimination ability of DCNN was verified by recognizing the X-ray projection angle and the retake of the radiograph. DCNN architectures were used with the different input image sizes and were evaluated by 5-fold cross-validation and leave-one-out cross-validation.

RESULT : Using the 5-fold cross-validation, the classification accuracy was 99.75% for the simple network and 80.00% for the VGG16 in small input image sizes, and when the input image size was general image size, simple network and VGG16 showed 79.58% and 80.00%, respectively.

CONCLUSION : The experimental results showed that the combination between the small input image size, and the shallow DCNN architecture was suitable for the four-category classification in X-ray projection angles. The classification accuracy was up to 99.75%. The proposed method has the potential to automatically recognize the slight projection angles and the re-taking images to the acceptance criteria. It is considered that our proposed method can contribute to feedback for re-taking images and to reduce radiation dose due to individual subjectivity.

Mitsutake Hideyoshi, Watanabe Haruyuki, Sakaguchi Aya, Uchiyama Kiyoshi, Lee Yongbum, Hayashi Norio, Shimosegawa Masayuki, Ogura Toshihiro

2022

X-ray image, artificial intelligence (AI), classification, deep convolutional neural network (DCNN), radiograph accuracy

Radiology Radiology

[Improvement of Motion Artifacts in Brain MRI Using Deep Learning by Simulation Training Data].

In Nihon Hoshasen Gijutsu Gakkai zasshi

PURPOSE : To test whether deep learning can be used to effectively reduce artifacts in MR images of the brain.

METHODS : In this study, a large set of images with and without motion artifacts is needed for training. It is difficult to collect training data from clinical images because it requires a lot of effort and time. We have created motion artifact images of the brain by computer simulation. As an experimental study, we obtained original images for deep learning from 20 volunteers. These original images were used to create various images of different artifacts by computer simulation and these were used the input images for deep learning. The same method was used to create test images and these images were used to compare the structural similarity (SSIM) index and peak signal-to-noise ratio (PSNR) between the input images and output images using the three denoising methods. The network models used were U-shaped fully convolutional network (U-Net), denoising convolutional neural network (DnCNN) and wide inference network and 5 layers Residual learning and batch normalization (Win5RB).

RESULTS : U-Net was the most effective model for reducing motion artifacts. The SSIM and PSNR were 0.978 and 32.5 dB.

CONCLUSION : This is an effective method to reduce artifacts without degrading the image quality of brain MRI images.

Muro Isao, Shimizu Syuntaro, Tsukamoto Hikari

2022

brain magnetic resonance imaging, computer simulation, deep learning convolutional neural network, motion artifact

General General

SynPathy: Predicting Drug Synergy through Drug-Associated Pathways Using Deep Learning.

In Molecular cancer research : MCR

Drug combination therapy has become a promising therapeutic strategy for cancer treatment. While high-throughput drug combination screening is effective for identifying synergistic drug combinations, measuring all possible combinations is impractical due to the vast space of therapeutic agents and cell lines. In this study, we propose a biologically-motivated deep learning approach to identify pathway-level features from drug and cell lines' molecular data for predicting drug synergy and quantifying the interactions in synergistic drug pairs. This method obtained an MSE of 70.6{plus minus}6.4, significantly surpassing previous approaches while providing potential candidate pathways to explain the prediction. We further demonstrate that drug combinations tend to be more synergistic when their top contributing pathways are closer to each other on a protein interaction network, suggesting a potential strategy for combination therapy with topologically interacting pathways. Our computational approach can thus be utilized both for pre-screening of potential drug combinations and for designing new combinations based on proximity of pathways associated with drug targets and cell lines. Implications: Our computational framework may be translated in the future to clinical scenarios where synergistic drugs are tailored to the patient and additionally, drug development could benefit from designing drugs that target topologically close pathways.

Tang Yi-Ching, Gottlieb Assaf

2022-Jan-19

General General

Prediction of diabetic kidney disease with machine learning algorithms, upon the initial diagnosis of type 2 diabetes mellitus.

In BMJ open diabetes research & care

INTRODUCTION : Diabetic kidney disease (DKD) accounts for the majority of increased risk of mortality for patients with diabetes, and eventually manifests in approximately half of those patients diagnosed with type 2 diabetes mellitus (T2DM). Although increased screening frequency can avoid delayed diagnoses, this is not uniformly implemented. The purpose of this study was to develop and retrospectively validate a machine learning algorithm (MLA) that predicts stages of DKD within 5 years upon diagnosis of T2DM.

RESEARCH DESIGN AND METHODS : Two MLAs were trained to predict stages of DKD severity, and compared with the Centers for Disease Control and Prevention (CDC) risk score to evaluate performance. The models were validated on a hold-out test set as well as an external dataset sourced from separate facilities.

RESULTS : The MLAs outperformed the CDC risk score in both the hold-out test and external datasets. Our algorithms achieved an area under the receiver operating characteristic curve (AUROC) of 0.75 on the hold-out set for prediction of any-stage DKD and an AUROC of over 0.82 for more severe endpoints, compared with the CDC risk score with an AUROC <0.70 on all test sets and endpoints.

CONCLUSION : This retrospective study shows that an MLA can provide timely predictions of DKD among patients with recently diagnosed T2DM.

Allen Angier, Iqbal Zohora, Green-Saxena Abigail, Hurtado Myrna, Hoffman Jana, Mao Qingqing, Das Ritankar

2022-Jan

algorithms, decision support techniques, diabetes mellitus, kidney diseases, type 2

General General

Early dietitian referral in lung cancer: use of machine learning.

In BMJ supportive & palliative care ; h5-index 29.0

OBJECTIVES : The Dietetic Assessment and Intervention in Lung Cancer (DAIL) study was an observational cohort study. It triaged the need for dietetic input in patients with lung cancer, using questionnaires with 137 responses. This substudy tested if machine learning could predict need to see a dietitian (NTSD) using 5 or 10 measures.

METHODS : 76 cases from DAIL were included (Royal Surrey NHS Foundation Trust; RSH: 56, Frimley Park Hospital; FPH 20). Univariate analysis was used to find the strongest correlates with NTSD and 'critical need to see a dietitian' CNTSD. Those with a Spearman correlation above ±0.4 were selected to train a support vector machine (SVM) to predict NTSD and CNTSD. The 10 and 5 best correlates were evaluated.

RESULTS : 18 and 13 measures had a correlation above ±0.4 for NTSD and CNTSD, respectively, producing SVMs with 3% and 7% misclassification error. 10 measures yielded errors of 7% (NTSD) and 9% (CNTSD). 5 measures yielded between 7% and 11% errors. SVM trained on the RSH data and tested on the FPH data resulted in errors of 20%.

CONCLUSIONS : Machine learning can predict NTSD producing misclassification errors <10%. With further work, this methodology allows integrated early referral to a dietitian independently of a healthcare professional.

Chung Michael, Phillips Iain, Allan Lindsey, Westran Naomi, Hug Adele, Evans Philip M

2022-Jan-19

cachexia, lung, symptoms and symptom management

General General

Deep learning fetal ultrasound video model match human observers in biometric measurements.

In Physics in medicine and biology

OBJECTIVE : This work investigates the use of deep convolutional neural networks (CNN) to automatically perform measurements of fetal body parts, including head circumference, biparietal diameter, abdominal circumference and femur length, and to estimate gestational age and fetal weight using fetal ultrasound videos.

APPROACH : We developed a novel multi-task CNN-based spatio-temporal fetal US feature extraction and standard plane detection algorithm (called FUVAI) and evaluated the method on 50 freehand fetal US video scans. We compared FUVAI fetal biometric measurements with measurements made by five experienced sonographers at two time points separated by at least two weeks. Intra- and inter-observer variabilities were estimated.

MAIN RESULTS : We found that automated fetal biometric measurements obtained by FUVAI were comparable to the measurements performed by experienced sonographers The observed differences in measurement values were within the range of inter- and intra-observer variability. Moreover, analysis has shown that these differences were not statistically significant when comparing any individual medical expert to our model.

SIGNIFICANCE : We argue that FUVAI has the potential to assist sonographers who perform fetal biometric measurements in clinical settings by providing them with suggestions regarding the best measuring frames, along with automated measurements. Moreover, FUVAI is able perform these tasks in just a few seconds, which is a huge difference compared to the average of six minutes taken by sonographers. This is significant, given the shortage of medical experts capable of interpreting fetal ultrasound images in numerous countries.

Płotka Szymon, Klasa Adam, Lisowska Aneta, Seliga-Siwecka Joanna, Lipa Michal, Trzcinski Tomasz, Sitek Arkadiusz

2022-Jan-20

Deep Learning, Fetal Measurements, Fetal Ultrasound Imaging, Medical Image Segmentation

Radiology Radiology

Global-Local attention network with multi-task uncertainty loss for abnormal lymph node detection in MR images.

In Medical image analysis

Accurate and reliable detection of abnormal lymph nodes in magnetic resonance (MR) images is very helpful for the diagnosis and treatment of numerous diseases. However, it is still a challenging task due to similar appearances between abnormal lymph nodes and other tissues. In this paper, we propose a novel network based on an improved Mask R-CNN framework for the detection of abnormal lymph nodes in MR images. Instead of laboriously collecting large-scale pixel-wise annotated training data, pseudo masks generated from RECIST bookmarks on hand are utilized as the supervision. Different from the standard Mask R-CNN architecture, there are two main innovations in our proposed network: 1) global-local attention which encodes the global and local scale context for detection and utilizes the channel attention mechanism to extract more discriminative features and 2) multi-task uncertainty loss which adaptively weights multiple objective loss functions based on the uncertainty of each task to automatically search the optimal solution. For the experiments, we built a new abnormal lymph node dataset with 821 RECIST bookmarks of 41 different types of abnormal abdominal lymph nodes from 584 different patients. The experimental results showed the superior performance of our algorithm over compared state-of-the-art approaches.

Wang Shuai, Zhu Yingying, Lee Sungwon, Elton Daniel C, Shen Thomas C, Tang Youbao, Peng Yifan, Lu Zhiyong, Summers Ronald M

2022-Jan-08

Deep learning, Image detection, Lymph node, Magnetic resonance imaging

General General

Multi-dimensional conditional mutual information with application on the EEG signal analysis for spatial cognitive ability evaluation.

In Neural networks : the official journal of the International Neural Network Society

This study aims to explore an effective method to evaluate spatial cognitive ability, which can effectively extract and classify the feature of EEG signals collected from subjects participating in the virtual reality (VR) environment; and evaluate the training effect objectively and quantitatively to ensure the objectivity and accuracy of spatial cognition evaluation, according to the classification results. Therefore, a multi-dimensional conditional mutual information (MCMI) method is proposed, which could calculate the coupling strength of two channels considering the influence of other channels. The coupled characteristics of the multi-frequency combination were transformed into multi-spectral images, and the image data were classified employing the convolutional neural networks (CNN) model. The experimental results showed that the multi-spectral image transform features based on MCMI are better in classification than other methods, and among the classification results of six band combinations, the best classification accuracy of Beta1-Beta2-Gamma combination is 98.3%. The MCMI characteristics on the Beta1-Beta2-Gamma band combination can be a biological marker for the evaluation of spatial cognition. The proposed feature extraction method based on MCMI provides a new perspective for spatial cognitive ability assessment and analysis.

Wen Dong, Li Rou, Jiang Mengmeng, Li Jingjing, Liu Yijun, Dong Xianling, Saripan M Iqbal, Song Haiqing, Han Wei, Zhou Yanhong

2021-Dec-25

Coupling feature extraction, Multi-dimensional conditional mutual information, Multi-spectral image, Spatial cognition, Task-state EEG signal

General General

A computer-aided diagnosis system using white-light endoscopy for the prediction of conventional adenoma with high grade dysplasia.

In Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver

OBJECTIVES : We developed a computer-aided diagnosis system called ECRCCAD using standard white-light endoscopy (WLE) for predicting conventional adenomas with high-grade dysplasia (HGD) to optimise the patients' management decisions during colonoscopy.

METHODS : Pretraining model was used to fine-tune the model parameters by transfer learning. 2,397 images of HGD and 2,487 low-grade dysplasia (LGD) images were randomly assigned (8:1:1) to the training, optimising, and internal validation dataset. The prospective validation dataset is the frames accessed from colonoscope videoes. One independent rural hospital provided an external validation dataset. Histopathological diagnosis was used as the standard criterion. The capability of the ECRCCAD to distinguish HGD was assessed and compared with two expert endoscopists.

RESULTS : The accuracy, sensitivity and specificity for diagnosis of HGD in the internal validation set were 90.5%, 93.2%, 87.9%, respectively. While 88.2%, 85.4%, 89.8%, respectively, for the external validation set. For the prospective validation set, ECRCCAD achieved an AUC of 93.5% in diagnosing HGD. The performance of ECRCCAD in diagnosing HGD was better than that of the expert endoscopist in the external validation set (88.2% vs. 71.5%, P < 0.0001).

CONCLUSION : ECRCCAD had good diagnostic capability for HGD and enabled a more convenient and accurate diagnosis using WLE.

Meng Sijun, Zheng Yueping, Wang Wangyue, Su Ruizhang, Zhang Yu, Zhang Yi, Guo Bingting, Han Zhaofang, Zhang Wen, Qin Wenjuan, Jiang Zhenghua, Xu Haineng, Bu Yemei, Zhong Yuhuan, He Yulong, Qiu Hesong, Xu Wen, Chen Hong, Wu Siqi, Zhang Yongxiu, Dong Chao, Hu Yongchao, Xie Lizhong, Li Xugong, Zhang Changhua, Pan Wensheng, Wu Shuisheng, Hu Yiqun

2022-Jan-16

Artificial intelligence, Colorectal cancer, Computer-aided diagnosis system, High grade dysplasia, White light endoscopy

General General

A novel lightweight bilateral segmentation network for detecting oil spills on the sea surface.

In Marine pollution bulletin

Accidental oil spills from pipelines or tankers have posed a big threat to marine life and natural resources. This paper presents a novel lightweight bilateral segmentation network for detecting oil spills on the sea surface. A novel deep-learning semantic-segmentation algorithm is firstly created for analyzing the characteristics of oil spill images. A Bilateral Segmentation Network (BiSeNetV2) is then selected as the basic network architecture and evaluated by using experimental comparison of the current mainstream networks on detection accuracy and real-time performances for oil samples. Furthermore, the Gather-and-Expansion (GE) layer of the semantic branch in the traditional network is redesigned and the parameter complexity is reduced. A dual attention mechanism is deployed in the two branches of the BiSeNetV2 to solve the problem of inter-class similarity. Finally, experimental results are given to show the good detection accuracy of the proposed network.

Chen Yuqing, Sun Yuhan, Yu Wei, Liu Yaowen, Hu Huosheng

2022-Jan-17

BiSeNet V2, Dual attention mechanism, Oil spill, Visible/infrared

Pathology Pathology

Computational pathology aids derivation of microRNA biomarker signals from Cytosponge samples.

In EBioMedicine

BACKGROUND : Non-endoscopic cell collection devices combined with biomarkers can detect Barrett's intestinal metaplasia and early oesophageal cancer. However, assays performed on multi-cellular samples lose information about the cell source of the biomarker signal. This cross-sectional study examines whether a bespoke artificial intelligence-based computational pathology tool could ascertain the cellular origin of microRNA biomarkers, to inform interpretation of the disease pathology, and confirm biomarker validity.

METHODS : The microRNA expression profiles of 110 targets were assessed with a custom multiplexed panel in a cohort of 117 individuals with reflux that took a Cytosponge test. A computational pathology tool quantified the amount of columnar epithelium present in pathology slides, and results were correlated with microRNA signals. An independent cohort of 139 Cytosponges, each from an individual patient, was used to validate the findings via qPCR.

FINDINGS : Seventeen microRNAs are upregulated in BE compared to healthy squamous epithelia, of which 13 remain upregulated in dysplasia. A pathway enrichment analysis confirmed association to neoplastic and cell cycle regulation processes. Ten microRNAs positively correlated with columnar epithelium content, with miRNA-192-5p and -194-5p accurately detecting the presence of gastric cells (AUC 0.97 and 0.95). In contrast, miR-196a-5p is confirmed as a specific BE marker.

INTERPRETATION : Computational pathology tools aid accurate cellular attribution of molecular signals. This innovative design with multiplex microRNA coupled with artificial intelligence has led to discovery of a quality control metric suitable for large scale application of the Cytosponge. Similar approaches could aid optimal interpretation of biomarkers for clinical use.

FUNDING : Funded by the NIHR Cambridge Biomedical Research Centre, the Medical Research Council, the Rosetrees and Stoneygate Trusts, and CRUK core grants.

Masqué-Soler Neus, Gehrung Marcel, Kosmidou Cassandra, Li Xiaodun, Diwan Izzuddin, Rafferty Conor, Atabakhsh Elnaz, Markowetz Florian, Fitzgerald Rebecca C

2022-Jan-17

Artificial intelligence, “Barretts oesophagus”, Computerized image analysis, Dysplasia, Oesophageal cancer, Screening

General General

A bagging ensemble machine learning framework to predict overall cognitive function of schizophrenia patients with cognitive domains and tests.

In Asian journal of psychiatry

BACKGROUND : It has been indicated that the interplay between functional outcomes and cognitive functions in schizophrenia is arbitrated by clinical symptoms, where cognitive functions are evaluated by cognitive domains and cognitive tests.

METHODS : To determine which single cognitive domain or test can best predict the overall cognitive function of schizophrenia, we established a bagging ensemble framework resulting from the analysis of factors such as 7 cognitive domain scores and 11 cognitive test scores of 302 schizophrenia patients in the Taiwanese population. We compared our bagging ensemble framework with other state-of-the-art algorithms such as multilayer feedforward neural networks, linear regression, support vector machine, and random forests.

RESULTS : The analysis revealed that among the 7 cognitive domains, the speed of processing domain can best predict the overall cognitive function in schizophrenia using our bagging ensemble framework. In addition, among the 11 cognitive tests, the visual learning and memory test can best predict the overall cognitive function in schizophrenia using our bagging ensemble framework. Finally, among the 7 cognitive domains and 11 cognitive tests, the speed of processing domain can best predict the overall cognitive function in schizophrenia using our bagging ensemble framework.

CONCLUSION : The study implicates that the bagging ensemble framework may provide an applicable approach to develop tools for forecasting overall cognitive function in schizophrenia using cognitive domains and/or cognitive tests.

Lin Eugene, Lin Chieh-Hsin, Lane Hsien-Yuan

2022-Jan-14

Cognitive domain, Cognitive test, Ensemble learning, Neurocognition, Schizophrenia, Social cognition

General General

Altered effective connectivity between lateral occipital cortex and superior parietal lobule contributes to manipulability-related modulation of the Ebbinghaus illusion.

In Cortex; a journal devoted to the study of the nervous system and behavior

Action and perception interact reciprocally in our daily life. Previous studies have found that object manipulability can affect visual perceptual processing. Here we probed the neural mechanisms underlying the manipulability-related modulation effect using the well-known Ebbinghaus illusion with the central circle replaced by a high (i.e., a basketball) or a low (i.e., a watermelon) manipulable object. Participants (N = 30) were required to adjust the size of a comparison circle to match that of the central object in the Ebbinghaus configuration. The results showed that the perceived illusion magnitude for the basketball target was significantly reduced than that for the watermelon target, and the manipulability-related modulation effect was manifested in self-connections in the left primary visual cortex and the left superior parietal lobule (SPL), as well as reciprocal connections between the left lateral occipital cortex (LOC) and SPL. Notably, the disparity of the illusion magnitude between the watermelon and the basketball target was positively correlated with the extrinsic connectivity from the left LOC to SPL. The findings suggest that object manipulability can modulate the Ebbinghaus illusion, likely through accentuating the high-manipulability object along the visual processing streams. Moreover, they provide clear evidence that manipulability-related modulation of visual perception relies on the functional interactions between the ventral and dorsal visual pathways.

Chen Lihong, Zhu Shengnan, Feng Bengang, Zhang Xue, Jiang Yi

2021-Dec-30

Dynamic causal modeling, Ebbinghaus illusion, Object manipulability, fMRI

General General

Predicting Individual Traits from Unperformed Tasks.

In NeuroImage ; h5-index 117.0

Relating individual differences in cognitive traits to brain functional organization is a long-lasting challenge for the neuroscience community. Individual intelligence scores were previously predicted from whole-brain connectivity patterns, extracted from functional magnetic resonance imaging (fMRI) data acquired at rest. Recently, it was shown that task-induced brain activation maps outperform these resting-state connectivity patterns in predicting individual intelligence, suggesting that a cognitively demanding environment improves prediction of cognitive abilities. Here, we use data from the Human Connectome Project to predict task-induced brain activation maps from resting-state fMRI, and proceed to use these predicted activity maps to further predict individual differences in a variety of traits. While models based on original task activation maps remain the most accurate, models based on predicted maps significantly outperformed those based on the resting-state connectome. Thus, we provide a promising approach for the evaluation of measures of human behavior from brain activation maps, that could be used without having participants actually perform the tasks.

Gal Shachar, Tik Niv, Bernstein-Eliav Michal, Tavor Ido

2022-Jan-17

Functional-connectivity, individual traits, machine-learning, prediction, resting-state fMRI, task fMRI

Surgery Surgery

Theoretical and Methodological Analysis of EEG based Seizure Detection and Prediction: An Exhaustive Review.

In Journal of neuroscience methods

Epilepsy is a chronic neurological disorder with a comparatively high prevalence rate. It is a condition characterized by repeated and unprovoked seizures. Seizures are managed with the help of Antiepileptic Drugs and other treatment options like epilepsy surgery. Unfortunately, people with Drug-Resistant Epilepsy is not able to achieve full seizure freedom since there is no effective treatment to handle the type of seizure or its cause. The possibility of untimely death in people with epilepsy is higher than in the general population. The prediction of seizures before the onset is very crucial in managing patients with uncontrollable seizures. The detection of seizures from long EEG recordings can help in the proper diagnosis of epilepsy. The focus of this review is to explore the methods for both seizure detection and prediction from traditional signal processing to models employing machine learning principally deep learning. The various neuroimaging techniques and comparison of non-invasive techniques for EEG data acquisition are presented. A thorough analysis of artifact removal techniques and entropy-based approaches for seizure detection is conducted. The challenges in developing robust models and the efficacy of existing approaches are also discussed. The novelty of this study is the comprehensive overview of the latest research on seizure detection and prediction with an emphasis on deep learning models.

Cherian Resmi, Kanaga E Gracemary

2022-Jan-17

Deep learning, EEG, Entropy, Epilepsy, Machine learning, Seizure detection, seizure prediction

Public Health Public Health

A full pipeline of diagnosis and prognosis the risk of chronic diseases using deep learning and Shapley values: The Ravansar county anthropometric cohort study.

In PloS one ; h5-index 176.0

Anthropometry is a Greek word that consists of the two words "Anthropo" meaning human species and "metery" meaning measurement. It is a science that deals with the size of the body including the dimensions of different parts, the field of motion and the strength of the muscles of the body. Specific individual dimensions such as heights, widths, depths, distances, environments and curvatures are usually measured. In this article, we investigate the anthropometric characteristics of patients with chronic diseases (diabetes, hypertension, cardiovascular disease, heart attacks and strokes) and find the factors affecting these diseases and the extent of the impact of each to make the necessary planning. We have focused on cohort studies for 10047 qualified participants from Ravansar County. Machine learning provides opportunities to improve discrimination through the analysis of complex interactions between broad variables. Among the chronic diseases in this cohort study, we have used three deep neural network models for diagnosis and prognosis of the risk of type 2 diabetes mellitus (T2DM) as a case study. Usually in Artificial Intelligence for medicine tasks, Imbalanced data is an important issue in learning and ignoring that leads to false evaluation results. Also, the accuracy evaluation criterion was not appropriate for this task, because a simple model that is labeling all samples negatively has high accuracy. So, the evaluation criteria of precession, recall, AUC, and AUPRC were considered. Then, the importance of variables in general was examined to determine which features are more important in the risk of T2DM. Finally, personality feature was added, in which individual feature importance was examined. Performing by Shapley Values, the model is tuned for each patient so that it can be used for prognosis of T2DM risk for that patient. In this paper, we have focused and implemented a full pipeline of Data Creation, Data Preprocessing, Handling Imbalanced Data, Deep Learning model, true Evaluation method, Feature Importance and Individual Feature Importance. Through the results, the pipeline demonstrated competence in improving the Diagnosis and Prognosis the risk of T2DM with personalization capability.

Jafari Habib, Shohaimi Shamarina, Salari Nader, Kiaei Ali Akbar, Najafi Farid, Khazaei Soleiman, Niaparast Mehrdad, Abdollahi Anita, Mohammadi Masoud

2022

General General

CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach.

In PLoS computational biology

Circular RNAs (circRNAs) are non-coding RNAs with a special circular structure produced formed by the reverse splicing mechanism. Increasing evidence shows that circular RNAs can directly bind to RNA-binding proteins (RBP) and play an important role in a variety of biological activities. The interactions between circRNAs and RBPs are key to comprehending the mechanism of posttranscriptional regulation. Accurately identifying binding sites is very useful for analyzing interactions. In past research, some predictors on the basis of machine learning (ML) have been presented, but prediction accuracy still needs to be ameliorated. Therefore, we present a novel calculation model, CRBPDL, which uses an Adaboost integrated deep hierarchical network to identify the binding sites of circular RNA-RBP. CRBPDL combines five different feature encoding schemes to encode the original RNA sequence, uses deep multiscale residual networks (MSRN) and bidirectional gating recurrent units (BiGRUs) to effectively learn high-level feature representations, it is sufficient to extract local and global context information at the same time. Additionally, a self-attention mechanism is employed to train the robustness of the CRBPDL. Ultimately, the Adaboost algorithm is applied to integrate deep learning (DL) model to improve prediction performance and reliability of the model. To verify the usefulness of CRBPDL, we compared the efficiency with state-of-the-art methods on 37 circular RNA data sets and 31 linear RNA data sets. Moreover, results display that CRBPDL is capable of performing universal, reliable, and robust. The code and data sets are obtainable at https://github.com/nmt315320/CRBPDL.git.

Niu Mengting, Zou Quan, Lin Chen

2022-Jan-20

General General

Credit card fraud detection using a hierarchical behavior-knowledge space model.

In PloS one ; h5-index 176.0

With the advancement in machine learning, researchers continue to devise and implement effective intelligent methods for fraud detection in the financial sector. Indeed, credit card fraud leads to billions of dollars in losses for merchants every year. In this paper, a multi-classifier framework is designed to address the challenges of credit card fraud detections. An ensemble model with multiple machine learning classification algorithms is designed, in which the Behavior-Knowledge Space (BKS) is leveraged to combine the predictions from multiple classifiers. To ascertain the effectiveness of the developed ensemble model, publicly available data sets as well as real financial records are employed for performance evaluations. Through statistical tests, the results positively indicate the effectiveness of the developed model as compared with the commonly used majority voting method for combination of predictions from multiple classifiers in tackling noisy data classification as well as credit card fraud detection problems.

Nandi Asoke K, Randhawa Kuldeep Kaur, Chua Hong Siang, Seera Manjeevan, Lim Chee Peng

2022

General General

Multivariate Pattern Analysis in Identifying Neuropathic Pain Following Brachial Plexus Avulsion Injury: A PET/CT Study.

In Pain physician ; h5-index 45.0

BACKGROUND : Neuropathic pain following brachial plexus avulsion injury (BPAI) induces plastic changes in multiple brain regions associated with somatosensory function, pain, or cognition at the group level. The alternation of the whole pattern of resting-state brain activity and the feasibility of a brain imaging, information-based diagnosis of pain following BPAI is poorly investigated.

OBJECTIVES : To investigate whether brain pattern alternation can  identify neuropathic pain from healthy controls at an individual level and the specific regions that can be used as diagnostic neuroimaging biomarkers.

STUDY DESIGN : Controlled animal study.

SETTING : The research took place in the school of rehabilitation science of a university and affiliated hospitals.

METHODS : A total of 48 female Sprague-Dawley rats weighing 180 g-200 g were randomly assigned to either the BPAI group (n = 24) or normal control group (n = 24). A neuropathic pain rat model following BPAI was established in the BPAI group and a mechanical withdrawal threshold (MWT) test was performed to verify the presence of neuropathic pain. Micro-positron emission tomography with [Fluorine-18]-fluoro-2-deoxy-D-glucose (18F-FDG-PET) was used to obtain the whole brain metabolic activity scans. Multivariate pattern analysis (MVPA) was performed with a linear support vector machine (SVM) analysis both in PRoNTo toolbox (based on regions of interests) and SearchlightSearchlight approach (based on voxels within the region).

RESULTS : Compared with baseline status, MWT of the left (intact) forepaw was significantly reduced in the BPAI group (P < 0.001). The accuracy of a whole brain image that correctly discriminated BPAI from normal controls rats was 87.5% with both the PRoNTo toolbox and SearchlightSearchlight method. Pearson's correlation analysis revealed significant positive correlations (P < 0.05) between MWT and the standard taken values of brain regions including the left olfactory nucleus, right entorhinal cortex in the PRoNTo toolbox, and bilateral amygdala, right piriform cortex and right ventral hippocampus in Searchlight method.

LIMITATIONS : The alternation of metabolic connectivity among regions and functional connectivity among different networks were not investigated in the present study.

CONCLUSIONS : Our study indicated that MVPA based on the PET scans of rats' brains  could successfully identify neuropathic pain from health condition at the individual level and predictive regions could potentially be provided as neuroimaging biomarkers for the neuropathic pain following BPAI.

Hou Ao-Lin, Wu Jia-Jia, Xing Xiang-Xin, Huo Bei-Bei, Shen Jun, Hua Xu-Yun, Zheng Mou-Xiong, Xu Jian-Guang

2022-Jan

** PET/CT, PRoNTo, SearchlightSearchlight, brachial plexus avulsion injury, machine learning\r, multivariate pattern analysis, neuroimaging, Neuropathic pain**

General General

An annotated dataset for extracting gene-melanoma relations from scientific literature.

In Journal of biomedical semantics ; h5-index 23.0

BACKGROUND : Melanoma is one of the least common but the deadliest of skin cancers. This cancer begins when the genes of a cell suffer damage or fail, and identifying the genes involved in melanoma is crucial for understanding the melanoma tumorigenesis. Thousands of publications about human melanoma appear every year. However, while biological curation of data is costly and time-consuming, to date the application of machine learning for gene-melanoma relation extraction from text has been severely limited by the lack of annotated resources.

RESULTS : To overcome this lack of resources for melanoma, we have exploited the information of the Melanoma Gene Database (MGDB, a manually curated database of genes involved in human melanoma) to automatically build an annotated dataset of binary relations between gene and melanoma entities occurring in PubMed abstracts. The entities were automatically annotated by state-of-the-art text-mining tools. Their annotation includes both the mention text spans and normalized concept identifiers. The relations among the entities were annotated at concept- and mention-level. The concept-level annotation was produced using the information of the genes in MGDB to decide if a relation holds between a gene and melanoma concept in the whole abstract. The exploitability of this dataset was tested with both traditional machine learning, and neural network-based models like BERT. The models were then used to automatically extract gene-melanoma relations from the biomedical literature. Most of the current models use context-aware representations of the target entities to establish relations between them. To facilitate researchers in their experiments we generated a mention-level annotation in support to the concept-level annotation. The mention-level annotation was generated by automatically linking gene and melanoma mentions co-occurring within the sentences that in MGDB establish the association of the gene with melanoma.

CONCLUSIONS : This paper presents a corpus containing gene-melanoma annotated relations. Additionally, it discusses experiments which show the usefulness of such a corpus for training a system capable of mining gene-melanoma relationships from the literature. Researchers can use the corpus to develop and compare their own models, and produce results which might be integrated with existing structured knowledge databases, which in turn might facilitate medical research.

Zanoli Roberto, Lavelli Alberto, Löffler Theresa, Perez Gonzalez Nicolas Andres, Rinaldi Fabio

2022-Jan-19

Annotated Dataset, Deep Learning, Machine Learning, Melanoma, Relation Extraction

Radiology Radiology

Computerized Characterization of Spinal Structures on MRI and Clinical Significance of 3D Reconstruction of Lumbosacral Intervertebral Foramen.

In Pain physician ; h5-index 45.0

BACKGROUND : Segmentation of spinal structures is important in medical imaging analysis, which facilitates surgeons to plan a preoperative trajectory for the transforaminal approach. However, manual segmentation of spinal structures is time-consuming, and studies have not explored automatic segmentation of spinal structures at the L5/S1 level.

OBJECTIVES : This study sought to develop a new method based on a deep learning algorithm for automatic segmentation of spinal structures. The resulting algorithm may be used to rapidly generate a precise 3D lumbosacral intervertebral foramen model to assist physicians in planning an ideal trajectory in L5/S1 lumbar transforaminal radiofrequency ablation (LTRFA).

STUDY DESIGN : This was an observational study for developing a new technique on spinal structures segmentation.

STUDY SITE : The study was carried out at the department of radiology and spine surgery at our hospital.

METHODS : A total of 100 L5/S1 level data samples from 100 study patients were used in this study. Masks of vertebral bone structures (VBSs) and intervertebral discs (IVDs) for all data samples were segmented manually by a skilled surgeon and served as the "ground truth." After data preprocessing, a 3D-UNet model based on deep learning was used for automated segmentation of lumbar spine structures at L5/S1 level magnetic resonance imaging (MRI). Segmentation performances and morphometric measurement were used for 3D lumbosacral intervertebral foramen (LIVF) reconstruction  generated by either manual segmentation and automatic segmentation.

RESULTS : The 3D-UNet model showed high performance in automatic segmentation of lumbar spinal structures (VBSs and IVDs). The corresponding mean Dice similarity coefficient (DSC) of 5-fold cross-validation scores for L5 vertebrae, IVDs, S1 vertebrae, and all L5/S1 level spinal structures were 93.46 ± 2.93%, 90.39 ± 6.22%, 93.32 ± 1.51%, and 92.39 ± 2.82%, respectively. Notably, the analysis showed no associated difference in morphometric measurements between the manual and automatic segmentation at the L5/S1 level.

LIMITATIONS : Semantic segmentation of multiple spinal structures (such as VBSs, IVDs, blood vessels, muscles, and ligaments) was simultaneously not integrated into the deep-learning method in this study. In addition, large clinical experiments are needed to evaluate the clinical efficacy of the model.

CONCLUSION : The 3D-UNet model developed in this study based on deep learning can effectively and simultaneously segment VBSs and IVDs at L5/S1 level formMR images, thereby enabling rapid and accurate 3D reconstruction of LIVF models. The method can be used to segment VBSs and IVDs of spinal structures on MR images within near-human expert performance; therefore, it is reliable for reconstructing LIVF for L5/S1 LTRFA.

Liu Zheng, Su Zhihai, Wang Min, Chen Tao, Cui Zhifei, Chen Xiaojun, Li Shaolin, Feng Qianjin, Pang Shumao, Lu Hai

2022-Jan

** 3D reconstruction \r, 3D-UNet model, MRI, automatic segmentation, intervertebral discs, lumbosacral intervertebral foramen, manual segmentation, vertebral bone structures, Deep learning**

Internal Medicine Internal Medicine

Integrated Tuberculosis and COVID-19 Activities in Karachi and Tuberculosis Case Notifications.

In Tropical medicine and infectious disease

As the COVID-19 pandemic surged, lockdowns led to the cancellation of essential health services. As part of our Zero TB activities in Karachi, we adapted our approach to integrate activities for TB and COVID-19 to decrease the impact on diagnosis and linkage to care for TB treatment. We implemented the following: (1) integrated COVID-19 screening and testing within existing TB program activities, along with the use of an artificial intelligence (AI) software reader on digital chest X-rays; (2) home delivery of medication; (3) use of telehealth and mental health counseling; (4) provision of PPE; (5) burnout monitoring of health workers; and (6) patient safety and disinfectant protocol. We used programmatic data for six districts of Karachi from January 2018 to March 2021 to explore the time trends in case notifications, the impact of the COVID-19 pandemic, and service adaptations in the city. The case notifications in all six districts in Karachi were over 80% of the trend-adjusted expected notifications with three districts having over 90% of the expected case notifications. Overall, Karachi reached 90% of the expected case notifications during the COVID-19 pandemic. The collaborative efforts by the provincial TB program and private sector partners facilitated this reduced loss in case notifications.

Malik Amyn A, Hussain Hamidah, Maniar Rabia, Safdar Nauman, Mohiuddin Amal, Riaz Najam, Pasha Aneeta, Khan Salman, Kazmi Syed Saleem Hasan, Kazmi Ershad, Khowaja Saira

2022-Jan-15

COVID-19, active case finding, case notification, screening, tuberculosis

Public Health Public Health

One Health Approach: A Data-Driven Priority for Mitigating Outbreaks of Emerging and Re-Emerging Zoonotic Infectious Diseases.

In Tropical medicine and infectious disease

This paper discusses the contributions that One Health principles can make in improving global response to zoonotic infectious disease. We highlight some key benefits of taking a One Health approach to a range of complex infectious disease problems that have defied a more traditional sectoral approach, as well as public health policy and practice, where gaps in surveillance systems need to be addressed. The historical examples demonstrate the scope of One Health, partly from an Australian perspective, but also with an international flavour, and illustrate innovative approaches and outcomes with the types of collaborative partnerships that are required.

Ajuwon Busayo I, Roper Katrina, Richardson Alice, Lidbury Brett A

2021-Dec-29

One Health, global health security, infectious disease, machine learning capability, surveillance systems

General General

Two Hits of EDCs Three Generations Apart: Effects on Social Behaviors in Rats, and Analysis by Machine Learning.

In Toxics

All individuals are directly exposed to extant environmental endocrine-disrupting chemicals (EDCs), and indirectly exposed through transgenerational inheritance from our ancestors. Although direct and ancestral exposures can each lead to deficits in behaviors, their interactions are not known. Here we focused on social behaviors based on evidence of their vulnerability to direct or ancestral exposures, together with their importance in reproduction and survival of a species. Using a novel "two hits, three generations apart" experimental rat model, we investigated interactions of two classes of EDCs across six generations. PCBs (a weakly estrogenic mixture Aroclor 1221, 1 mg/kg), Vinclozolin (antiandrogenic, 1 mg/kg) or vehicle (6% DMSO in sesame oil) were administered to pregnant rat dams (F0) to directly expose the F1 generation, with subsequent breeding through paternal or maternal lines. A second EDC hit was given to F3 dams, thereby exposing the F4 generation, with breeding through the F6 generation. Approximately 1200 male and female rats from F1, F3, F4 and F6 generations were run through tests of sociability and social novelty as indices of social preference. We leveraged machine learning using DeepLabCut to analyze nuanced social behaviors such as nose touching with accuracy similar to a human scorer. Surprisingly, social behaviors were affected in ancestrally exposed but not directly exposed individuals, particularly females from a paternally exposed breeding lineage. Effects varied by EDC: Vinclozolin affected aspects of behavior in the F3 generation while PCBs affected both the F3 and F6 generations. Taken together, our data suggest that specific aspects of behavior are particularly vulnerable to heritable ancestral exposure of EDC contamination, that there are sex differences, and that lineage is a key factor in transgenerational outcomes.

Gillette Ross, Dias Michelle, Reilly Michael P, Thompson Lindsay M, Castillo Norma J, Vasquez Erin L, Crews David, Gore Andrea C

2022-Jan-11

Aroclor 1221 (A1221), PCBs, endocrine-disrupting chemicals (EDC), epigenetic, sex differences, social behavior, transgenerational, vinclozolin

General General

A Narrative Review for a Machine Learning Application in Sports: An Example Based on Injury Forecasting in Soccer.

In Sports (Basel, Switzerland)

In the last decade, the number of studies about machine learning algorithms applied to sports, e.g., injury forecasting and athlete performance prediction, have rapidly increased. Due to the number of works and experiments already present in the state-of-the-art regarding machine-learning techniques in sport science, the aim of this narrative review is to provide a guideline describing a correct approach for training, validating, and testing machine learning models to predict events in sports science. The main contribution of this narrative review is to highlight any possible strengths and limitations during all the stages of model development, i.e., training, validation, testing, and interpretation, in order to limit possible errors that could induce misleading results. In particular, this paper shows an example about injury forecaster that provides a description of all the features that could be used to predict injuries, all the possible pre-processing approaches for time series analysis, how to correctly split the dataset to train and test the predictive models, and the importance to explain the decision-making approach of the white and black box models.

Rossi Alessio, Pappalardo Luca, Cintia Paolo

2021-Dec-24

artificial intelligence, soccer, sport science, training and testing

General General

Model predictive control based on artificial intelligence and EPA-SWMM model to reduce CSOs impacts in sewer systems.

In Water science and technology : a journal of the International Association on Water Pollution Research

Urbanization and an increase in precipitation intensities due to climate change, in addition to limited urban drainage systems (UDS) capacity, are the main causes of combined sewer overflows (CSOs) that cause serious water pollution problems in many cities around the world. Model predictive control (MPC) systems offer a new approach to mitigate the impact of CSOs by generating optimal temporally and spatially varied dynamic control strategies of sewer system actuators. This paper presents a novel MPC based on neural networks for predicting flows, a stormwater management model (SWMM) for flow conveyance, and a genetic algorithm for optimizing the operation of sewer systems and defining the best control strategies. The proposed model was tested on the sewer system of the city of Casablanca in Morocco. The results have shown the efficiency of the developed MPC to reduce CSOs while considering short optimization time thanks to parallel computing.

El Ghazouli Khalid, El Khatabi Jamal, Soulhi Aziz, Shahrour Isam

2022-Jan

Public Health Public Health

Identifying the underlying factors associated with antidepressant drug discontinuation: content analysis of patients' drug reviews.

In Informatics for health & social care

The rate of antidepressant prescriptions is globally increasing. A large portion of patients stop their medications, which could lead to many side effects including relapse, and anxiety. The aim of this was to develop a drug-continuity prediction model and identify the factors associated with drug-continuity using online patient forums. We retrieved 982 antidepressant drug reviews from the online patient's forum AskaPatient.com. We followed the Analytical Framework Method to extract structured data from unstructured data. Using the structured data, we examined the factors associated with antidepressant discontinuity and developed a predictive model using multiple machine learning techniques. We tested multiple machine learning techniques which resulted in different performances ranging from accuracy of 65% to 82%. We found that Random Forest algorithm provides the highest prediction method with 82% Accuracy, 78% Precision, 88.03% Recall, and 84.2% F1-Score. The factors associated with drug discontinuity the most were: withdrawal symptoms, effectiveness-ineffectiveness, perceived-distress-adverse drug reaction, rating, and perceiveddistress related to withdrawal symptoms. Although the nature of data available at online forums differ from data collected through surveys, we found that online patients forum can be a valuable source of data for drug continuity prediction and understanding patients experience. The factors identified through our techniques were consistent with the findings of prior studies that used surveys.

Alarifi Mohammad, Jabour Abdulrahman, Foy Doreen M, Zolnoori Maryam

2022-Jan-20

Antidepressive agents, attitude, chronic disease, data mining, depression, internet, medication adherence, patient-centered care, perception, social media

Public Health Public Health

Forecasting the COVID-19 vaccine uptake rate: an infodemiological study in the US.

In Human vaccines & immunotherapeutics ; h5-index 43.0

A year following the initial COVID-19 outbreak in China, many countries have approved emergency vaccines. Public-health practitioners and policymakers must understand the predicted populational willingness for vaccines and implement relevant stimulation measures. This study developed a framework for predicting vaccination uptake rate based on traditional clinical data - involving an autoregressive model with autoregressive integrated moving average (ARIMA) - and innovative web search queries - involving a linear regression with ordinary least squares/least absolute shrinkage and selection operator, and machine-learning with boost and random forest. For accuracy, we implemented a stacking regression for the clinical data and web search queries. The stacked regression of ARIMA (1,0,8) for clinical data and boost with support vector machine for web data formed the best model for forecasting vaccination speed in the US. The stacked regression provided a more accurate forecast. These results can help governments and policymakers predict vaccine demand and finance relevant programs.

Zhou Xingzuo, Li Yiang

2022-Jan-20

Public health, forecast, infodemiology, machine-learning, vaccine

General General

GRNUlar: A Deep Learning Framework for Recovering Single-Cell Gene Regulatory Networks.

In Journal of computational biology : a journal of computational molecular cell biology

We propose GRNUlar, a novel deep learning framework for supervised learning of gene regulatory networks (GRNs) from single-cell RNA-Sequencing (scRNA-Seq) data. Our framework incorporates two intertwined models. First, we leverage the expressive ability of neural networks to capture complex dependencies between transcription factors and the corresponding genes they regulate, by developing a multitask learning framework. Second, to capture sparsity of GRNs observed in the real world, we design an unrolled algorithm technique for our framework. Our deep architecture requires supervision for training, for which we repurpose existing synthetic data simulators that generate scRNA-Seq data guided by an underlying GRN. Experimental results demonstrate that GRNUlar outperforms state-of-the-art methods on both synthetic and real data sets. Our study also demonstrates the novel and successful use of expression data simulators for supervised learning of GRN inference.

Shrivastava Harsh, Zhang Xiuwei, Song Le, Aluru Srinivas

2022-Jan

deep learning, gene regulatory networks, single-cell RNA-Seq, unrolled algorithms

General General

Electronic Structure-Based Descriptors for Oxide Properties and Functions.

In Accounts of chemical research ; h5-index 162.0

ConspectusThe transition from fossil fuels to renewable energy requires the development of efficient and cost-effective energy storage technologies. A promising way forward is to harness the energy of intermittent renewable sources, such as solar and wind, to perform (electro)catalytic reactions to generate fuels, thus storing energy in the form of chemical bonds. However, current catalysts rely on the use of expensive, rare, or geographically localized elements, such as platinum. Widespread adoption of new (electro)catalytic technologies hinges on the discovery and development of materials containing earth-abundant elements, which can efficiently catalyze an array of (electro)chemical reactions.In the context of catalysis, descriptors provide correlations between fundamental physical properties, such as the electronic structure, and the resulting catalytic activity. The use of easily accessible descriptors has proven to be a powerful method to advance and accelerate discovery and design of new catalyst materials. The position of the oxygen electronic 2p band center has been proposed to capture the basic physical properties of oxides, including oxygen vacancy formation energy, diffusion barrier of oxygen ions, and work function. Moreover, the adsorption strength of relevant reaction intermediates at the surface of oxides can be strongly correlated with the energy of the oxygen 2p states, which affects the catalytic activity of reactions, such as oxygen electrocatalysis, and oxidative dehydrogenation of organic molecules. Such descriptors for catalytic activity can be used to predict the activity of new catalysts and understand trends and behavior among different catalysts.In this Account, we discuss how the energy of the oxygen 2p states can be used as a descriptor for oxide bulk and surface chemical properties. We show how the oxide redox properties vary linearly with the position of the oxygen 2p band center with respect to the Fermi level, and we discuss how this descriptor can be expanded across different materials and structural families, including possible generalizations to compounds outside oxides. We highlight the power of the oxygen 2p band center to predict the catalytic activity of oxides. We conclude with an outlook examining under which conditions this descriptor can be applied to predict oxide properties and possible opportunities for further refining and accelerating property predictions of oxides by leveraging material databases and machine learning.

Giordano Livia, Akkiraju Karthik, Jacobs Ryan, Vivona Daniele, Morgan Dane, Shao-Horn Yang

2022-Jan-20

General General

Characterizing memory T helper cells in patients with psoriasis, subclinical, or early psoriatic arthritis using a machine learning algorithm.

In Arthritis research & therapy ; h5-index 60.0

BACKGROUND : Psoriasis patients developing psoriatic arthritis (PsA) are thought to go through different phases. Understanding the underlying events in these phases is crucial to diagnose PsA early. Here, we have characterized the circulating memory T helper (Th) cells in psoriasis patients with or without arthralgia, psoriasis patients who developed PsA during follow-up (subclinical PsA), early PsA patients and healthy controls to elucidate their role in PsA development.

METHODS : We used peripheral blood mononuclear cells of sex and age-matched psoriasis patients included in Rotterdam Joint Skin study (n=22), early PsA patients included in Dutch South West Early Psoriatic Arthritis Cohort (DEPAR) (n=23) and healthy controls (HC; n=17). We profiled memory Th cell subsets with flow cytometry and used the machine learning algorithm FlowSOM to interpret the data.

RESULTS : Three of the 22 psoriasis patients developed PsA during 2-year follow-up. FlowSOM identified 12 clusters of memory Th cells, including Th1, Th2, Th17/22, and Th17.1 cells. All psoriasis and PsA patients had higher numbers of Th17/22 than healthy controls. Psoriasis patients without arthralgia had lower numbers of CCR6-CCR4+CXCR3+ memory Th cells and higher numbers of CCR6+CCR4-CXCR3-memory Th cells compared to HC. PsA patients had higher numbers of Th2 cells and CCR6+CCR4+CXCR3- cells, but lower numbers of CCR6+CCR4+CXCR3+ memory Th cells compared to HC. The number of CCR6+ Th17.1 cells negatively correlated with tender joint counts and the number of CCR6+ Th17 cells positively correlated with skin disease severity.

CONCLUSIONS : Unsupervised clustering analysis revealed differences in circulating memory Th cells between psoriasis and PsA patients compared to HC; however, no specific subset was identified characterizing subclinical PsA patients.

den Braanker Hannah, Razawy Wida, Wervers Kim, Mus Anne-Marie C, Davelaar Nadine, Kok Marc R, Lubberts Erik

2022-Jan-19

Machine learning algorithms, Psoriatic arthritis, T cells

General General

Large-scale surgical workflow segmentation for laparoscopic sacrocolpopexy.

In International journal of computer assisted radiology and surgery

PURPOSE : Laparoscopic sacrocolpopexy is the gold standard procedure for the management of vaginal vault prolapse. Studying surgical skills and different approaches to this procedure requires an analysis at the level of each of its individual phases, thus motivating investigation of automated surgical workflow for expediting this research. Phase durations in this procedure are significantly larger and more variable than commonly available benchmarks such as Cholec80, and we assess these differences.

METHODOLOGY : We introduce sequence-to-sequence (seq2seq) models for coarse-level phase segmentation in order to deal with highly variable phase durations in Sacrocolpopexy. Multiple architectures (LSTM and transformer), configurations (time-shifted, time-synchronous), and training strategies are tested with this novel framework to explore its flexibility.

RESULTS : We perform 7-fold cross-validation on a dataset with 14 complete videos of sacrocolpopexy. We perform both a frame-based (accuracy, F1-score) and an event-based (Ward metric) evaluation of our algorithms and show that different architectures present a trade-off between higher number of accurate frames (LSTM, Mode average) or more consistent ordering of phase transitions (Transformer). We compare the implementations on the widely used Cholec80 dataset and verify that relative performances are different to those in Sacrocolpopexy.

CONCLUSIONS : We show that workflow segmentation of Sacrocolpopexy videos has specific challenges that are different to the widely used benchmark Cholec80 and require dedicated approaches to deal with the significantly larger phase durations. We demonstrate the feasibility of seq2seq models in Sacrocolpopexy, a broad framework that can be further explored with new configurations. We show that an event-based evaluation metric is useful to evaluate workflow segmentation algorithms and provides complementary insight to the more commonly used metrics such as accuracy or F1-score.

Zhang Yitong, Bano Sophia, Page Ann-Sophie, Deprest Jan, Stoyanov Danail, Vasconcelos Francisco

2022-Jan-20

Laparoscopic sacrocolpopexy, Long short-term memory networks, Machine learning, Surgical workflow segmentation, Transformer networks

General General

The validation of orthodontic artificial intelligence systems that perform orthodontic diagnoses and treatment planning.

In European journal of orthodontics

AIM : This study was aimed to evaluate two artificial intelligence (AI) systems that created a prioritized problem list and treatment plan, and examine whether the performance of the aforementioned systems was equivalent to orthodontists.

MATERIALS AND METHODS : A total of 967 consecutive cases [800: training; 67: validation; 100: evaluation (40: randomly selected for the clinical evaluation)] were used. We used a stored document that describes (1) the patient's clinical information, (2) the prioritized list, and (3) a treatment strategy without digital tooth movement. Sentences of (1) were vectorized according to the bag of words method (V); sentences of (2) and (3) were relabelled with 423 and 330 labels, respectively. AI systems that output labels for the prioritized list (subtask 1) and treatment planning (subtask 2) based on the vectors V were developed using a support vector machine and self-attention network, respectively, while the system was trained to improve precision and recall. Clinical evaluations were conducted by four orthodontists (no faculty or residents; peer group) in two sessions: in the first session, peer group and the developed AI systems created problem lists and treatment plans; in the second session, two of the peer group (not AI) evaluated these lists and plans, including the lists and plans of the AIs, by scoring them using 4-point scales [unacceptable (1) to ideal (4)]. Scores were compared among the system and peer group (Wilcoxon signed-rank test, P < 0.05).

RESULTS : The precision after system training was 65% and 48% for subtasks 1 and 2 respectively, with recall of 55% and 48%, respectively. The clinical evaluation of the AI system for subtask 1 showed a mid-rank. For subtask 2, the AI system had a significantly lower score than the three panels but the same rank with one panel.

CONCLUSIONS : Two AI systems that output a prioritized problem list and create a treatment plan were developed. The clinical system ability of the former system showed a mid-rank in the peer group, and the latter system was almost equivalent to the worst orthodontist.

Shimizu Yuujin, Tanikawa Chihiro, Kajiwara Tomoyuki, Nagahara Hajime, Yamashiro Takashi

2022-Jan-20

Radiology Radiology

Early Readout on Overall Survival of Patients With Melanoma Treated With Immunotherapy Using a Novel Imaging Analysis.

In JAMA oncology ; h5-index 85.0

Importance : Existing criteria to estimate the benefit of a therapy in patients with cancer rely almost exclusively on tumor size, an approach that was not designed to estimate survival benefit and is challenged by the unique properties of immunotherapy. More accurate prediction of survival by treatment could enhance treatment decisions.

Objective : To validate, using radiomics and machine learning, the performance of a signature of quantitative computed tomography (CT) imaging features for estimating overall survival (OS) in patients with advanced melanoma treated with immunotherapy.

Design, Setting, and Participants : This prognostic study used radiomics and machine learning to retrospectively analyze CT images obtained at baseline and first follow-up and their associated clinical metadata. Data were prospectively collected in the KEYNOTE-002 (Study of Pembrolizumab [MK-3475] Versus Chemotherapy in Participants With Advanced Melanoma; 2017 analysis) and KEYNOTE-006 (Study to Evaluate the Safety and Efficacy of Two Different Dosing Schedules of Pembrolizumab [MK-3475] Compared to Ipilimumab in Participants With Advanced Melanoma; 2016 analysis) multicenter clinical trials. Participants included 575 patients with a diagnosis of advanced melanoma who were randomly assigned to training and validation sets. Data for the present study were collected from November 20, 2012, to June 3, 2019, and analyzed from July 1, 2019, to September 15, 2021.

Interventions : KEYNOTE-002 featured trial groups testing intravenous pembrolizumab, 2 mg/kg or 10 mg/kg every 2 or every 3 weeks based on randomization, or investigator-choice chemotherapy; KEYNOTE-006 featured trial groups testing intravenous ipilimumab, 3 mg/kg every 3 weeks and intravenous pembrolizumab, 10 mg/kg every 2 or 3 weeks based on randomization.

Main Outcomes and Measures : The performance of the signature CT imaging features for estimating OS at the month 6 posttreatment landmark in patients who received pembrolizumab was measured using an area under the time-dependent receiver operating characteristics curve (AUC).

Results : A random forest model combined 25 imaging features extracted from tumors segmented on CT images to identify the combination (signature) that best estimated OS with pembrolizumab in 575 patients. The signature combined 4 imaging features, 2 related to tumor size and 2 reflecting changes in tumor imaging phenotype. In the validation set (287 patients treated with pembrolizumab), the signature reached an AUC for estimation of OS status of 0.92 (95% CI, 0.89-0.95). The standard method, Response Evaluation Criteria in Solid Tumors 1.1, achieved an AUC of 0.80 (95% CI, 0.75-0.84) and classified tumor outcomes as partial or complete response (93 of 287 [32.4%]), stable disease (90 of 287 [31.3%]), or progressive disease (104 of 287 [36.2%]).

Conclusions and Relevance : The findings of this prognostic study suggest that the radiomic signature discerned from conventional CT images at baseline and on first follow-up may be used in clinical settings to provide an accurate early readout of future OS probability in patients with melanoma treated with single-agent programmed cell death 1 blockade.

Dercle Laurent, Zhao Binsheng, Gönen Mithat, Moskowitz Chaya S, Firas Ahmed, Beylergil Volkan, Connors Dana E, Yang Hao, Lu Lin, Fojo Tito, Carvajal Richard, Karovic Sanja, Maitland Michael L, Goldmacher Gregory V, Oxnard Geoffrey R, Postow Michael A, Schwartz Lawrence H

2022-Jan-20

Cardiology Cardiology

The Use of Machine Learning Algorithms in the Evaluation of the Effectiveness of Resynchronization Therapy.

In Journal of cardiovascular development and disease

BACKGROUND : Cardiovascular disease remains the leading cause of death in the European Union and worldwide. Constant improvement in cardiac care is leading to an increased number of patients with heart failure, which is a challenging condition in terms of clinical management. Cardiac resynchronization therapy is becoming more popular because of its grounded position in guidelines and clinical practice. However, some patients do not respond to treatment as expected. One way of assessing cardiac resynchronization therapy is with ECG analysis. Artificial intelligence is increasing in terms of everyday usability due to the possibility of everyday workflow improvement and, as a result, shortens the time required for diagnosis. A special area of artificial intelligence is machine learning. AI algorithms learn on their own based on implemented data. The aim of this study was to evaluate using artificial intelligence algorithms for detecting inadequate resynchronization therapy.

METHODS : A total of 1241 ECG tracings were collected from 547 cardiac department patients. All ECG signals were analyzed by three independent cardiologists. Every signal event (QRS-complex) and rhythm was manually classified by the medical team and fully reviewed by additional cardiologists. The results were divided into two parts: 80% of the results were used to train the algorithm, and 20% were used for the test (Cardiomatics, Cracow, Poland).

RESULTS : The required level of detection sensitivity of effective cardiac resynchronization therapy stimulation was achieved: 99.2% with a precision of 92.4%.

CONCLUSIONS : Artificial intelligence algorithms can be a useful tool in assessing the effectiveness of resynchronization therapy.

Krzowski Bartosz, Rokicki Jakub, Główczyńska Renata, Fajkis-Zajączkowska Nikola, Barczewska Katarzyna, Mąsior Mariusz, Grabowski Marcin, Balsam Paweł

2022-Jan-10

artificial intelligence, cardiac resynchronization therapy, heart failure

Surgery Surgery

Estimated Renal Metabolomics at Reperfusion Predicts One-Year Kidney Graft Function.

In Metabolites

Renal transplantation is the gold-standard procedure for end-stage renal disease patients, improving quality of life and life expectancy. Despite continuous advancement in the management of post-transplant complications, progress is still needed to increase the graft lifespan. Early identification of patients at risk of rapid graft failure is critical to optimize their management and slow the progression of the disease. In 42 kidney grafts undergoing protocol biopsies at reperfusion, we estimated the renal metabolome from RNAseq data. The estimated metabolites' abundance was further used to predict the renal function within the first year of transplantation through a random forest machine learning algorithm. Using repeated K-fold cross-validation we first built and then tuned our model on a training dataset. The optimal model accurately predicted the one-year eGFR, with an out-of-bag root mean square root error (RMSE) that was 11.8 ± 7.2 mL/min/1.73 m2. The performance was similar in the test dataset, with a RMSE of 12.2 ± 3.2 mL/min/1.73 m2. This model outperformed classic statistical models. Reperfusion renal metabolome may be used to predict renal function one year after allograft kidney recipients.

Verissimo Thomas, Faivre Anna, Sgardello Sebastian, Naesens Maarten, de Seigneux Sophie, Criton Gilles, Legouis David

2022-Jan-10

AKI (acute kidney injury), machine learning, metabolomics, renal transplantation

Public Health Public Health

Machine Learning Applied to Omics Datasets Predicts Mortality in Patients with Alcoholic Hepatitis.

In Metabolites

Alcoholic hepatitis is a major health care burden in the United States due to significant morbidity and mortality. Early identification of patients with alcoholic hepatitis at greatest risk of death is extremely important for proper treatments and interventions to be instituted. In this study, we used gradient boosting, random forest, support vector machine and logistic regression analysis of laboratory parameters, fecal bacterial microbiota, fecal mycobiota, fecal virome, serum metabolome and serum lipidome to predict mortality in patients with alcoholic hepatitis. Gradient boosting achieved the highest AUC of 0.87 for both 30-day mortality prediction using the bacteria and metabolic pathways dataset and 90-day mortality prediction using the fungi dataset, which showed better performance than the currently used model for end-stage liver disease (MELD) score.

Gao Bei, Wu Tsung-Chin, Lang Sonja, Jiang Lu, Duan Yi, Fouts Derrick E, Zhang Xinlian, Tu Xin-Ming, Schnabl Bernd

2022-Jan-05

machine learning, metabolomics, microbiota, mycobiome, virome

General General

Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data.

In Metabolites

Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.

Passi Anurag, Tibocha-Bonilla Juan D, Kumar Manish, Tec-Campos Diego, Zengler Karsten, Zuniga Cristal

2021-Dec-24

ME-models, big data, computational tools, flux balance analysis, genome-scale metabolic models, machine learning, phenotypes, reconstruction

General General

Metabolomics Defines Complex Patterns of Dyslipidaemia in Juvenile-SLE Patients Associated with Inflammation and Potential Cardiovascular Disease Risk.

In Metabolites

Cardiovascular disease (CVD) is a leading cause of mortality in patients with juvenile-onset systemic lupus erythematosus (JSLE) associated with atherosclerosis. The interplay between dyslipidaemia and inflammation-mechanisms that drive atherosclerosis-were investigated retrospectively in adolescent JSLE patients using lipoprotein-based serum metabolomics in patients with active and inactive disease, compared to healthy controls (HCs). Data was analysed using machine learning, logistic regression, and linear regression. Dyslipidaemia in JSLE patients was characterised by lower levels of small atheroprotective high-density lipoprotein subsets compared to HCs. These changes were exacerbated by active disease and additionally associated with significantly higher atherogenic very-low-density lipoproteins (VLDL) compared to patients with low disease activity. Atherogenic lipoprotein subset expression correlated positively with clinical and serological markers of JSLE disease activity/inflammation and was associated with disturbed liver function, and elevated expression of T-cell and B-cell lipid rafts (cell signalling platforms mediating immune cell activation). Finally, exposing VLDL/LDL from patients with active disease to HC lymphocytes induced a significant increase in lymphocyte lipid raft activation compared to VLDL/LDL from inactive patients. Thus, metabolomic analysis identified complex patterns of atherogenic dyslipidaemia in JSLE patients associated with inflammation. This could inform lipid-targeted therapies in JSLE to improve cardiovascular outcomes.

Robinson George A, Peng Junjie, Pineda-Torra Ines, Ciurtin Coziana, Jury Elizabeth C

2021-Dec-21

B-cells, T-cells, atherosclerosis, cardiovascular disease, disease activity, juvenile-onset SLE, lipid rafts, lipoproteins, metabolomics

General General

Role of Autophagy in Haematococcus lacustris Cell Growth under Salinity.

In Plants (Basel, Switzerland)

The microalga Haematococcus lacustris (formerly H. pluvialis) is able to accumulate high amounts of the carotenoid astaxanthin in the course of adaptation to stresses like salinity. Technologies aimed at production of natural astaxanthin for commercial purposes often involve salinity stress; however, after a switch to stressful conditions, H.&nbsp;lacustris experiences massive cell death which negatively influences astaxanthin yield. This study addressed the possibility to improve cell survival in H.&nbsp;lacustris subjected to salinity via manipulation of the levels of autophagy using AZD8055, a known inhibitor of TOR kinase previously shown to accelerate autophagy in several microalgae. Addition of NaCl in concentrations of 0.2% or 0.8% to the growth medium induced formation of autophagosomes in H. lacustris, while simultaneous addition of AZD8055 up to a final concentration of 0.2 µM further stimulated this process. AZD8055 significantly improved the yield of H. lacustris cells after 5 days of exposure to 0.2% NaCl. Strikingly, this occurred by acceleration of cell growth, and not by acceleration of aplanospore formation. The level of astaxanthin synthesis was not affected by AZD8055. However, cytological data suggested a role of autophagosomes, lysosomes and Golgi cisternae in cell remodeling during high salt stress.

Zharova Daria A, Ivanova Alexandra N, Drozdova Irina V, Belyaeva Alla I, Boldina Olga N, Voitsekhovskaja Olga V, Tyutereva Elena V

2022-Jan-12

Haematococcus, astaxanthin, autophagy, cell remodeling, potassium, salinity

General General

How Do Roots Interact with Layered Soils?

In Journal of imaging

Vegetation alters soil fabric by providing biological reinforcement and enhancing the overall mechanical behaviour of slopes, thereby controlling shallow mass movement. To predict the behaviour of vegetated slopes, parameters representing the root system structure, such as root distribution, length, orientation and diameter, should be considered in slope stability models. This study quantifies the relationship between soil physical characteristics and root growth, giving special emphasis on (1) how roots influence the physical architecture of the surrounding soil structure and (2) how soil structure influences the root growth. A systematic experimental study is carried out using high-resolution X-ray micro-computed tomography (µCT) to observe the root behaviour in layered soil. In total, 2 samples are scanned over 15 days, enabling the acquisition of 10 sets of images. A machine learning algorithm for image segmentation is trained to act at 3 different training percentages, resulting in the processing of 30 sets of images, with the outcomes prompting a discussion on the size of the training data set. An automated in-house image processing algorithm is employed to quantify the void ratio and root volume ratio. This script enables post processing and image analysis of all 30 cases within few hours. This work investigates the effect of stratigraphy on root growth, along with the effect of image-segmentation parameters on soil constitutive properties.

Kemp Nina, Angelidakis Vasileios, Luli Saimir, Nadimi Sadegh

2022-Jan-05

computed tomography, deep learning, micromechanics, particle-scale behaviour, soil fabric

General General

Cross-condition and cross-platform remaining useful life estimation via adversarial-based domain adaptation.

In Scientific reports ; h5-index 158.0

Supervised machine learning is a traditionally remaining useful life (RUL) estimation tool, which requires a lot of prior knowledge. For the situation lacking labeled data, supervised methods are invalid for the issue of domain shift in data distribution. In this paper, a adversarial-based domain adaptation (ADA) architecture with convolution neural networks (CNN) for RUL estimation of bearings under different conditions and platforms, referred to as ADACNN, is proposed. Specifically, ADACNN is trained in source labeled data and fine-tunes to similar target unlabeled data via an adversarial training and parameters shared mechanism. Besides a feature extractor and source domain regressive predictor, ADACNN also includes a domain classifier that tries to guide feature extractor find some domain-invariant features, which differents with traditional methods and belongs to a unsupervised learning in target domain, which has potential application value and far-reaching significance in academia. In addition, according to different first predictive time (FPT) detection mechanisms, we also explores the impact of different FPT detection mechanisms on RUL estimation performance. Finally, according to extensive experiments, the results of RUL estimation of bearing in cross-condition and cross-platform prove that ADACNN architecture has satisfactory generalization performance and great practical value in industry.

Zhao Dongdong, Liu Feng

2022-Jan-18

Surgery Surgery

Machine Learning Can be Used to Predict Function but Not Pain After Surgery for Thumb Carpometacarpal Osteoarthritis.

In Clinical orthopaedics and related research ; h5-index 71.0

BACKGROUND : Surgery for thumb carpometacarpal osteoarthritis is offered to patients who do not benefit from nonoperative treatment. Although surgery is generally successful in reducing symptoms, not all patients benefit. Predicting clinical improvement after surgery could provide decision support and enhance preoperative patient selection.

QUESTIONS/PURPOSES : This study aimed to develop and validate prediction models for clinically important improvement in (1) pain and (2) hand function 12 months after surgery for thumb carpometacarpal osteoarthritis.

METHODS : Between November 2011 and June 2020, 2653 patients were surgically treated for thumb carpometacarpal osteoarthritis. Patient-reported outcome measures were used to preoperatively assess pain, hand function, and satisfaction with hand function, as well as the general mental health of patients and mindset toward their condition. Patient characteristics, medical history, patient-reported symptom severity, and patient-reported mindset were considered as possible predictors. Patients who had incomplete Michigan Hand outcomes Questionnaires at baseline or 12 months postsurgery were excluded, as these scores were used to determine clinical improvement. The Michigan Hand outcomes Questionnaire provides subscores for pain and hand function. Scores range from 0 to 100, with higher scores indicating less pain and better hand function. An improvement of at least the minimum clinically important difference (MCID) of 14.4 for the pain score and 11.7 for the function score were considered "clinically relevant." These values were derived from previous reports that provided triangulated estimates of two anchor-based and one distribution-based MCID. Data collection resulted in a dataset of 1489 patients for the pain model and 1469 patients for the hand function model. The data were split into training (60%), validation (20%), and test (20%) dataset. The training dataset was used to select the predictive variables and to train our models. The performance of all models was evaluated in the validation dataset, after which one model was selected for further evaluation. Performance of this final model was evaluated on the test dataset. We trained the models using logistic regression, random forest, and gradient boosting machines and compared their performance. We chose these algorithms because of their relative simplicity, which makes them easier to implement and interpret. Model performance was assessed using discriminative ability and qualitative visual inspection of calibration curves. Discrimination was measured using area under the curve (AUC) and is a measure of how well the model can differentiate between the outcomes (improvement or no improvement), with an AUC of 0.5 being equal to chance. Calibration is a measure of the agreement between the predicted probabilities and the observed frequencies and was assessed by visual inspection of calibration curves. We selected the model with the most promising performance for clinical implementation (that is, good model performance and a low number of predictors) for further evaluation in the test dataset.

RESULTS : For pain, the random forest model showed the most promising results based on discrimination, calibration, and number of predictors in the validation dataset. In the test dataset, this pain model had a poor AUC (0.59) and poor calibration. For function, the gradient boosting machine showed the most promising results in the validation dataset. This model had a good AUC (0.74) and good calibration in the test dataset. The baseline Michigan Hand outcomes Questionnaire hand function score was the only predictor in the model. For the hand function model, we made a web application that can be accessed via https://analyse.equipezorgbedrijven.nl/shiny/cmc1-prediction-model-Eng/.

CONCLUSION : We developed a promising model that may allow clinicians to predict the chance of functional improvement in an individual patient undergoing surgery for thumb carpometacarpal osteoarthritis, which would thereby help in the decision-making process. However, caution is warranted because our model has not been externally validated. Unfortunately, the performance of the prediction model for pain is insufficient for application in clinical practice.

LEVEL OF EVIDENCE : Level III, therapeutic study.

Loos Nina L, Hoogendam Lisa, Souer J Sebastiaan, Slijper Harm P, Andrinopoulou Eleni-Rosalina, Coppieters Michel W, Selles Ruud W

2022-01-18

Ophthalmology Ophthalmology

Retinal age gap as a predictive biomarker for mortality risk.

In The British journal of ophthalmology

AIM : To develop a deep learning (DL) model that predicts age from fundus images (retinal age) and to investigate the association between retinal age gap (retinal age predicted by DL model minus chronological age) and mortality risk.

METHODS : A total of 80 169 fundus images taken from 46 969 participants in the UK Biobank with reasonable quality were included in this study. Of these, 19 200 fundus images from 11 052 participants without prior medical history at the baseline examination were used to train and validate the DL model for age prediction using fivefold cross-validation. A total of 35 913 of the remaining 35 917 participants had available mortality data and were used to investigate the association between retinal age gap and mortality.

RESULTS : The DL model achieved a strong correlation of 0.81 (p<0·001) between retinal age and chronological age, and an overall mean absolute error of 3.55 years. Cox regression models showed that each 1 year increase in the retinal age gap was associated with a 2% increase in risk of all-cause mortality (hazard ratio (HR)=1.02, 95% CI 1.00 to 1.03, p=0.020) and a 3% increase in risk of cause-specific mortality attributable to non-cardiovascular and non-cancer disease (HR=1.03, 95% CI 1.00 to 1.05, p=0.041) after multivariable adjustments. No significant association was identified between retinal age gap and cardiovascular- or cancer-related mortality.

CONCLUSIONS : Our findings indicate that retinal age gap might be a potential biomarker of ageing that is closely related to risk of mortality, implying the potential of retinal image as a screening tool for risk stratification and delivery of tailored interventions.

Zhu Zhuoting, Shi Danli, Guankai Peng, Tan Zachary, Shang Xianwen, Hu Wenyi, Liao Huan, Zhang Xueli, Huang Yu, Yu Honghua, Meng Wei, Wang Wei, Ge Zongyuan, Yang Xiaohong, He Mingguang

2022-Jan-18

telemedicine

General General

External Validation of Postpartum Hemorrhage Prediction Models Using Electronic Health Record Data.

In American journal of perinatology ; h5-index 32.0

OBJECTIVE : A recent study leveraging machine learning methods found that postpartum hemorrhage (PPH) can be predicted accurately at the time of labor admission in the U.S. Consortium for Safe Labor (CSL) dataset, with a C-statistic as high as 0.93. These CSL models were developed in older data (2002-2008) and used an estimated blood loss (EBL) of ≥1000 mL to define PPH. We sought to externally validate these models using a more recent cohort of births where blood loss was measured using quantitative blood loss (QBL) methods.

STUDY DESIGN : Using data from 5,261 deliveries between February 1, 2019 to May 11, 2020 at a single tertiary hospital, we mapped our electronic health record (EHR) data to the 55 predictors described in previously published CSL models. PPH was defined as QBL ≥1000 mL within 24 hours after delivery. Model discrimination and calibration of the four CSL models were measured using our cohort. In a secondary analysis, we fit new models in our study cohort using the same predictors and algorithms as the original CSL models.

RESULTS : The original study cohort had a substantially lower rate of PPH, 4.8% (7,279/228,438) vs. 25% (1,321/5,261), possibly due to differences in measurement. The CSL models had lower discrimination in our study cohort, with a C-statistic as high as 0.57 (logistic regression). Models refit in our study cohort achieved better discrimination, with a C-statistic as high as 0.64 (random forrest). Calibration improved in the refit models as compared to the original models.

CONCLUSION : The CSL models' accuracy was lower in a contemporary EHR where PPH is assessed using QBL. As institutions continue to adopt QBL methods, further data are needed to understand the differences between EBL and QBL to enable accurate prediction of PPH.

Meyer Sean R, Carver Alissa, Joo Hyeon, Venkatesh Kartik Kailas, Jelovsek J Eric, Klumpner Thomas T, Singh Karandeep

2022-Jan-19

Pathology Pathology

Evolved explainable classifications for lymph node metastases.

In Neural networks : the official journal of the International Neural Network Society

A novel evolutionary approach for Explainable Artificial Intelligence is presented: the "Evolved Explanations" model (EvEx). This methodology combines Local Interpretable Model Agnostic Explanations (LIME) with Multi-Objective Genetic Algorithms to allow for automated segmentation parameter tuning in image classification tasks. In this case, the dataset studied is Patch-Camelyon, comprised of patches from pathology whole slide images. A publicly available Convolutional Neural Network (CNN) was trained on this dataset to provide a binary classification for presence/absence of lymph node metastatic tissue. In turn, the classifications are explained by means of evolving segmentations, seeking to optimize three evaluation goals simultaneously. The final explanation is computed as the mean of all explanations generated by Pareto front individuals, evolved by the developed genetic algorithm. To enhance reproducibility and traceability of the explanations, each of them was generated from several different seeds, randomly chosen. The observed results show remarkable agreement between different seeds. Despite the stochastic nature of LIME explanations, regions of high explanation weights proved to have good agreement in the heat maps, as computed by pixel-wise relative standard deviations. The found heat maps coincide with expert medical segmentations, which demonstrates that this methodology can find high quality explanations (according to the evaluation metrics), with the novel advantage of automated parameter fine tuning. These results give additional insight into the inner workings of neural network black box decision making for medical data.

Palatnik de Sousa Iam, Vellasco Marley M B R, Costa da Silva Eduardo

2021-Dec-31

Artificial intelligence, Convolutional Neural Networks, Explainable AI, Multi-objective genetic algorithms

Radiology Radiology

Detection of metallic objects on digital radiographs with convolutional neural networks: A MRI screening tool.

In Radiography (London, England : 1995)

INTRODUCTION : Screening for metallic implants and foreign bodies before magnetic resonance imaging (MRI) examinations, are crucial for patient safety. History of health are supplied by the patient, a family member, screening of electronic health records or the picture and archive systems (PACS). PACS securely store and transmits digital radiographs (DR) and related reports with patient information. Convolutional neural networks (CNN) can be used to detect metallic objects in DRs stored in PACS. This study evaluates the accuracy of CNNs in the detection of metallic objects on DRs as an MRI screening tool.

METHODS : The musculoskeletal radiographs (MURA) dataset consisting of 14.863 upper extremity studies were stratified into datasets with and without metal. For each anatomical region: Elbow, finger, hand, humerus, forearm, shoulder and wrist we trained and validated CNN algorithms to classify radiographs with and without metal. Algorithm performance was evaluated with area under the receiver-operating curve (AUC), sensitivity, specificity, predictive values and accuracies compared with a reference standard of manually labelling.

RESULTS : Sensitivities, specificities and area under the ROC-curves (AUC) for the six anatomic regions ranged from 85.33% (95% CI: 78.64%-90.57%) to 100.00% (95% CI: 98.16%-100.00%), 75.44% (95% CI: 62.24%-85.87%) to 93.57% (95% CI: 88.78%-96.75%) and 0.95 to 0.99, respectively.

CONCLUSION : CNN algorithms classify DRs with metallic objects for six different anatomic regions with near-perfect accuracy. The rapid and iterative capability of the algorithms allows for scalable expansion and as a substitute MRI screening tool for metallic objects.

IMPLICATIONS FOR PRACTICE : All CNNs would be able to assist in metal detection of digital radiographs prior to MRI, an substantially decrease screening time.

Lie S O, Lysdahlgaard S

2022-Jan-15

Deep learning, Digital radiography, Magnetic resonance imaging, Metallic objects, Patient safety

Surgery Surgery

Questionnaire-based survey on the prevalence of medication-overuse headache in Japanese one city-Itoigawa study.

In Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology

OBJECTIVE : The medication-overuse headache (MOH) prevalence has not been investigated in a general Japanese population. We performed questionnaire-based survey and revealed MOH prevalence and its characteristics. We also performed clustering to obtain insight for MOH subgrouping.

METHODS : In this cross-sectional study, the 15-64-year-old population was investigated in Itoigawa during their COVID-19 vaccination under the national policy. MOH was defined as ≥ 15 days/month plus self-report of use of pain medications ≥ 10 or 15 days/month in the last 3 months. Ward method and k-means +  + were used to perform clustering MOH patients.

RESULTS : Among 5865 valid responses, MOH prevalence was 2.32%. MOH was common among females and the middle-aged. Combination-analgesic is the most overused as 50%. MOH had aggravation by routine physical activity, moderate or severe pain, and migraine-like, compared to non-MOH. The 136 MOH patients could be grouped into 3 clusters. Age and frequency of acute medication use were essential factors for clustering.

CONCLUSIONS : This is the first study of MOH prevalence in Japan. Most MOH characteristics were similar to previous reports worldwide. Public awareness of proper headache treatment knowledge is still needed. Clustering results may be important for subtype grouping from a social perspective apart from existing clinical subtypes.

Katsuki Masahito, Yamagishi Chinami, Matsumori Yasuhiko, Koh Akihito, Kawamura Shin, Kashiwagi Kenta, Kito Tomohiro, Entani Akio, Yamamoto Toshiko, Ikeda Takashi, Yamagishi Fuminori

2022-Jan-19

Artificial intelligence (AI), Chronic headache, Clustering, Epidemiology, Medication-overuse headache (MOH), Migraine

General General

Prediction of Poststroke Urinary Tract Infection Risk in Immobile Patients Using Machine Learning: a observational cohort study.

In The Journal of hospital infection

BACKGROUND : Urinary tract infection (UTI) is one of major nosocomial infections significantly affecting the outcomes of immobile stroke patients. Previous studies have identified several risk factors, but it's still challenging to accurately estimate personal UTI risk.

OBJECTIVES : We aimed to develop predictive models for UTI risk identification for immobile stroke patients.

METHODS : Research data were collected from our previous multi-centre study. Derivation cohort included 3982 immobile stroke patients collected from November 1, 2015 to June 30, 2016; external validation cohort included 3837 patients collected from November 1, 2016 to July 30, 2017. 6 machine learning models and an ensemble learning model were derived based on 80% of derivation cohort and effectiveness was evaluated with the remaining 20%. We used Shapley additive explanation values to determine feature importance and examine the clinical significance of prediction models.

RESULTS : 2.59% (103/3982) patients were diagnosed with UTI in derivation cohort, 1.38% (53/3837) in external cohort. The ensemble learning model performed the best in area under the receiver operating characteristic (ROC) curve in internal validation (82.2%); second best in external validation (80.8%). In addition, the ensemble learning model performed the best sensitivity in both internal and external validation sets (80.9% and 81.1%, respectively). We also identified seven UTI risk factors (pneumonia, glucocorticoid use, female sex, mixed cerebrovascular disease, increased age, prolonged length of stay, and duration of catheterization).

CONCLUSIONS : Our ensemble learning model demonstrated promising performance. Future work should continue to develop a more concise scoring tool based on machine learning models and prospectively examining the model in practical use, thus improving clinical outcomes.

Zhu Chen, Xu Zidu, Gu Yaowen, Zheng Si, Sun Xiangyu, Cao Jing, Song Baoyun, Jin Jingfen, Liu Yilan, Wen Xianxiu, Cheng Shouzhen, Li Jiao, Wu Xinjuan

2022-Jan-16

immobility, machine learning, prediction, stroke, urinary tract infections

Pathology Pathology

Genome-wide identification of the genetic basis of amyotrophic lateral sclerosis.

In Neuron ; h5-index 148.0

Amyotrophic lateral sclerosis (ALS) is a complex disease that leads to motor neuron death. Despite heritability estimates of 52%, genome-wide association studies (GWASs) have discovered relatively few loci. We developed a machine learning approach called RefMap, which integrates functional genomics with GWAS summary statistics for gene discovery. With transcriptomic and epigenetic profiling of motor neurons derived from induced pluripotent stem cells (iPSCs), RefMap identified 690 ALS-associated genes that represent a 5-fold increase in recovered heritability. Extensive conservation, transcriptome, network, and rare variant analyses demonstrated the functional significance of candidate genes in healthy and diseased motor neurons and brain tissues. Genetic convergence between common and rare variation highlighted KANK1 as a new ALS gene. Reproducing KANK1 patient mutations in human neurons led to neurotoxicity and demonstrated that TDP-43 mislocalization, a hallmark pathology of ALS, is downstream of axonal dysfunction. RefMap can be readily applied to other complex diseases.

Zhang Sai, Cooper-Knock Johnathan, Weimer Annika K, Shi Minyi, Moll Tobias, Marshall Jack N G, Harvey Calum, Nezhad Helia Ghahremani, Franklin John, Souza Cleide Dos Santos, Ning Ke, Wang Cheng, Li Jingjing, Dilliott Allison A, Farhan Sali, Elhaik Eran, Pasniceanu Iris, Livesey Matthew R, Eitan Chen, Hornstein Eran, Kenna Kevin P, Veldink Jan H, Ferraiuolo Laura, Shaw Pamela J, Snyder Michael P

2022-Jan-11

ALS, TDP-43 mislocalization, axonal dysfunction, epigenetics, gene discovery, genetics, iPSC, machine learning, motor neurons, multiomics

General General

Applying machine learning algorithms to electronic health records predicted pneumonia after respiratory tract infection.

In Journal of clinical epidemiology ; h5-index 60.0

OBJECTIVES : To predict community acquired pneumonia (CAP) after respiratory tract infection (RTI) consultations in primary care by applying machine learning to electronic health records (EHRs).

STUDY DESIGN AND SETTING : A population-based cohort study was conducted using primary care electronic health records between 2002 to 2017. 16,289 patients who consulted with RTIs then subsequently diagnosed with pneumonia within 30 days were compared with a random sample of eligible RTI patients. Variable selection compared logistic regression, random forest and penalized regression models. Prediction models were developed using classification and regression trees (CART) and logistic regression. Model performance was assessed through internal and temporal validations.

RESULTS : Older age, comorbidity and initial presentation with lower respiratory tract infection (LRTIs) were identified as the main predictors of pneumonia diagnosis. Developed models achieved good discrimination accuracy with AUROC for the logistic regression model being 0.81 (0.80, 0.84) and 0.70 (0.69, 0.71) for CART during internal validation, and 0.80 (0.79, 0.81) vs 0.68 (0.67, 0.69) for temporal validation.

CONCLUSION : From a large number of candidate variables, a small number of predictors of pneumonia were consistently identified through machine learning variable selection procedures. Logistic regression generally provided better model performance than CART models.

Sun Xiaohui, Douiri Abdel, Gulliford Martin

2022-Jan-16

Electronic health records, Machine learning, Pneumonia, Prediction modelling, Primary care, Respiratory tract infection

Pathology Pathology

A functional module states framework reveals transcriptional states for drug and target prediction.

In Cell reports ; h5-index 119.0

Cells are complex systems in which many functions are performed by different genetically defined and encoded functional modules. To systematically understand how these modules respond to drug or genetic perturbations, we develop a functional module states framework. Using this framework, we (1) define the drug-induced transcriptional state space for breast cancer cell lines using large public gene expression datasets and reveal that the transcriptional states are associated with drug concentration and drug targets, (2) identify potential targetable vulnerabilities through integrative analysis of transcriptional states after drug treatment and gene knockdown-associated cancer dependency, and (3) use functional module states to predict transcriptional state-dependent drug sensitivity and build prediction models for drug response. This approach demonstrates a similar prediction performance as approaches using high-dimensional gene expression values, with the added advantage of more clearly revealing biologically relevant transcriptional states and key regulators.

Qin Guangrong, Knijnenburg Theo A, Gibbs David L, Moser Russell, Monnat Raymond J, Kemp Christopher J, Shmulevich Ilya

2022-Jan-18

cell states, drug response prediction, functional states, machine learning, target prediction

General General

Trends in the conduct and reporting of clinical prediction model development and validation: a systematic review.

In Journal of the American Medical Informatics Association : JAMIA

OBJECTIVES : This systematic review aims to provide further insights into the conduct and reporting of clinical prediction model development and validation over time. We focus on assessing the reporting of information necessary to enable external validation by other investigators.

MATERIALS AND METHODS : We searched Embase, Medline, Web-of-Science, Cochrane Library, and Google Scholar to identify studies that developed 1 or more multivariable prognostic prediction models using electronic health record (EHR) data published in the period 2009-2019.

RESULTS : We identified 422 studies that developed a total of 579 clinical prediction models using EHR data. We observed a steep increase over the years in the number of developed models. The percentage of models externally validated in the same paper remained at around 10%. Throughout 2009-2019, for both the target population and the outcome definitions, code lists were provided for less than 20% of the models. For about half of the models that were developed using regression analysis, the final model was not completely presented.

DISCUSSION : Overall, we observed limited improvement over time in the conduct and reporting of clinical prediction model development and validation. In particular, the prediction problem definition was often not clearly reported, and the final model was often not completely presented.

CONCLUSION : Improvement in the reporting of information necessary to enable external validation by other investigators is still urgently needed to increase clinical adoption of developed models.

Yang Cynthia, Kors Jan A, Ioannou Solomon, John Luis H, Markus Aniek F, Rekkas Alexandros, de Ridder Maria A J, Seinen Tom M, Williams Ross D, Rijnbeek Peter R

2022-Jan-19

clinical decision support, clinical prediction model, electronic health record, external validation, machine learning

General General

Hybrid machine learning to localize atrial flutter substrates using the surface 12-lead electrocardiogram.

In Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology

AIMS : Atrial flutter (AFlut) is a common re-entrant atrial tachycardia driven by self-sustainable mechanisms that cause excitations to propagate along pathways different from sinus rhythm. Intra-cardiac electrophysiological mapping and catheter ablation are often performed without detailed prior knowledge of the mechanism perpetuating AFlut, likely prolonging the procedure time of these invasive interventions. We sought to discriminate the AFlut location [cavotricuspid isthmus-dependent (CTI), peri-mitral, and other left atrium (LA) AFlut classes] with a machine learning-based algorithm using only the non-invasive signals from the 12-lead electrocardiogram (ECG).

METHODS AND RESULTS : Hybrid 12-lead ECG dataset of 1769 signals was used (1424 in silico ECGs, and 345 clinical ECGs from 115 patients-three different ECG segments over time were extracted from each patient corresponding to single AFlut cycles). Seventy-seven features were extracted. A decision tree classifier with a hold-out classification approach was trained, validated, and tested on the dataset randomly split after selecting the most informative features. The clinical test set comprised 38 patients (114 clinical ECGs). The classifier yielded 76.3% accuracy on the clinical test set with a sensitivity of 89.7%, 75.0%, and 64.1% and a positive predictive value of 71.4%, 75.0%, and 86.2% for CTI, peri-mitral, and other LA class, respectively. Considering majority vote of the three segments taken from each patient, the CTI class was correctly classified at 92%.

CONCLUSION : Our results show that a machine learning classifier relying only on non-invasive signals can potentially identify the location of AFlut mechanisms. This method could aid in planning and tailoring patient-specific AFlut treatments.

Luongo Giorgio, Vacanti Gaetano, Nitzke Vincent, Nairn Deborah, Nagel Claudia, Kabiri Diba, Almeida Tiago P, Soriano Diogo C, Rivolta Massimo W, Ng Ghulam André, Dössel Olaf, Luik Armin, Sassi Roberto, Schmitt Claus, Loewe Axel

2022-Jan-19

Atrial flutter, Cardiac modelling, Electrocardiography, Machine learning, Personalized medicine

Public Health Public Health

Efficacy and Applications of Artificial Intelligence and Machine Learning Analyses in Total Joint Arthroplasty: A Call for Improved Reporting.

In The Journal of bone and joint surgery. American volume

BACKGROUND : There has been a considerable increase in total joint arthroplasty (TJA) research using machine learning (ML). Therefore, the purposes of this study were to synthesize the applications and efficacies of ML reported in the TJA literature, and to assess the methodological quality of these studies.

METHODS : PubMed, OVID/MEDLINE, and Cochrane libraries were queried in January 2021 for articles regarding the use of ML in TJA. Study demographics, topic, primary and secondary outcomes, ML model development and testing, and model presentation and validation were recorded. The TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines were used to assess the methodological quality.

RESULTS : Fifty-five studies were identified: 31 investigated clinical outcomes and resource utilization; 11, activity and motion surveillance; 10, imaging detection; and 3, natural language processing. For studies reporting the area under the receiver operating characteristic curve (AUC), the median AUC (and range) was 0.80 (0.60 to 0.97) among 26 clinical outcome studies, 0.99 (0.83 to 1.00) among 6 imaging-based studies, and 0.88 (0.76 to 0.98) among 3 activity and motion surveillance studies. Twelve studies compared ML to logistic regression, with 9 (75%) reporting that ML was superior. The average number of TRIPOD guidelines met was 11.5 (range: 5 to 18), with 38 (69%) meeting greater than half of the criteria. Presentation and explanation of the full model for individual predictions and assessments of model calibration were poorly reported (<30%).

CONCLUSIONS : The performance of ML models was good to excellent when applied to a wide variety of clinically relevant outcomes in TJA. However, reporting of certain key methodological and model presentation criteria was inadequate. Despite the recent surge in TJA literature utilizing ML, the lack of consistent adherence to reporting guidelines needs to be addressed to bridge the gap between model development and clinical implementation.

Polce Evan M, Kunze Kyle N, Dooley Matthew S, Piuzzi Nicolas S, Boettner Friedrich, Sculco Peter K

2022-Jan-19

Public Health Public Health

Predictors of underutilization of lung cancer screening: a machine learning approach.

In European journal of cancer prevention : the official journal of the European Cancer Prevention Organisation (ECP)

Lung cancer is the second common cancer and a leading cause of cancer-related death in the US. Unfavorably, the prevalence of using low-dose computed tomography (LDCT) for lung cancer prevention in the US has remained below 4% over time. The purpose of this study is to develop machine learning models to analyze interactive pathways of factors associated with lung cancer screening use with the LDCT. The study was based on the data retrieved from the 2018 Behavioral Risk Factor Surveillance System. After dealing with missing values, 86 variables and 710 samples were included in the decision tree model and the random forest model. The data were randomly split into training (569/710, 80%) and testing (141/710, 20%) sets. Gini impurity is used to select and determine the optimal split of the nodes in the model. Machine learning performance was evaluated by model accuracy, sensitivity, specificity, F1 score, etc. The average performance metrics of the decision tree model were obtained: average accuracy is 67.78%, F1 score is 65.76%, sensitivity is 62.52%, and specificity is 73.57% based on 100 runs. In the decision model, nine interactive pathways were identified among the following factors: average drinks per month, BMI, diabetes, first smoke age, years of smoking, year(s) quit smoking, sex, last sigmoidoscopy or colonoscopy, last dental visit, general health, insurance, education, and last Pap test. Lung cancer screening utilization is the result of the interplay of multifactors. Lung cancer screening programs in clinical settings should not only focus on patients' smoking behaviors but also consider other socioeconomic factors.

Guo Yuqi, Yin Shuhua, Chen Shi, Ge Yaorong

2022-Jan-17

General General

Multiparty Dual Learning.

In IEEE transactions on cybernetics

The performance of machine learning algorithms heavily relies on the availability of a large amount of training data. However, in reality, data usually reside in distributed parties such as different institutions and may not be directly gathered and integrated due to various data policy constraints. As a result, some parties may suffer from insufficient data available for training machine learning models. In this article, we propose a multiparty dual learning (MPDL) framework to alleviate the problem of limited data with poor quality in an isolated party. Since the knowledge-sharing processes for multiple parties always emerge in dual forms, we show that dual learning is naturally suitable to handle the challenge of missing data, and explicitly exploits the probabilistic correlation and structural relationship between dual tasks to regularize the training process. We introduce a feature-oriented differential privacy with mathematical proof, in order to avoid possible privacy leakage of raw features in the dual inference process. The approach requires minimal modifications to the existing multiparty learning structure, and each party can build flexible and powerful models separately, whose accuracy is no less than nondistributed self-learning approaches. The MPDL framework achieves significant improvement compared with state-of-the-art multiparty learning methods, as we demonstrated through simulations on real-world datasets.

Gao Yuan, Gong Maoguo, Xie Yu, Qin A K, Pan Ke, Ong Yew-Soon

2022-Jan-19

General General

CPInformer for Efficient and Robust Compound-Protein Interaction Prediction.

In IEEE/ACM transactions on computational biology and bioinformatics

Recently, deep learning has become the mainstream methodology for Compound-Protein Interaction (CPI) prediction. However, the existing compound-protein feature extraction methods have some issues that limit their performance. First, graph networks are widely used for structural compound feature extraction, but the chemical properties of a compound depend on functional groups rather than graphic structure. Besides, the existing methods lack capabilities in extracting rich and discriminative protein features. Last, the compound-protein features are usually simply combined for CPI prediction, without considering information redundancy and effective feature mining. To address the above issues, we propose a novel CPInformer method. Specifically, we extract heterogeneous compound features, including structural graph features and functional class fingerprints, to reduce prediction errors caused by similar structural compounds. Then, we combine local and global features using dense connections to obtain multi-scale protein features. Last, we apply ProbSparse self-attention to protein features, under the guidance of compound features, to eliminate information redundancy, and to improve the accuracy of CPInformer. More importantly, the proposed method identifies the activated local regions that link a CPI, providing a good visualisation for the CPI state. The results obtained on five benchmarks demonstrate the merits and superiority of CPInformer over the state-of-the-art approaches.

Hua Yang, Song Xiao-Ning, Feng Zhenhua, Wu Xiao-Jun, Kittler Josef, Yu Dong-Jun

2022-Jan-19

General General

Monitoring of COVID-19 Pandemic-related Psychopathology using Machine Learning.

In Acta neuropsychiatrica

The COVID-19 pandemic is believed to have a major negative impact on global mental health due to the viral disease itself as well as the associated lockdowns, social distancing, isolation, fear, and increased uncertainty. Individuals with preexisting mental illness are likely to be particularly vulnerable to these conditions and may develop outright "COVID-19 related psychopathology". Here, we trained a machine learning model on structured and natural text data from electronic health records to identify COVID-19 pandemic-related psychopathology among patients receiving care in the Psychiatric Services of the Central Denmark Region. Subsequently, applying this model, we found that pandemic-related psychopathology covaries with the pandemic pressure over time. These findings may aid psychiatric services in their planning during the ongoing and future pandemics. Furthermore, the results are a testament to the potential of applying machine learning to data from electronic health records.

Enevoldsen Kenneth C, Danielsen Andreas A, Rohde Christopher, Jefsen Oskar H, Nielbo Kristoffer L, Østergaard Søren D

2022-Jan-19

COVID-19, Coronavirus, Machine Learning, Mental Disorders, Natural Language Processing

General General

Evidence for Peroxisomal Dysfunction and Dysregulation of the CDP-Choline Pathway in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.

In medRxiv : the preprint server for health sciences

Background : Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic and debilitating disease that is characterized by unexplained physical fatigue unrelieved by rest. Symptoms also include cognitive and sensory dysfunction, sleeping disturbances, orthostatic intolerance, and gastrointestinal problems. A syndrome clinically similar to ME/CFS has been reported following well-documented infections with the coronaviruses SARS-CoV and MERS-CoV. At least 10% of COVID-19 survivors develop post acute sequelae of SARS-CoV-2 infection (PASC). Although many individuals with PASC have evidence of structural organ damage, a subset have symptoms consistent with ME/CFS including fatigue, post exertional malaise, cognitive dysfunction, gastrointestinal disturbances, and postural orthostatic intolerance. These common features in ME/CFS and PASC suggest that insights into the pathogenesis of either may enrich our understanding of both syndromes, and could expedite the development of strategies for identifying those at risk and interventions that prevent or mitigate disease.

Methods : Using regression, Bayesian and enrichment analyses, we conducted targeted and untargeted metabolomic analysis of 888 metabolic analytes in plasma samples of 106 ME/CFS cases and 91 frequency-matched healthy controls.

Results : In ME/CFS cases, regression, Bayesian and enrichment analyses revealed evidence of peroxisomal dysfunction with decreased levels of plasmalogens. Other findings included decreased levels of several membrane lipids, including phosphatidylcholines and sphingomyelins, that may indicate dysregulation of the cytidine-5’-diphosphocholine pathway. Enrichment analyses revealed decreased levels of choline, ceramides and carnitines, and increased levels of long chain triglycerides (TG) and hydroxy-eicosapentaenoic acid. Elevated levels of dicarboxylic acids were consistent with abnormalities in the tricarboxylic acid cycle. Using machine learning algorithms with selected metabolites as predictors, we were able to differentiate female ME/CFS cases from female controls (highest AUC=0.794) and ME/CFS cases without self-reported irritable bowel syndrome (sr-IBS) from controls without sr-IBS (highest AUC=0.873).

Conclusion : Our findings are consistent with earlier ME/CFS work indicating compromised energy metabolism and redox imbalance, and highlight new abnormalities that may provide insights into the pathogenesis of ME/CFS.

One Sentence Summary : Plasma levels of plasmalogens are decreased in patients with myalgic encephalomyelitis/chronic fatigue syndrome suggesting peroxisome dysfunction.

Che Xiaoyu, Brydges Christopher R, Yu Yuanzhi, Price Adam, Joshi Shreyas, Roy Ayan, Lee Bohyun, Barupal Dinesh K, Cheng Aaron, Palmer Dana March, Levine Susan, Peterson Daniel L, Vernon Suzanne D, Bateman Lucinda, Hornig Mady, Montoya Jose G, Komaroff Anthony L, Fiehn Oliver, Lipkin W Ian

2022-Jan-11

General General

A review of fusion methods for omics and imaging data.

In IEEE/ACM transactions on computational biology and bioinformatics

The development of omics data and biomedical images has greatly advanced the progress of precision medicine in diagnosis, treatment, and prognosis. The fusion of omics and imaging data, i.e., omics-imaging fusion, offers a new strategy for understanding complex diseases. However, due to a variety of issues such as the limited number of samples, high dimensionality of features, and heterogeneity of different data types, efficiently learning complementary or associated discriminative fusion information from omics and imaging data remains a challenge. Recently, numerous machine learning methods have been proposed to alleviate these problems. In this review, from the perspective of fusion levels and fusion methods, we first provide an overview of preprocessing and feature extraction methods for omics and imaging data, and comprehensively analyze and summarize the basic forms and variations of commonly used and newly emerging fusion methods, along with their advantages, disadvantages and the applicable scope. We then describe public datasets and compare experimental results of various fusion methods on the ADNI and TCGA datasets. Finally, we discuss future prospects and highlight remaining challenges in the field.

Huang Weixian, Tan Kaiwen, Hu Jinlong, Zhang Ziye, Dong Shoubin

2022-Jan-19

General General

Multiview Robust Graph-based Clustering for Cancer Subtype Identification.

In IEEE/ACM transactions on computational biology and bioinformatics

Cancer subtype identification is to classify cancer into groups according to their molecular characteristics and clinical manifestations and is the basis for more personalized diagnosis and therapy. Public datasets such as The Cancer Genome Atlas (TCGA) have collected a massive number of multi-omics data. The accumulation of these datasets provides unprecedented opportunities to study the mechanism of cancers and further identify cancer subtypes at a comprehensive level. In this paper, we propose a multi-view robust graph-based clustering (MRGC) method to effectively identify cancer subtypes. Our method first learns robust latent representations from the raw omics data to alleviate the influences of the noise, where a set of similarity matrices are then adaptively learned based on these new representations. Finally, a global similarity graph is obtained by exploiting the consensus structure from the graphs. As a result, the three parts in our method can reinforce each other in a mutual iterative manner. We conduct extensive experiments on both generic machine learning datasets and cancer datasets. The experimental results confirm that our model can achieve satisfactory clustering performance compared to several state-of-the-art approaches. Moreover, we convey the practicability of MRGC by carrying out a case study on hepatocellular carcinoma.

Shi Xiaofeng, Liang Cheng, Wang Hong

2022-Jan-19

General General

Automatic Schelling Points Detection from Meshes.

In IEEE transactions on visualization and computer graphics

Mesh Schelling points explain how humans focus on specific regions of a 3D object. They have a large number of important applications in computer graphics and provide valuable information for perceptual psychology studies. However, detecting mesh Schelling points is time-consuming and expensive since the existing techniques are mostly based on participant observation studies. To overcome these limitations, we propose to employ powerful deep learning techniques to detect mesh Schelling points in an automatic manner, free from participant observation studies. Specifically, we utilize the mesh convolution and pooling operations to extract informative features from mesh objects, and then predict the 3D heat map of Schelling points in an end-to-end manner. In addition, we propose a Deep Schelling Network (DS-Net) to automatically detect the Schelling points, including a multi-scale fusion component and a novel region-specific loss function to improve our network for a better regression of heat maps. To the best of our knowledge, DS-Net is the first deep neural network for detecting Schelling points from 3D meshes. We evaluate DS-Net on a mesh Schelling point dataset obtained from participant observation studies. The experimental results demonstrate that DS-Net is capable of detecting mesh Schelling points effectively and outperforms various state-of-the-art mesh saliency methods and deep learning models, both qualitatively and quantitatively.

Chen Geng, Dai Hang, Zhou Tao, Shen Jianbing, Shao Ling

2022-Jan-19

General General

Analysis and classification of peanuts with fungal diseases based on real-time spectral processing.

In Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment

The study presents an approach to the analysis and classification of peanuts performed in order to detect kernels with fungi diseases, i.e. kernels prone to contamination with mycotoxigenic Aspergillus flavus (Aspergillus parasiticus). The aim of this study was to evaluate the effectiveness of luminescent spectroscopy with a violet laser (405 nm wavelength) as the excitation source of the fluorescence when applied for real-time detection of mould in peanuts performed by means of multispectral processing based on machine learning methods. We suggest a laboratory unit used to form, register, and process the luminescence spectra of peanuts in visible and near-infrared wavelength ranges in the real-time mode. The study demonstrated that contaminated peanuts have increased luminous intensity and show a redshift in the fluorescence peaks of the contaminated samples as compared to the pure ones. The difference in the fluorescence spectra of pure and contaminated kernels is compatible with the results obtained when traditional UV-light sources are used (365 nm). To classify peanuts by their spectral characteristics, neural network algorithms were used combined with dimensionality reduction methods. The paper presents the probabilities of incorrect recognition of the peanuts' type depending on the number of relevant secondary features determined when reducing the dimensionality of the initial data. When 10 spectral components were used, the error ratios were 0.7% or 0.3% depending on the method of reducing the dimensionality of the initial data.

Lavrinenko Igor A, Donskikh Artem O, Minakov Dmitriy A, Sirota Alexander A

2022-Jan-19

Violet laser, fungal diseases of peanut, image classification, luminescence spectroscopy, on-stream express analysis

General General

Negative Patient Descriptors: Documenting Racial Bias In The Electronic Health Record.

In Health affairs (Project Hope)

Little is known about how racism and bias may be communicated in the medical record. This study used machine learning to analyze electronic health records (EHRs) from an urban academic medical center and to investigate whether providers' use of negative patient descriptors varied by patient race or ethnicity. We analyzed a sample of 40,113 history and physical notes (January 2019-October 2020) from 18,459 adult patients for sentences containing a negative descriptor (for example, resistant or noncompliant) of the patient or the patient's behavior. We used mixed effects logistic regression to determine the odds of finding at least one negative descriptor as a function of the patient's race or ethnicity, controlling for sociodemographic and health characteristics. Compared with White patients, Black patients had 2.54 times the odds of having at least one negative descriptor in the history and physical notes. Our findings raise concerns about stigmatizing language in the EHR and its potential to exacerbate racial and ethnic health care disparities.

Sun Michael, Oliwa Tomasz, Peek Monica E, Tung Elizabeth L

2022-Jan-19

General General

Inverse design of soft materials via a deep learning-based evolutionary strategy.

In Science advances

Colloidal self-assembly-the spontaneous organization of colloids into ordered structures-has been considered key to produce next-generation materials. However, the present-day staggering variety of colloidal building blocks and the limitless number of thermodynamic conditions make a systematic exploration intractable. The true challenge in this field is to turn this logic around and to develop a robust, versatile algorithm to inverse design colloids that self-assemble into a target structure. Here, we introduce a generic inverse design method to efficiently reverse-engineer crystals, quasicrystals, and liquid crystals by targeting their diffraction patterns. Our algorithm relies on the synergetic use of an evolutionary strategy for parameter optimization, and a convolutional neural network as an order parameter, and provides a way forward for the inverse design of experimentally feasible colloidal interactions, specifically optimized to stabilize the desired structure.

Coli Gabriele M, Boattini Emanuele, Filion Laura, Dijkstra Marjolein

2022-Jan-21

General General

Biometrics-protected optical communication enabled by deep learning-enhanced triboelectric/photonic synergistic interface.

In Science advances

Security is a prevailing concern in communication as conventional encryption methods are challenged by progressively powerful supercomputers. Here, we show that biometrics-protected optical communication can be constructed by synergizing triboelectric and nanophotonic technology. The synergy enables the loading of biometric information into the optical domain and the multiplexing of digital and biometric information at zero power consumption. The multiplexing process seals digital signals with a biometric envelope to avoid disrupting the original high-speed digital information and enhance the complexity of transmitted information. The system can perform demultiplexing, recover high-speed digital information, and implement deep learning to identify 15 users with around 95% accuracy, irrespective of biometric information data types (electrical, optical, or demultiplexed optical). Secure communication between users and the cloud is established after user identification for document exchange and smart home control. Through integrating triboelectric and photonics technology, our system provides a low-cost, easy-to-access, and ubiquitous solution for secure communication.

Dong Bowei, Zhang Zixuan, Shi Qiongfeng, Wei Jingxuan, Ma Yiming, Xiao Zian, Lee Chengkuo

2022-Jan-21

General General

Learning robust perceptive locomotion for quadrupedal robots in the wild.

In Science robotics

Legged robots that can operate autonomously in remote and hazardous environments will greatly increase opportunities for exploration into underexplored areas. Exteroceptive perception is crucial for fast and energy-efficient locomotion: Perceiving the terrain before making contact with it enables planning and adaptation of the gait ahead of time to maintain speed and stability. However, using exteroceptive perception robustly for locomotion has remained a grand challenge in robotics. Snow, vegetation, and water visually appear as obstacles on which the robot cannot step or are missing altogether due to high reflectance. In addition, depth perception can degrade due to difficult lighting, dust, fog, reflective or transparent surfaces, sensor occlusion, and more. For this reason, the most robust and general solutions to legged locomotion to date rely solely on proprioception. This severely limits locomotion speed because the robot has to physically feel out the terrain before adapting its gait accordingly. Here, we present a robust and general solution to integrating exteroceptive and proprioceptive perception for legged locomotion. We leverage an attention-based recurrent encoder that integrates proprioceptive and exteroceptive input. The encoder is trained end to end and learns to seamlessly combine the different perception modalities without resorting to heuristics. The result is a legged locomotion controller with high robustness and speed. The controller was tested in a variety of challenging natural and urban environments over multiple seasons and completed an hour-long hike in the Alps in the time recommended for human hikers.

Miki Takahiro, Lee Joonho, Hwangbo Jemin, Wellhausen Lorenz, Koltun Vladlen, Hutter Marco

2022-Jan-19

Public Health Public Health

Causal inference methods for vaccine sieve analysis with effect modification.

In Statistics in medicine

The protective effects of vaccines may vary depending on individual characteristics, such as age. Traditionally, such effect modification has been examined with subgroup analyses or inclusion of cross-product terms in regression frameworks. However, in many vaccine settings, effect modification may also depend on the infecting pathogen's characteristics, which are measured postrandomization. Sieve analysis examines whether such effects are present by combining pathogen genetic sequence information with individual-level data and can generate new hypotheses on the pathways whereby vaccines provide protection. In this article, we develop a causal framework for evaluating effect modification in the context of sieve analysis. Our approach can be used to assess the magnitude of sieve effects and, in particular, whether these effects are modified by individual-level characteristics. Our method accounts for difficulties occurring in real-world data analysis, such as competing risks, nonrandomized treatments, and differential dropout. Our approach also integrates modern machine learning techniques. We demonstrate the validity and efficiency of our approach in simulation studies and apply the methodology to a malaria vaccine study.

Yang Guandong, Balzer Laura B, Benkeser David

2022-Jan-19

malaria, marginal structural models, sieve analysis, targeted minimum loss-based estimation, vaccines

General General

Colon tissue image segmentation with MWSI-NET.

In Medical & biological engineering & computing ; h5-index 32.0

Developments in deep learning have resulted in computer-aided diagnosis for many types of cancer. Previously, pathologists manually performed the labeling work in the analysis of colon tissues, which is both time-consuming and labor-intensive. Results are easily affected by subjective conditions. Therefore, it is beneficial to identify the cancerous regions of colon cancer with the assistance of computer-aided technology. Pathological images are often difficult to process due to their irregularity, similarity between cancerous and non-cancerous tissues and large size. We propose a multi-scale perceptual field fusion structure based on a dilated convolutional network. Using this model, a structure of dilated convolution kernels with different aspect ratios is inserted, which can process cancerous regions of different sizes and generate larger receptive fields. Thus, the model can fuse detailed information at different scales for better semantic segmentation. Two different attention mechanisms are adopted to highlight the cancerous areas. A large, open-source dataset was used to verify improved efficacy when compared to previously disclosed methods.

Cheng Hao, Wu Kaijie, Tian Jie, Ma Kai, Gu Chaocheng, Guan Xinping

2022-Jan-19

Attention mechanism, Dilated convolution, Multi-scale, Pathological image segmentation

General General

HairNet: a deep learning model to score leaf hairiness, a key phenotype for cotton fibre yield, value and insect resistance.

In Plant methods

BACKGROUND : Leaf hairiness (pubescence) is an important plant phenotype which regulates leaf transpiration, affects sunlight penetration, and provides increased resistance or susceptibility against certain insects. Cotton accounts for 80% of global natural fibre production, and in this crop leaf hairiness also affects fibre yield and value. Currently, this key phenotype is measured visually which is slow, laborious and operator-biased. Here, we propose a simple, high-throughput and low-cost imaging method combined with a deep-learning model, HairNet, to classify leaf images with great accuracy.

RESULTS : A dataset of [Formula: see text] 13,600 leaf images from 27 genotypes of Cotton was generated. Images were collected from leaves at two different positions in the canopy (leaf 3 & leaf 4), from genotypes grown in two consecutive years and in two growth environments (glasshouse & field). This dataset was used to build a 4-part deep learning model called HairNet. On the whole dataset, HairNet achieved accuracies of 89% per image and 95% per leaf. The impact of leaf selection, year and environment on HairNet accuracy was then investigated using subsets of the whole dataset. It was found that as long as examples of the year and environment tested were present in the training population, HairNet achieved very high accuracy per image (86-96%) and per leaf (90-99%). Leaf selection had no effect on HairNet accuracy, making it a robust model.

CONCLUSIONS : HairNet classifies images of cotton leaves according to their hairiness with very high accuracy. The simple imaging methodology presented in this study and the high accuracy on a single image per leaf achieved by HairNet demonstrates that it is implementable at scale. We propose that HairNet replaces the current visual scoring of this trait. The HairNet code and dataset can be used as a baseline to measure this trait in other species or to score other microscopic but important phenotypes.

Rolland Vivien, Farazi Moshiur R, Conaty Warren C, Cameron Deon, Liu Shiming, Petersson Lars, Stiller Warwick N

2022-Jan-19

Cotton, Deep learning, Hair, Hairiness, Leaf, Machine learning, Neural network, Phenotyping, Pubescence, Trichome

General General

Forecasting Covid-19 Transmission with ARIMA and LSTM Techniques in Morocco.

In SN computer science

In this paper, we are interested to forecast and predict the time evolution of the Covid-19 in Morocco based on two different time series forecasting models. We used Auto-Regressive Integrated Moving Average (ARIMA) and Long short-term memory (LSTM) models to predict the outbreak of Covid-19 in the upcoming 2 months in Morocco. In this work, we measured the effective reproduction number using the real data and also the fitted forecasted data produced by the two used approaches, to reveal how effective the measures taken by the Moroccan government have been controlling the Covid-19 outbreak. The prediction results for the next 2 months show a strong evolution in the number of confirmed and death cases in Morocco. According to the measures of the effective reproduction number, the transmissibility of the disease will continue to expand in the next 2 months, but fortunately, the higher value of the effective reproduction number is not considered to be dramatic and, therefore, may give hope for controlling the disease.

Rguibi Mohamed Amine, Moussa Najem, Madani Abdellah, Aaroud Abdessadak, Zine-Dine Khalid

2022

Covid-19, Epidemic transmission, Machine learning, Time series forecasting

Radiology Radiology

Population-specific age estimation in Black Americans and Chinese people based on pulp chamber volume of first molars from cone beam computed tomography.

In International journal of legal medicine

OBJECTIVES : To validate the fitness of the age estimation model in Black Americans, which was previously and solely established for the Chinese population based on pulp chamber volume of the first molars from cone beam computed tomography (CBCT), and to establish a new age estimation model for Black Americans.

MATERIALS AND METHODS : A total of 203 subjects with CBCT scans, including 119 Chinese and 84 Black Americans, were retrospectively identified. The age range of subjects was between 11 and 87 years. For both populations, automated 3D pulp chamber segmentation of the first molars was performed by deep learning, followed by volume calculation and age estimation by a logarithmic regression model, which was established in a prior study solely on Chinese population. Additionally, a separate logarithmic regression analysis was carried out on Black Americans. The performance of age estimation was assessed by the mean absolute error (MAE), root mean square error (RMSE), Wilcoxon signed rank test, and coefficient of determination (R2) between the actual and estimated human ages.

RESULTS : When applying the age estimation model established in the prior study, MAE = 7.994 years and RMSE = 10.065 years were observed in the Chinese population, while MAE = 14.049 years and RMSE = 17.866 years were observed in Black Americans. The new age estimation model established for Black Americans was AGE = 89.752 - 21.176 × lnV (V = pulp chamber volume), with MAE = 7.930 years, RMSE = 10.664 years, and coefficient of determination (R2) = 0.600.

CONCLUSIONS : Population-specific age estimation is needed when applied in Black Americans and Chinese people based on pulp chamber volume of the first molars from CBCT.

Du Han, Li Gang, Zheng Qiang, Yang Jie

2022-Jan-19

Age estimation, Cone beam CT, Forensic anthropology, Pulp chamber volume

Radiology Radiology

MRI-based radiomics analysis for differentiating phyllodes tumors of the breast from fibroadenomas.

In European radiology ; h5-index 62.0

OBJECTIVES : To evaluate the diagnostic performance of MRI-based radiomics model for differentiating phyllodes tumors of the breast from fibroadenomas.

METHODS : This retrospective study included 88 patients (32 with phyllodes tumors and 56 with fibroadenomas) who underwent MRI. Radiomic features were extracted from T2-weighted image, pre-contrast T1-weighted image, and the first-phase and late-phase dynamic contrast-enhanced MRIs. To create stable machine learning models and balanced classes, data augmentation was performed. A least absolute shrinkage and selection operator (LASSO) regression was performed to select features and build the radiomics model. A radiological model was constructed from conventional MRI features evaluated by radiologists. A combined model was constructed using both radiomics features and radiological features. Machine learning classifications were done using support vector machine, extreme gradient boosting, and random forest. The area under the receiver operating characteristic (ROC) curve (AUC) was computed to assess the performance of each model.

RESULTS : Among 1070 features, the LASSO logistic regression selected 35 features. Among three machine learning classifiers, support vector machine had the best performance. Compared to the radiological model (AUC: 0.77 ± 0.11), the radiomics model (AUC: 0.96 ± 0.04) and combined model (0.97 ± 0.03) had significantly improved AUC values (both p < 0.01) in the validation set. The combined model had a relatively higher AUC than that of the radiomics model in the validation set, but this was not significantly different (p = 0.391).

CONCLUSIONS : Radiomics analysis based on MRI showed promise for discriminating phyllodes tumors from fibroadenomas.

KEY POINTS : • The radiomics model and the combined model were superior to the radiological model for differentiating phyllodes tumors from fibroadenomas. • The SVM classifier performed best in the current study. • MRI-based radiomics model could help accurately differentiate phyllodes tumors from fibroadenomas.

Tsuchiya Mitsuteru, Masui Takayuki, Terauchi Kazuma, Yamada Takahiro, Katyayama Motoyuki, Ichikawa Shintaro, Noda Yoshifumi, Goshima Satoshi

2022-Jan-19

Breast, Fibroadenoma, Machine learning, Magnetic resonance imaging, Phyllodes tumor

Ophthalmology Ophthalmology

Archetypal Analysis Reveals Quantifiable Patterns of Visual Field Loss in Optic Neuritis.

In Translational vision science & technology

Purpose : Identifying and monitoring visual field (VF) defects due to optic neuritis (ON) relies on qualitative clinician interpretation. Archetypal analysis (AA), a form of unsupervised machine learning, is used to quantify VF defects in glaucoma. We hypothesized that AA can identify quantifiable, ON-specific patterns (as archetypes [ATs]) of VF loss that resemble known ON VF defects.

Methods : We applied AA to a dataset of 3892 VFs prospectively collected from 456 eyes in the Optic Neuritis Treatment Trial (ONTT), and decomposed each VF into component ATs (total weight = 100%). AA of 568 VFs from 61 control eyes was used to define a minimum meaningful (≤7%) AT weight and weight change. We correlated baseline ON AT weights with global VF indices, visual acuity, and contrast sensitivity. For eyes with a dominant AT (weight ≥50%), we compared the ONTT VF classification with the AT pattern.

Results : AA generated a set of 16 ATs containing patterns seen in the ONTT. These were distinct from control ATs. Baseline study eye VFs were decomposed into 2.9 ± 1.5 ATs. AT2, a global dysfunction pattern, had the highest mean weight at baseline (36%; 95% confidence interval, 33%-40%), and showed the strongest correlation with MD (r = -0.91; P < 0.001), visual acuity (r = 0.70; P < 0.001), and contrast sensitivity (r = -0.77; P < 0.001). Of 191 baseline VFs with a dominant AT, 81% matched the descriptive classifications.

Conclusions : AA identifies and quantifies archetypal, ON-specific patterns of VF loss.

Translational Relevance : AA is a quantitative, objective method for demonstrating and monitoring change in regional VF deficits in ON.

Solli Elena, Doshi Hiten, Elze Tobias, Pasquale Louis, Wall Michael, Kupersmith Mark

2022-Jan-03

Cardiology Cardiology

Machine Learning for Cardiovascular Outcomes From Wearable Data: Systematic Review From a Technology Readiness Level Point of View.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : Wearable technology has the potential to improve cardiovascular health monitoring by using machine learning. Such technology enables remote health monitoring and allows for the diagnosis and prevention of cardiovascular diseases. In addition to the detection of cardiovascular disease, it can exclude this diagnosis in symptomatic patients, thereby preventing unnecessary hospital visits. In addition, early warning systems can aid cardiologists in timely treatment and prevention.

OBJECTIVE : This study aims to systematically assess the literature on detecting and predicting outcomes of patients with cardiovascular diseases by using machine learning with data obtained from wearables to gain insights into the current state, challenges, and limitations of this technology.

METHODS : We searched PubMed, Scopus, and IEEE Xplore on September 26, 2020, with no restrictions on the publication date and by using keywords such as "wearables," "machine learning," and "cardiovascular disease." Methodologies were categorized and analyzed according to machine learning-based technology readiness levels (TRLs), which score studies on their potential to be deployed in an operational setting from 1 to 9 (most ready).

RESULTS : After the removal of duplicates, application of exclusion criteria, and full-text screening, 55 eligible studies were included in the analysis, covering a variety of cardiovascular diseases. We assessed the quality of the included studies and found that none of the studies were integrated into a health care system (TRL<6), prospective phase 2 and phase 3 trials were absent (TRL<7 and 8), and group cross-validation was rarely used. These issues limited these studies' ability to demonstrate the effectiveness of their methodologies. Furthermore, there seemed to be no agreement on the sample size needed to train these studies' models, the size of the observation window used to make predictions, how long participants should be observed, and the type of machine learning model that is suitable for predicting cardiovascular outcomes.

CONCLUSIONS : Although current studies show the potential of wearables to monitor cardiovascular events, their deployment as a diagnostic or prognostic cardiovascular clinical tool is hampered by the lack of a realistic data set and proper systematic and prospective evaluation.

Naseri Jahfari Arman, Tax David, Reinders Marcel, van der Bilt Ivo

2022-Jan-19

cardiovascular disease, digital health, mHealth, machine learning, mobile phone, review, wearable

oncology Oncology

Toward Data-Driven Radiation Oncology Using Standardized Terminology as a Starting Point: Cross-sectional Study.

In JMIR formative research

BACKGROUND : The inability to seamlessly exchange information across radiation therapy ecosystems is a limiting factor in the pursuit of data-driven clinical practice. The implementation of semantic interoperability is a prerequisite for achieving the full capacity of the latest developments in personalized and precision medicine, such as mathematical modeling, advanced algorithmic information processing, and artificial intelligence approaches.

OBJECTIVE : This study aims to evaluate the state of terminology resources (TRs) dedicated to radiation oncology as a prerequisite for an oncology semantic ecosystem. The goal of this cross-sectional analysis is to quantify the state of the art in radiation therapy specific terminology.

METHODS : The Unified Medical Language System (UMLS) was searched for the following terms: radio oncology, radiation oncology, radiation therapy, and radiotherapy. We extracted 6509 unique concepts for further analysis. We conducted a quantitative analysis of available source vocabularies (SVs) and analyzed all UMLS SVs according to the route source, number, author, location of authors, license type, the lexical density of TR, and semantic types. Descriptive data are presented as numbers and percentages.

RESULTS : The concepts were distributed across 35 SVs. The median number of unique concepts per SV was 5 (range 1-5479), with 14% (5/35) of SVs containing 94.59% (6157/6509) of the concepts. The SVs were created by 29 authors, predominantly legal entities registered in the United States (25/35, 71%), followed by international organizations (6/35, 17%), legal entities registered in Australia (2/35, 6%), and the Netherlands and the United Kingdom with 3% (1/35) of authors each. Of the total 35 SVs, 16 (46%) did not have any restrictions on use, whereas for 19 (54%) of SVs, some level of restriction was required. Overall, 57% (20/35) of SVs were updated within the last 5 years. All concepts found within radiation therapy SVs were labeled with one of the 29 semantic types represented within UMLS. After removing the stop words, the total number of words for all SVs together was 56,219, with a median of 25 unique words per SV (range 3-50,682). The total number of unique words in all SVs was 1048, with a median of 19 unique words per vocabulary (range 3-406). The lexical density for all concepts within all SVs was 0 (0.02 rounded to 2 decimals). Median lexical density per unique SV was 0.7 (range 0.0-1.0). There were no dedicated radiation therapy SVs.

CONCLUSIONS : We did not identify any dedicated TRs for radiation oncology. Current terminologies are not sufficient to cover the need of modern radiation oncology practice and research. To achieve a sufficient level of interoperability, of the creation of a new, standardized, universally accepted TR dedicated to modern radiation therapy is required.

Cihoric Nikola, Badra Eugenia Vlaskou, Stenger-Weisser Anna, Aebersold Daniel M, Pavic Matea

2022-Jan-19

eHealth, informatics, lexical analysis, medical informatics, oncology, radiation oncology, semantic interoperability, terminology

General General

A Clinical Decision Support System for Sleep Staging Tasks With Explanations From Artificial Intelligence: User-Centered Design and Evaluation Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Despite the unprecedented performance of deep learning algorithms in clinical domains, full reviews of algorithmic predictions by human experts remain mandatory. Under these circumstances, artificial intelligence (AI) models are primarily designed as clinical decision support systems (CDSSs). However, from the perspective of clinical practitioners, the lack of clinical interpretability and user-centered interfaces hinders the adoption of these AI systems in practice.

OBJECTIVE : This study aims to develop an AI-based CDSS for assisting polysomnographic technicians in reviewing AI-predicted sleep staging results. This study proposed and evaluated a CDSS that provides clinically sound explanations for AI predictions in a user-centered manner.

METHODS : Our study is based on a user-centered design framework for developing explanations in a CDSS that identifies why explanations are needed, what information should be contained in explanations, and how explanations can be provided in the CDSS. We conducted user interviews, user observation sessions, and an iterative design process to identify three key aspects for designing explanations in the CDSS. After constructing the CDSS, the tool was evaluated to investigate how the CDSS explanations helped technicians. We measured the accuracy of sleep staging and interrater reliability with macro-F1 and Cohen κ scores to assess quantitative improvements after our tool was adopted. We assessed qualitative improvements through participant interviews that established how participants perceived and used the tool.

RESULTS : The user study revealed that technicians desire explanations that are relevant to key electroencephalogram (EEG) patterns for sleep staging when assessing the correctness of AI predictions. Here, technicians wanted explanations that could be used to evaluate whether the AI models properly locate and use these patterns during prediction. On the basis of this, information that is closely related to sleep EEG patterns was formulated for the AI models. In the iterative design phase, we developed a different visualization strategy for each pattern based on how technicians interpreted the EEG recordings with these patterns during their workflows. Our evaluation study on 9 polysomnographic technicians quantitatively and qualitatively investigated the helpfulness of the tool. For technicians with <5 years of work experience, their quantitative sleep staging performance improved significantly from 56.75 to 60.59 with a P value of .05. Qualitatively, participants reported that the information provided effectively supported them, and they could develop notable adoption strategies for the tool.

CONCLUSIONS : Our findings indicate that formulating clinical explanations for automated predictions using the information in the AI with a user-centered design process is an effective strategy for developing a CDSS for sleep staging.

Hwang Jeonghwan, Lee Taeheon, Lee Honggu, Byun Seonjeong

2022-Jan-19

clinical decision support, medical artificial intelligence, sleep staging, user-centered design

Public Health Public Health

Engagement With a Mobile Phone-Based Life Skills Intervention for Adolescents and Its Association With Participant Characteristics and Outcomes: Tree-Based Analysis.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Mobile phone-delivered life skills programs are an emerging and promising way to promote mental health and prevent substance use among adolescents, but little is known about how adolescents actually use them.

OBJECTIVE : The aim of this study is to determine engagement with a mobile phone-based life skills program and its different components, as well as the associations of engagement with adolescent characteristics and intended substance use and mental health outcomes.

METHODS : We performed secondary data analysis on data from the intervention group (n=750) from a study that compared a mobile phone-based life skills intervention for adolescents recruited in secondary and upper secondary school classes with an assessment-only control group. Throughout the 6-month intervention, participants received 1 SMS text message prompt per week that introduced a life skills topic or encouraged participation in a quiz or individual life skills training or stimulated sharing messages with other program participants through a friendly contest. Decision trees were used to identify predictors of engagement (use and subjective experience). The stability of these decision trees was assessed using a resampling method and by graphical representation. Finally, associations between engagement and intended substance use and mental health outcomes were examined using logistic and linear regression analyses.

RESULTS : The adolescents took part in half of the 50 interactions (mean 23.6, SD 15.9) prompted by the program, with SMS text messages being the most used and contests being the least used components. Adolescents who did not drink in a problematic manner and attended an upper secondary school were the ones to use the program the most. Regarding associations between engagement and intended outcomes, adolescents who used the contests more frequently were more likely to be nonsmokers at follow-up than those who did not (odds ratio 0.86, 95% CI 0.76-0.98; P=.02). In addition, adolescents who read the SMS text messages more attentively were less likely to drink in a problematic manner at follow-up (odds ratio 0.43, 95% CI 1.29-3.41; P=.003). Finally, participants who used the program the most and least were more likely to increase their well-being from baseline to 6-month follow-up compared with those with average engagement (βs=.39; t586=2.66; P=.008; R2=0.24).

CONCLUSIONS : Most of the adolescents participating in a digital life skills program that aimed to prevent substance use and promote mental health engaged with the intervention. However, measures to increase engagement in problem drinkers should be considered. Furthermore, efforts must be made to ensure that interventions are engaging and powerful across different educational levels. First results indicate that higher engagement with digital life skills programs could be associated with intended outcomes. Future studies should apply further measures to improve the reach of lower-engaged participants at follow-up to establish such associations with certainty.

Paz Castro Raquel, Haug Severin, Debelak Rudolf, Jakob Robert, Kowatsch Tobias, Schaub Michael P

2022-Jan-19

adolescents, decision tree, engagement, life skills, machine learning, mobile phone

General General

EMD-WOG-2DCNN based EEG signal processing for Rolandic seizure classification.

In Computer methods in biomechanics and biomedical engineering

Objective Approximately 65 million people have epilepsy around the world. Recognition of epilepsy types is the basis to determine the treatment method and predict the prognosis in epilepsy patients. Childhood benign epilepsy with centrotemporal spikes (BECTS) or benign Rolandic epilepsy is the most common focal epilepsy in children, accounting for 15-20% of childhood epilepsies. These EEG patterns of individuals usually predict good treatment responses and prognosis. Until now, the interpretation of EEG still depends entirely on experienced neurologists, which may be a lengthy and tedious task. Method In this article, we proposed a novel machine learning model that efficiently distinguished Rolandic seizures from normal EEG signals. The proposed machine learning model processes the identification procedure in the following order (1) creating preliminary EEG features using signal empirical mode decomposition, (2) applying weighted overlook graph (WOG) to represent the decomposed EMD of IMF, and (3) classifying the results through a two Dimensional Convolutional Neural Network (2DCNN). The performance of our classification model is compared with other representative machine learning models. Results The model offered in this article gains an accuracy performance exceeding 97.6% in the Rolandic dataset, which is higher than other classification models. The effect of the model on the Bonn public dataset is also comparable to existing methods and even performs better in some subsets. Conclusion The purpose of this study is to introduce the most common childhood benign epilepsy type and propose a model that meets the real clinical needs to distinguish this Rolandic EEG pattern from normal signals accurately. Significance Future research will optimize the model to categorize other types of epilepsies beyond BECTS and finally implement them in the hospital system.

Luo Tian, Wang Jialin, Zhou Yuanfeng, Zhou Shuizhen, Hu Chunhui, Yao Peili, Zhang Yanjiong, Wang Yi

2022-Jan-19

BECTS, Rolandic seizure, complex network, convolutional neural network, electroencephalogram, empirical mode decomposition, epilepsy

Public Health Public Health

A machine learning approach to predict e-cigarette use and dependence among Ontario youth.

In Health promotion and chronic disease prevention in Canada : research, policy and practice

INTRODUCTION : We developed separate random forest algorithms to predict e-cigarette (vaping) ever use and daily use among Ontario youth, and subsequently examined predictor importance and statistical interaction.

METHODS : This cross-sectional study used a representative sample of Ontario elementary and high school students in 2019 (N = 6471). Vaping frequency over the last 12 months was used to define ever-vaping and daily vaping. We considered a large set of individual characteristics as potential correlates for ever-vaping (176 variables) and daily vaping (179 variables). Using cross-validation, we developed random forest algorithms and evaluated model performance based on the C-index, a measure to assess the discriminatory ability of a model, for both outcomes. Further, the top 10 correlates were identified by relative importance score calculation and their interaction with sociodemographic characteristics.

RESULTS : There were 2064 (31.9%) ever-vapers, and 490 (7.6%) of the respondents were daily users. The random forest algorithms for both outcomes achieved high performance, with C-index over 0.90. The top 10 correlates of daily vaping included use of caffeine, cannabis and tobacco, source and type of e-cigarette and absence in last 20 school days. Those of ever-vaping included school size, use of alcohol, cannabis and tobacco; 9 of the top 10 ever-vaping correlates demonstrated interactions with ethnicity.

CONCLUSION : Machine learning is a promising methodology for identifying the risks of ever-vaping and daily vaping. Furthermore, it enables the identification of important correlates and the assessment of complex intersections, which may inform future longitudinal studies to customize public health policies for targeted population subgroups.

Shi Jiamin, Fu Rui, Hamilton Hayley, Chaiton Michael

2022-Jan

Ontario, machine learning, smoking, vaping, youth

Surgery Surgery

Identification of a glioma functional network from gene fitness data using machine learning.

In Journal of cellular and molecular medicine

Glioblastoma multiforme (GBM) is an aggressive form of brain tumours that remains incurable despite recent advances in clinical treatments. Previous studies have focused on sub-categorizing patient samples based on clustering various transcriptomic data. While functional genomics data are rapidly accumulating, there exist opportunities to leverage these data to decipher glioma-associated biomarkers. We sought to implement a systematic approach to integrating data from high throughput CRISPR-Cas9 screening studies with machine learning algorithms to infer a glioma functional network. We demonstrated the network significantly enriched various biological pathways and may play roles in glioma tumorigenesis. From densely connected glioma functional modules, we further predicted 12 potential Wnt/β-catenin signalling pathway targeted genes, including AARSD1, HOXB5, ITGA6, LRRC71, MED19, MED24, METTL11B, SMARCB1, SMARCE1, TAF6L, TENT5A and ZNF281. Cox regression modelling with these targets was significantly associated with glioma overall survival prognosis. Additionally, TRIB2 was identified as a glioma neoplastic cell marker in single-cell RNA-seq of GBM samples. This work establishes novel strategies for constructing functional networks to identify glioma biomarkers for the development of diagnosis and treatment in clinical practice.

Xiang Chun-Xiang, Liu Xi-Guo, Zhou Da-Quan, Zhou Yi, Wang Xu, Chen Feng

2022-Jan-19

CRISPR-Cas9, co-functional network, glioma, prognostic biomarkers, scRNA-seq

Dermatology Dermatology

CellSeg: a robust, pre-trained nucleus segmentation and pixel quantification software for highly multiplexed fluorescence images.

In BMC bioinformatics

BACKGROUND : Algorithmic cellular segmentation is an essential step for the quantitative analysis of highly multiplexed tissue images. Current segmentation pipelines often require manual dataset annotation and additional training, significant parameter tuning, or a sophisticated understanding of programming to adapt the software to the researcher's need. Here, we present CellSeg, an open-source, pre-trained nucleus segmentation and signal quantification software based on the Mask region-convolutional neural network (R-CNN) architecture. CellSeg is accessible to users with a wide range of programming skills.

RESULTS : CellSeg performs at the level of top segmentation algorithms in the 2018 Kaggle Data Challenge both qualitatively and quantitatively and generalizes well to a diverse set of multiplexed imaged cancer tissues compared to established state-of-the-art segmentation algorithms. Automated segmentation post-processing steps in the CellSeg pipeline improve the resolution of immune cell populations for downstream single-cell analysis. Finally, an application of CellSeg to a highly multiplexed colorectal cancer dataset acquired on the CO-Detection by indEXing (CODEX) platform demonstrates that CellSeg can be integrated into a multiplexed tissue imaging pipeline and lead to accurate identification of validated cell populations.

CONCLUSION : CellSeg is a robust cell segmentation software for analyzing highly multiplexed tissue images, accessible to biology researchers of any programming skill level.

Lee Michael Y, Bedia Jacob S, Bhate Salil S, Barlow Graham L, Phillips Darci, Fantl Wendy J, Nolan Garry P, Schürch Christian M

2022-Jan-18

CODEX, Deep learning, Image analysis, Mask R-CNN, Multiplexed imaging, Pre-trained model, Segmentation

General General

The influence of fake accounts on sentiment analysis related to COVID-19 in Indonesia.

In Procedia computer science

Cases of the spread of COVID-19 that continue to increase in Indonesia have made the level of public satisfaction with the government in dealing with this virus fairly low. One way to measure the level of community satisfaction is by analyzing social media. Sentiment analysis can be used to analyze feedback from the public. Research related to sentiment analysis has been mostly carried out, but so far, it has focused more on opinions contained in sentences and comments and has not considered the subject of the account that posted it. On the other hand, the use of fake accounts or bots on social media is becoming more and more prevalent, so that the credibility of opinion makers is reduced. Based on these problems, this research conducted several experiments related to sentiment analysis using a machine learning approach and fake account categories to see the influence of fake accounts on sentiment analysis. The data used in this research were taken from social media Twitter. The results showed that there was an influence from fake accounts that can reduce the performance of sentiment classification. The experimental results of the two algorithms also prove that the Support Vector Machine algorithm has a better performance than the Naïve-Bayes algorithm for this case with the highest Accuracy value of 80.6%. In addition, the results of the sentiment visualization showed that there was an influence from fake accounts which actually leads to positive sentiment although it is not significant.

Pratama Rivanda Putra, Tjahyanto Aris

2022

Sentiment analysis, fake account, machine learning, twitter

General General

GAN-based Matrix Factorization for Recommender Systems

ArXiv Preprint

Proposed in 2014, Generative Adversarial Networks (GAN) initiated a fresh interest in generative modelling. They immediately achieved state-of-the-art in image synthesis, image-to-image translation, text-to-image generation, image inpainting and have been used in sciences ranging from medicine to high-energy particle physics. Despite their popularity and ability to learn arbitrary distributions, GAN have not been widely applied in recommender systems (RS). Moreover, only few of the techniques that have introduced GAN in RS have employed them directly as a collaborative filtering (CF) model. In this work we propose a new GAN-based approach that learns user and item latent factors in a matrix factorization setting for the generic top-N recommendation problem. Following the vector-wise GAN training approach for RS introduced by CFGAN, we identify 2 unique issues when utilizing GAN for CF. We propose solutions for both of them by using an autoencoder as discriminator and incorporating an additional loss function for the generator. We evaluate our model, GANMF, through well-known datasets in the RS community and show improvements over traditional CF approaches and GAN-based models. Through an ablation study on the components of GANMF we aim to understand the effects of our architectural choices. Finally, we provide a qualitative evaluation of the matrix factorization performance of GANMF.

Ervin Dervishaj, Paolo Cremonesi

2022-01-20

General General

Machine learning and magnetic resonance imaging radiomics for predicting human papilloma virus status and prognostic factors in oropharyngeal squamous cell carcinoma.

In Head & neck ; h5-index 50.0

BACKGROUND : We attempted to predict pathological factors and treatment outcomes using machine learning and radiomic features extracted from preoperative magnetic resonance imaging (MRI) of oropharyngeal squamous cell carcinoma (OPSCC) patients.

METHODS : The medical records and imaging data of 155 patients who were diagnosed with OPSCC were analyzed retrospectively.

RESULTS : The logistic regression model showed that the area under the receiver operating characteristic curve (AUC) of the model was 0.792 in predicting human papilloma virus (HPV) status. The LightGBM model showed an AUC of 0.8333 in predicting HPV status. The performance of the logistic model in predicting lymphovascular invasion, extracapsular nodal spread, and metastatic lymph nodes showed AUC values of 0.7871, 0.6713, and 0.6638, respectively. In predicting disease recurrence, the LightGBM model showed an AUC of 0.8571. In predicting patient death, the logistic model showed an AUC of 0.8175.

CONCLUSIONS : A machine learning model using MRI radiomics showed satisfactory performance in predicting pathologic factors and treatment outcomes of OPSCC patients.

Park Young Min, Lim Jae-Yol, Koh Yoon Woo, Kim Se-Heon, Choi Eun Chang

2022-Jan-19

HPV, MRI, extracapsular nodal spread, lymphovascular invasion, machine learning, oropharyngeal squamous cell carcinoma, radiomics

General General

High-accuracy, direct aberration determination using self-attention-armed deep convolutional neural networks.

In Journal of microscopy

Optical microscopes have long been essential for many scientific disciplines. However, the resolution and contrast of such microscopic images are dramatically affected by aberrations. In this study, compacted with adaptive optics, we propose a machine learning technique, called the "phase-retrieval deep convolutional neural networks (PRDCNNs)". This aberration determination architecture is direct and exhibits high accuracy and certain generalization ability. Notably, its performance surpasses those of similar, existing methods, with fewer fluctuations and greater robustness against noise. We anticipate future application of the proposed PRDCNNs to super-resolution microscopes. This article is protected by copyright. All rights reserved.

Wang Yangyundou, Wang Hao, Li Yiming, Hu Chuanfei, Yang Hui, Gu Min

2022-Jan-19

Aberration determination, deep learning, self-attention mechanism

oncology Oncology

A hybrid optimization strategy for deliverable Intensity-modulated radiotherapy plan generation using deep learning-based dose prediction.

In Medical physics ; h5-index 59.0

PURPOSE : To propose a clinically feasible automatic planning solution for external beam intensity-modulated radiotherapy (IMRT) including dose prediction via a deep learning and voxel-based optimization strategy.

MATERIALS AND METHODS : The dose distribution of patients was predicted using a U-Net-based deep learning network based on the patient's anatomy information. One hundred seventeen patients with nasopharyngeal cancer (NPC) and 200 patients with rectal cancer were enrolled in this study. For NPC cases, 94 cases were included in the training dataset, 13 in the validation dataset and 10 in the testing dataset. For rectal cancer cases, 172 cases were included in the training set, 18 in the validation set and 10 in the testing set. A voxel-based optimization strategy, "Voxel", was proposed to achieve treatment planning optimization by dividing body voxels into two parts: inside planning target volumes (PTVs) and outside PTVs. Fixed dose-volume objectives were attached to the total objective function to realize individualized planning intended as the "hybrid" optimizing strategy. Automatically generated plans were compared with clinically approved plans to evaluate clinical gains, according to dosimetric indices and dose-volume histograms (DVHs).

RESULTS : Similarities were found between the DVH of the predicted dose and clinical plan, although significant differences were found in some organs at risk (OARs). Better organ sparing and suboptimal PTV coverage were shown using the voxel strategy; however, the deviation in homogeneity indices (HI) and conformity indices (CI) of the PTV between automatically generated plans and manual plans were reduced by the hybrid strategy ((Manual plans)/(Voxel plans)/(Hybrid plans): HI of PTV70 (1.06/1.12/1.02), CI of PTV70 (0.79/0.58/0.76)). The optimization time for each patient was within 1 minute and included fluence map optimization, leaf sequencing and control point optimization. All the generated plans (voxel & hybrid strategy) could be delivered on uRT-linac 506c (United Imaging Health care, Shanghai, China).

CONCLUSION : Deliverable plans can be generated by incorporating a voxel-based optimization strategy into a commercial TPS. The hybrid optimization method shows the benefit and clinical feasibility in generating clinically acceptable plans. This article is protected by copyright. All rights reserved.

Sun Zihan, Xia Xiang, Fan Jiawei, Zhao Jun, Zhang Kang, Wang Jiazhou, Hu Weigang

2022-Jan-19

IMRT auto-planning, NPC and rectal cancer, deep learning, hybrid objective function, voxel-based plan optimization

General General

BACPI: a bi-directional attention neural network for compound-protein interaction and binding affinity prediction.

In Bioinformatics (Oxford, England)

MOTIVATION : The identification of compound-protein interactions (CPIs) is an essential step in the process of drug discovery. The experimental determination of CPIs is known for a large amount of funds and time it consumes. Computational model has therefore become a promising and efficient alternative for predicting novel interactions between compounds and proteins on a large scale. Most supervised machine learning prediction models are approached as a binary classification problem, which aim to predict whether there is an interaction between the compound and the protein or not. However, compound-protein interaction is not a simple binary on-off relationship, but a continuous value reflects how tightly the compound binds to a particular target protein, also called binding affinity.

RESULTS : In this study, we propose an end-to-end neural network model, called BACPI, to predict compound-protein interaction and binding affinity. We employ graph attention network (GAT) and convolutional neural network (CNN) to learn the representations of compounds and proteins, and develop a bi-directional attention neural network model to integrate the representations. To evaluate the performance of BACPI, we use three CPI datasets and four binding affinity datasets in our experiments. The results show that, when predicting CPIs, BACPI significantly outperforms other available machine learning methods on both balanced and unbalanced datasets. This suggests that the end-to-end neural network model that predicts CPIs directly from low level representations is more robust than traditional machine learning-based methods. And when predicting binding affinities, BACPI achieves higher performance on large datasets compared to other state-of-the-art deep learning methods. This comparison result suggests that the proposed method with bi-directional attention neural network can capture the important regions of compounds and proteins for binding affinity prediction.

AVAILABILITY AND IMPLEMENTATION : Data and source codes are available at https://github.com/CSUBioGroup/BACPI.

SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.

Li Min, Lu Zhangli, Wu Yifan, Li YaoHang

2022-Jan-19

General General

Deep-learning approach for automated thickness measurement of epithelial tissue and scab using optical coherence tomography.

In Journal of biomedical optics

SIGNIFICANCE : In order to elucidate therapeutic treatment to accelerate wound healing, it is crucial to understand the process underlying skin wound healing, especially re-epithelialization. Epidermis and scab detection is of importance in the wound healing process as their thickness is a vital indicator to judge whether the re-epithelialization process is normal or not. Since optical coherence tomography (OCT) is a real-time and non-invasive imaging technique that can perform a cross-sectional evaluation of tissue microstructure, it is an ideal imaging modality to monitor the thickness change of epidermal and scab tissues during wound healing processes in micron-level resolution. Traditional segmentation on epidermal and scab regions was performed manually, which is time-consuming and impractical in real time.

AIM : We aim to develop a deep-learning-based skin layer segmentation method for automated quantitative assessment of the thickness of in vivo epidermis and scab tissues during a time course of healing within a rodent model.

APPROACH : Five convolution neural networks were trained using manually labeled epidermis and scab regions segmentation from 1000 OCT B-scan images (assisted by its corresponding angiographic information). The segmentation performance of five segmentation architectures was compared qualitatively and quantitatively for validation set.

RESULTS : Our results show higher accuracy and higher speed of the calculated thickness compared with human experts. The U-Net architecture represents a better performance than other deep neural network architectures with 0.894 at F1-score, 0.875 at mean intersection over union, 0.933 at Dice similarity coefficient, and 18.28  μm at an average symmetric surface distance. Furthermore, our algorithm is able to provide abundant quantitative parameters of the wound based on its corresponding thickness maps in different healing phases. Among them, normalized epidermal thickness is recommended as an essential hallmark to describe the re-epithelialization process of the rodent model.

CONCLUSIONS : The automatic segmentation and thickness measurements within different phases of wound healing data demonstrates that our pipeline provides a robust, quantitative, and accurate method for serving as a standard model for further research into effect of external pharmacological and physical factors.

Ji Yubo, Yang Shufan, Zhou Kanheng, Rocliffe Holly R, Pellicoro Antonella, Cash Jenna L, Wang Ruikang, Li Chunhui, Huang Zhihong

2022-Jan

deep-learning network, epidermis, optical coherence tomography, re-epithelialization, scab, wound healing

General General

The Impact of an Integrated Care Management Program on Acute Care Use and Outpatient Appointment Attendance Among High-Risk Patients With Lupus.

In ACR open rheumatology

OBJECTIVE : Patients with systemic lupus erythematosus (SLE) often struggle with high acute care use (emergency department [ED] visits and hospitalizations) and missed appointments. A nurse-led integrated care management program (iCMP) at our multihospital system coordinates care for patients at high risk for frequent acute care use due to comorbidities, demographics, and prior use patterns. We studied whether iCMP enrollment was associated with decreased acute care use and missed appointment rates among patients with SLE.

METHODS : We used a validated electronic health record (EHR) machine learning algorithm to identify adults with SLE and then determined which patients were enrolled in the iCMP from January 2012 to February 2019. We then used EHR data linked to insurance claims to compare the incidence rates of ED visits, hospitalizations, potentially avoidable ED visits and hospitalizations, and missed appointments during iCMP enrollment versus the 12 months prior to iCMP enrollment. We used Poisson regression to compare incidence rate ratios (IRRs) during the iCMP versus pre-iCMP for each use measure, adjusted for age, sex, race and ethnicity, number of comorbidities, and calendar year, accounting for within-patient clustering.

RESULTS : We identified 67 iCMP enrollees with SLE and linked EHR claims data. In adjusted analyses, iCMP enrollment was associated with reduced rates of ED visits (IRR 0.63, 95% confidence interval [CI] 0.47-0.85), avoidable ED visits (IRR 0.50, 95% CI 0.28-0.88), and avoidable hospitalizations (IRR 0.37, 95% CI 0.21-0.65).

CONCLUSION : A nurse-led iCMP was effective at decreasing the rate of all ED visits and potentially avoidable ED visits and hospitalizations among high-risk patients with SLE. Further studies are needed to confirm these findings in other patient populations.

Williams Jessica N, Taber Kreager, Huang Weixing, Collins Jamie, Cunningham Rebecca, McLaughlin Katherine, Vogeli Christine, Wichmann Lisa, Feldman Candace H

2022-Jan-18

General General

The utility of DNA methylation signatures in directing genome sequencing workflow: Kabuki syndrome and CDK13-related disorder.

In American journal of medical genetics. Part A

Kabuki syndrome (KS) is a neurodevelopmental disorder characterized by hypotonia, intellectual disability, skeletal anomalies, and postnatal growth restriction. The characteristic facial appearance is not pathognomonic for KS as several other conditions demonstrate overlapping features. For 20-30% of children with a clinical diagnosis of KS, no causal variant is identified by conventional genetic testing of the two associated genes, KMT2D and KDM6A. Here, we describe two cases of suspected KS that met clinical diagnostic criteria and had a high gestalt match on the artificial intelligence platform Face2Gene. Although initial KS testing was negative, genome-wide DNA methylation (DNAm) was instrumental in guiding genome sequencing workflow to establish definitive molecular diagnoses. In one case, a positive DNAm signature for KMT2D led to the identification of a cryptic variant in KDM6A by genome sequencing; for the other case, a DNAm signature different from KS led to the detection of another diagnosis in the KS differential, CDK13-related disorder. This approach illustrates the clinical utility of DNAm signatures in the diagnostic workflow for the genome analyst or clinical geneticist-especially for disorders with overlapping clinical phenotypes.

Marwaha Ashish, Costain Gregory, Cytrynbaum Cheryl, Mendoza-Londano Roberto, Chad Lauren, Awamleh Zain, Chater-Diehl Eric, Choufani Sanaa, Weksberg Rosanna

2022-Jan-18

CDK13, DNA methylation signature, KDM6A, KMT2D, Kabuki syndrome

Public Health Public Health

Breast microscopic cancer segmentation and classification using unique 4-qubit-quantum model.

In Microscopy research and technique

The visual inspection of histopathological samples is the benchmark for detecting breast cancer, but a strenuous and complicated process takes a long time of the pathologist practice. Deep learning models have shown excellent outcomes in clinical diagnosis and image processing and advances in various fields, including drug development, frequency simulation, and optimization techniques. However, the resemblance of histopathologic images of breast cancer and the inclusion of stable and infected tissues in different areas make detecting and classifying tumors on entire slide images more difficult. In breast cancer, a correct diagnosis is needed for complete care in a limited amount of time. An effective detection can relieve the pathologist's workload and mitigate diagnostic subjectivity. Therefore, this research work investigates improved the pre-trained xception and deeplabv3+ design semantic model. The model has been trained on input images with ground masks on the tuned parameters that significantly improve the segmentation of ultrasound breast images into respective classes, that is, benign/malignant. The segmentation model delivered an accuracy of greater than 99% to prove the model's effectiveness. The segmented images and histopathological breast images are transferred to the 4-qubit-quantum circuit with six-layered architecture to detect breast malignancy. The proposed framework achieved remarkable performance as contrasted to currently published methodologies. HIGHLIGHTS: This research proposed hybrid semantic model using pre-trained xception and deeplabv3 for breast microscopic cancer classification in to benign and malignant classes at accuracy of 95% accuracy, 99% accuracy for detection of breast malignancy.

Amin Javaria, Sharif Muhammad, Fernandes Steven Lawrence, Wang Shui-Hua, Saba Tanzila, Khan Amjad Rehman

2022-Jan-18

4-qubit-quantum circuit, ReLU, breast cancer, deeplabv3, health care, public health, xception

Public Health Public Health

Clustering Analysis and Machine Learning algorithms in the prediction of dietary patterns: Cross-sectional results of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil).

In Journal of human nutrition and dietetics : the official journal of the British Dietetic Association

BACKGROUND : Machine learning investigates how computers can automatically learn. This study aimed to predict dietary patterns and compare algorithm performance in making predictions of dietary patterns.

METHODS : We analyzed the data of public employees (n=12,667) participating in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). The K-means clustering algorithm and six other classifiers (support vector machines, naïve Bayes, K-nearest neighbors, decision tree, random forest, and xgboost) were used to predict the dietary patterns.

RESULTS : K-means clustering identified two dietary patterns. Cluster 1, labeled the Western pattern, was characterized by a higher energy intake and consumption of refined cereals, beans and other legumes, tubers, pasta, processed and red meats, high-fat milk and dairy products, and sugary beverages; Cluster 2, labeled the Prudent pattern, was characterized by higher intakes of fruit, vegetables, whole cereals, white meats, and milk and reduced-fat milk derivatives. The most important predictors were age, sex, per capita income, education level, and physical activity. The accuracy of the models varied from moderate to good (69-72%).

CONCLUSIONS : The algorithms' performance in dietary pattern prediction was similar, and the models presented may provide support in screener tasks and guide health professionals in the analysis of dietary data. This article is protected by copyright. All rights reserved.

Silva Vanderlei Carneiro, Gorgulho Bartira, Marchioni Dirce Maria, Araujo Tânia Aparecida de, Santos Itamar de Souza, Lotufo Paulo Andrade, Benseñor Isabela Martins

2022-Jan-18

classification algorithms, clustering analysis, dietary patterns, machine learning

General General

Joint kinematics alone can distinguish hip or knee osteoarthritis patients from asymptomatic controls with high accuracy.

In Journal of orthopaedic research : official publication of the Orthopaedic Research Society

Osteoarthritis (OA) is one of the leading musculoskeletal disabilities worldwide, and several interventions intend to change the gait pattern in OA patients to more healthy patterns. However, an accessible way to follow up the biomechanical changes in a clinical setting is still missing. Therefore, this study aims to evaluate whether we can use biomechanical data collected from a specific activity of daily living to help distinguish hip OA patients from controls and knee OA patients from controls using features that potentially could be measured in a clinical setting. To achieve this goal, we considered three different classes of statistical models with different levels of data complexity. Class 1 is kinematics based only (clinically applicable), class 2 includes joint kinetics (semi-applicable under the condition of access to a force plate or prediction models), and class 3 uses data from advanced musculoskeletal modeling (not clinically applicable). We used a machine learning pipeline to determine which classification model was best. We found 100% classification accuracy for KneeOA-vs-Asymptomatic and 93.9% for HipOA-vs-Asymptomatic using seven features derived from the lumbar spine and hip kinematics collected during ascending stairs. These results indicate that kinematical data alone can distinguish hip or knee OA patients from asymptomatic controls. However, to enable clinical use, we need to validate if the classifier also works with sensor-based kinematical data and whether the probabilistic outcome of the logistic regression model can be used in the follow-up of patients with OA.

Emmerzaal Jill, Van Rossom Sam, van der Straaten Rob, De Brabandere Arne, Corten Kristoff, De Baets Liesbet, Davis Jesse, Jonkers Ilse, Timmermans Annick, Vanwanseele Benedicte

2022-Jan-18

biomechanics, classification model, daily activities, machine learning, osteoarthritis

General General

Using machine learning to detect the differential usage of novel gene isoforms.

In BMC bioinformatics

BACKGROUND : Differential isoform usage is an important driver of inter-individual phenotypic diversity and is linked to various diseases and traits. However, accurately detecting the differential usage of different gene transcripts between groups can be difficult, in particular in less well annotated genomes where the spectrum of transcript isoforms is largely unknown.

RESULTS : We investigated whether machine learning approaches can detect differential isoform usage based purely on the distribution of reads across a gene region. We illustrate that gradient boosting and elastic net approaches can successfully identify large numbers of genes showing potential differential isoform usage between Europeans and Africans, that are enriched among relevant biological pathways and significantly overlap those identified by previous approaches. We demonstrate that diversity at the 3' and 5' ends of genes are primary drivers of these differences between populations.

CONCLUSION : Machine learning methods can effectively detect differential isoform usage from read fraction data, and can provide novel insights into the biological differences between groups.

Zhang Xiaopu, Hassan Musa A, Prendergast James G D

2022-Jan-18

Differential expression, Isoform usage, Machine learning, RNA-seq

General General

A comprehensive study of the COVID-19 impact on PM2.5 levels over the contiguous United States: A deep learning approach.

In Atmospheric environment (Oxford, England : 1994)

We investigate the impact of the COVID-19 outbreak on PM2.5 levels in eleven urban environments across the United States: Washington DC, New York, Boston, Chicago, Los Angeles, Houston, Dallas, Philadelphia, Detroit, Phoenix, and Seattle. We estimate daily PM2.5 levels over the contiguous U.S. in March-May 2019 and 2020, and leveraging a deep convolutional neural network, we find a correlation coefficient, an index of agreement, a mean absolute bias, and a root mean square error of 0.90 (0.90), 0.95 (0.95), 1.34 (1.24) μg/m3, and 2.04 (1.87) μg/m3, respectively. Results from Google Community Mobility Reports and estimated PM2.5 concentrations show a greater reduction of PM2.5 in regions with larger decreases in human mobility and those in which individuals remain in their residential areas longer. The relationship between vehicular PM2.5 (i.e., the ratio of vehicular PM2.5 to other sources of PM2.5) emissions and PM2.5 reductions (R = 0.77) in various regions indicates that regions with higher emissions of vehicular PM2.5 generally experience greater decreases in PM2.5. While most of the urban environments ⸺ Washington DC, New York, Boston, Chicago, Los Angeles, Houston, Dallas, Philadelphia, Detroit, and Seattle ⸺ show a decrease in PM2.5 levels by 21.1%, 20.7%, 18.5%, 8.05%, 3.29%, 3.63%, 6.71%, 4.82%, 13.5%, and 7.73%, respectively, between March-May of 2020 and 2019, Phoenix shows a 5.5% increase during the same period. Similar to their PM2.5 reductions, Washington DC, New York, and Boston, compared to other cities, exhibit the highest reductions in human mobility and the highest vehicular PM2.5 emissions, highlighting the great impact of human activity on PM2.5 changes in eleven regions. Moreover, compared to changes in meteorological factors, changes in pollutant concentrations, including those of black carbon, organic carbon, SO2, SO4, and especially NO2, appear to have had a significantly greater impact on PM2.5 changes during the study period.

Ghahremanloo Masoud, Lops Yannic, Choi Yunsoo, Jung Jia, Mousavinezhad Seyedali, Hammond Davyda

2022-Jan-14

COVID-19, Community multiscale air quality (CMAQ) model, Deep convolutional neural network, Google mobility reports, PM2.5 estimation, United States

General General

Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations

ArXiv Preprint

The widespread adoption of electronic health records (EHRs) and subsequent increased availability of longitudinal healthcare data has led to significant advances in our understanding of health and disease with direct and immediate impact on the development of new diagnostics and therapeutic treatment options. However, access to EHRs is often restricted due to their perceived sensitive nature and associated legal concerns, and the cohorts therein typically are those seen at a specific hospital or network of hospitals and therefore not representative of the wider population of patients. Here, we present HealthGen, a new approach for the conditional generation of synthetic EHRs that maintains an accurate representation of real patient characteristics, temporal information and missingness patterns. We demonstrate experimentally that HealthGen generates synthetic cohorts that are significantly more faithful to real patient EHRs than the current state-of-the-art, and that augmenting real data sets with conditionally generated cohorts of underrepresented subpopulations of patients can significantly enhance the generalisability of models derived from these data sets to different patient populations. Synthetic conditionally generated EHRs could help increase the accessibility of longitudinal healthcare data sets and improve the generalisability of inferences made from these data sets to underrepresented populations.

Simon Bing, Andrea Dittadi, Stefan Bauer, Patrick Schwab

2022-01-20

General General

Value of artificial intelligence with novel tumor tracking technology in the diagnosis of gastric submucosal tumors by contrast-enhanced harmonic endoscopic ultrasonography.

In Journal of gastroenterology and hepatology ; h5-index 51.0

** : BACKGROUND AND AIMS Contrast-enhanced harmonic endoscopic ultrasonography (CH-EUS) is useful for the diagnosis of lesions inside and outside the digestive tract. This study evaluated the value of artificial intelligence (AI) in the diagnosis of gastric submucosal tumors by CH-EUS.

METHODS : This retrospective study included 53 patients with gastrointestinal stromal tumors (GISTs) and leiomyomas, all of whom underwent CH-EUS between June 2015 and February 2020. A novel technology, SiamMask, was used to track and trim the lesions in CH-EUS videos. CH-EUS was evaluated by AI using deep learning involving a residual neural network and leave-one-out cross-validation. The diagnostic accuracy of AI in discriminating between GISTs and leiomyomas was assessed and compared with that of blind reading by two expert endosonographers.

RESULTS : Of the 53 patients, 42 had GISTs and 11 had leiomyomas. Mean tumor size was 26.4 mm. The consistency rate of the segment range of the tumor image extracted by SiamMask and marked by the endosonographer was 96% with a Dice coefficient. The sensitivity, specificity, and accuracy of AI in diagnosing GIST were 90.5%, 90.9%, and 90.6%, respectively, whereas those of blind reading were 90.5%, 81.8%, and 88.7%, respectively (P = 0.683). The κ -coefficient between the two reviewers was 0.713.

CONCLUSIONS : The diagnostic ability of CH-EUS results evaluated by AI to distinguish between GISTs and leiomyomas was comparable with that of blind reading by expert endosonographers.

Tanaka Hidekazu, Kamata Ken, Ishihara Rika, Handa Hisashi, Otsuka Yasuo, Yoshida Akihiro, Yoshikawa Tomoe, Ishikawa Rei, Okamoto Ayana, Yamazaki Tomohiro, Nakai Atsushi, Omoto Shunsuke, Minaga Kosuke, Yamao Kentaro, Takenaka Mamoru, Watanabe Tomohiro, Nishida Naoshi, Kudo Masatoshi

2022-Jan-18

artificial intelligences, contrast-enhanced harmonic endoscopic ultrasonography, endoscopic ultrasonography, gastrointestinal stromal tumor, neural network, submucosal tumor

Internal Medicine Internal Medicine

Classification of rotator cuff tears in ultrasound images using deep learning models.

In Medical & biological engineering & computing ; h5-index 32.0

Rotator cuff tears (RCTs) are one of the most common shoulder injuries, which are typically diagnosed using relatively expensive and time-consuming diagnostic imaging tests such as magnetic resonance imaging or computed tomography. Deep learning algorithms are increasingly used to analyze medical images, but they have not been used to identify RCTs with ultrasound images. The aim of this study is to develop an approach to automatically classify RCTs and provide visualization of tear location using ultrasound images and convolutional neural networks (CNNs). The proposed method was developed using transfer learning and fine-tuning with five pre-trained deep models (VGG19, InceptionV3, Xception, ResNet50, and DenseNet121). The Bayesian optimization method was also used to optimize hyperparameters of the CNN models. A total of 194 ultrasound images from Kosin University Gospel Hospital were used to train and test the CNN models by five-fold cross-validation. Among the five models, DenseNet121 demonstrated the best classification performance with 88.2% accuracy, 93.8% sensitivity, 83.6% specificity, and AUC score of 0.832. A gradient-weighted class activation mapping (Grad-CAM) highlighted the sensitive features in the learning process on ultrasound images. The proposed approach demonstrates the feasibility of using deep learning and ultrasound images to assist RCTs' diagnosis.

Ho Thao Thi, Kim Geun-Tae, Kim Taewoo, Choi Sanghun, Park Eun-Kee

2022-Jan-18

Convolutional neural network, Deep learning, Rotator cuff tears, Transfer learning, Ultrasound

General General

U-net Models Based on Computed Tomography Perfusion Predict Tissue Outcome in Patients with Different Reperfusion Patterns.

In Translational stroke research ; h5-index 39.0

Evaluation of cerebral perfusion is important for treatment selection in patients with acute large vessel occlusion (LVO). To assess ischemic core and tissue at risk more accurately, we developed a deep learning model named U-net using computed tomography perfusion (CTP) images. A total of 110 acute ischemic stroke patients undergoing endovascular treatment with major reperfusion (≥ 80%) or minimal reperfusion (≤ 20%) were included. Using baseline CTP, we developed two U-net models: one model in major reperfusion group to identify infarct core; the other in minimal reperfusion group to identify tissue at risk. The performance of fixed-thresholding methods was compared with that of U-net models. In the major reperfusion group, the model estimated infarct core with a Dice score coefficient (DSC) of 0.61 and an area under the curve (AUC) of 0.92, while fixed-thresholding methods had a DSC of 0.52. In the minimal reperfusion group, the model estimated tissue at risk with a DSC of 0.67 and an AUC of 0.93, while fixed-thresholding methods had a DSC of 0.51. In both groups, excellent volumetric consistency (intraclass correlation coefficient was 0.951 in major reperfusion and 0.746 in minimal reperfusion) was achieved between the estimated lesion and the actual lesion volume. Thus, in patients with anterior LVO, the CTP-based U-net models were able to identify infarct core and tissue at risk on baseline CTP superior to fixed-thresholding methods, providing individualized prediction of final lesion in patients with different reperfusion patterns.

He Yaode, Luo Zhongyu, Zhou Ying, Xue Rui, Li Jiaping, Hu Haitao, Yan Shenqiang, Chen Zhicai, Wang Jianan, Lou Min

2022-Jan-19

Acute ischemic stroke, Computed tomographic perfusion, Deep learning, Endovascular therapy, Reperfusion patterns

Surgery Surgery

Questionnaire-based survey on the prevalence of medication-overuse headache in Japanese one city-Itoigawa study.

In Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology

OBJECTIVE : The medication-overuse headache (MOH) prevalence has not been investigated in a general Japanese population. We performed questionnaire-based survey and revealed MOH prevalence and its characteristics. We also performed clustering to obtain insight for MOH subgrouping.

METHODS : In this cross-sectional study, the 15-64-year-old population was investigated in Itoigawa during their COVID-19 vaccination under the national policy. MOH was defined as ≥ 15 days/month plus self-report of use of pain medications ≥ 10 or 15 days/month in the last 3 months. Ward method and k-means +  + were used to perform clustering MOH patients.

RESULTS : Among 5865 valid responses, MOH prevalence was 2.32%. MOH was common among females and the middle-aged. Combination-analgesic is the most overused as 50%. MOH had aggravation by routine physical activity, moderate or severe pain, and migraine-like, compared to non-MOH. The 136 MOH patients could be grouped into 3 clusters. Age and frequency of acute medication use were essential factors for clustering.

CONCLUSIONS : This is the first study of MOH prevalence in Japan. Most MOH characteristics were similar to previous reports worldwide. Public awareness of proper headache treatment knowledge is still needed. Clustering results may be important for subtype grouping from a social perspective apart from existing clinical subtypes.

Katsuki Masahito, Yamagishi Chinami, Matsumori Yasuhiko, Koh Akihito, Kawamura Shin, Kashiwagi Kenta, Kito Tomohiro, Entani Akio, Yamamoto Toshiko, Ikeda Takashi, Yamagishi Fuminori

2022-Jan-19

Artificial intelligence (AI), Chronic headache, Clustering, Epidemiology, Medication-overuse headache (MOH), Migraine

General General

Prognostic value, DNA variation and immunologic features of a tertiary lymphoid structure-related chemokine signature in clear cell renal cell carcinoma.

In Cancer immunology, immunotherapy : CII

BACKGROUND : The tumor microenvironment (TME) and tertiary lymphoid structures (TLS) affect the occurrence and development of cancers. How the immune contexture interacts with the phenotype of clear cell renal cell carcinoma (ccRCC) remains unclear.

METHODS : We identified and evaluated TLS clusters in ccRCC using machine learning algorithms and the 12-chemokine gene signature for TLS. Analyses for functional enrichment, DNA variation, immune cell distribution, association with independent clinicopathological features and predictive value of CXCL13 in ccRCC were performed.

RESULTS : We found a prominently enrichment of the 12-chemokine gene signature for TLS in patients with ccRCC compared with other types of renal cell carcinoma. We identified a prognostic value of CCL4, CCL5, CCL8, CCL19 and CXCL13 expression in ccRCC. DNA deletion of the TLS gene signature significantly predicted poor outcome in ccRCC compared with amplification and wild-type gene signature. We established TLS clusters (C1-4) and observed distinct differences in survival, stem cell-like characteristics, immune cell distribution, response to immunotherapies and VEGF-targeted therapies among the clusters. We found that elevated CXCL13 expression significantly predicted aggressive progression and poor prognosis in 232 patients with ccRCC in a real-world validation cohort.

CONCLUSION : This study described a 12-chemokine gene signature for TLS in ccRCC and established TLS clusters that reflected different TME immune status and corresponded to prognosis of ccRCC. We confirmed the dense presence of TILs aggregation and TLS in ccRCC and demonstrated an oncogenic role of CXCL13 expression of ccRCC, which help develop immunotherapies and provide novel insights on the long-term management of ccRCC.

Xu Wenhao, Ma Chunguang, Liu Wangrui, Anwaier Aihetaimujiang, Tian Xi, Shi Guohai, Qu Yuanyuan, Wei Shiyin, Zhang Hailiang, Ye Dingwei

2022-Jan-19

CXCL13, Clear cell renal cell carcinoma, Machine learning algorithm, Prognosis, Tertiary lymphoid structures, Tumor microenvironment

Radiology Radiology

Identifying symptomatic trigeminal nerves from MRI in a cohort of trigeminal neuralgia patients using radiomics.

In Neuroradiology

INTRODUCTION : Trigeminal neuralgia (TN) is a devastating neuropathic condition. This work tests whether radiomics features derived from MRI of the trigeminal nerve can distinguish between TN-afflicted and pain-free nerves.

METHODS : 3D T1- and T2-weighted 1.5-Tesla MRI volumes were retrospectively acquired for patients undergoing stereotactic radiosurgery to treat TN. A convolutional U-net deep learning network was used to segment the trigeminal nerves from the pons to the ganglion. A total of 216 radiomics features consisting of image texture, shape, and intensity were extracted from each nerve. Within a cross-validation scheme, a random forest feature selection method was used, and a shallow neural network was trained using the selected variables to differentiate between TN-affected and non-affected nerves. Average performance over the validation sets was measured to estimate generalizability.

RESULTS : A total of 134 patients (i.e., 268 nerves) were included. The top 16 performing features extracted from the masks were selected for the predictive model. The average validation accuracy was 78%. The validation AUC of the model was 0.83, and sensitivity and specificity were 0.82 and 0.76, respectively.

CONCLUSION : Overall, this work suggests that radiomics features from MR imaging of the trigeminal nerves correlate with the presence of pain from TN.

Mulford Kellen L, Moen Sean L, Grande Andrew W, Nixdorf Donald R, Van de Moortele Pierre-Francois

2022-Jan-19

MRI, Machine learning, Radiomics, Trigeminal neuralgia

General General

DSGAT: predicting frequencies of drug side effects by graph attention networks.

In Briefings in bioinformatics

A critical issue of drug risk-benefit evaluation is to determine the frequencies of drug side effects. Randomized controlled trail is the conventional method for obtaining the frequencies of side effects, while it is laborious and slow. Therefore, it is necessary to guide the trail by computational methods. Existing methods for predicting the frequencies of drug side effects focus on modeling drug-side effect interaction graph. The inherent disadvantage of these approaches is that their performance is closely linked to the density of interactions but which is highly sparse. More importantly, for a cold start drug that does not appear in the training data, such methods cannot learn the preference embedding of the drug because there is no link to the drug in the interaction graph. In this work, we propose a new method for predicting the frequencies of drug side effects, DSGAT, by using the drug molecular graph instead of the commonly used interaction graph. This leads to the ability to learn embeddings for cold start drugs with graph attention networks. The proposed novel loss function, i.e. weighted $\varepsilon$-insensitive loss function, could alleviate the sparsity problem. Experimental results on one benchmark dataset demonstrate that DSGAT yields significant improvement for cold start drugs and outperforms the state-of-the-art performance in the warm start scenario. Source code and datasets are available at https://github.com/xxy45/DSGAT.

Xu Xianyu, Yue Ling, Li Bingchun, Liu Ying, Wang Yuan, Zhang Wenjuan, Wang Lin

2022-Jan-19

chemical structure, cold start, deep learning, graph attention network, side effect frequency

General General

PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein-protein interaction network.

In Briefings in bioinformatics

Although drug combinations in cancer treatment appear to be a promising therapeutic strategy with respect to monotherapy, it is arduous to discover new synergistic drug combinations due to the combinatorial explosion. Deep learning technology holds immense promise for better prediction of in vitro synergistic drug combinations for certain cell lines. In methods applying such technology, omics data are widely adopted to construct cell line features. However, biological network data are rarely considered yet, which is worthy of in-depth study. In this study, we propose a novel deep learning method, termed PRODeepSyn, for predicting anticancer synergistic drug combinations. By leveraging the Graph Convolutional Network, PRODeepSyn integrates the protein-protein interaction (PPI) network with omics data to construct low-dimensional dense embeddings for cell lines. PRODeepSyn then builds a deep neural network with the Batch Normalization mechanism to predict synergy scores using the cell line embeddings and drug features. PRODeepSyn achieves the lowest root mean square error of 15.08 and the highest Pearson correlation coefficient of 0.75, outperforming two deep learning methods and four machine learning methods. On the classification task, PRODeepSyn achieves an area under the receiver operator characteristics curve of 0.90, an area under the precision-recall curve of 0.63 and a Cohen's Kappa of 0.53. In the ablation study, we find that using the multi-omics data and the integrated PPI network's information both can improve the prediction results. Additionally, the case study demonstrates the consistency between PRODeepSyn and previous studies.

Wang Xiaowen, Zhu Hongming, Jiang Yizhi, Li Yulong, Tang Chen, Chen Xiaohan, Li Yunjie, Liu Qi, Liu Qin

2022-Jan-19

deep learning, graph convolutional network, omics data, protein–protein interaction network, synergistic drug combinations

oncology Oncology

TP53_PROF: a machine learning model to predict impact of missense mutations in TP53.

In Briefings in bioinformatics

Correctly identifying the true driver mutations in a patient's tumor is a major challenge in precision oncology. Most efforts address frequent mutations, leaving medium- and low-frequency variants mostly unaddressed. For TP53, this identification is crucial for both somatic and germline mutations, with the latter associated with the Li-Fraumeni syndrome (LFS), a multiorgan cancer predisposition. We present TP53_PROF (prediction of functionality), a gene specific machine learning model to predict the functional consequences of every possible missense mutation in TP53, integrating human cell- and yeast-based functional assays scores along with computational scores. Variants were labeled for the training set using well-defined criteria of prevalence in four cancer genomics databases. The model's predictions provided accuracy of 96.5%. They were validated experimentally, and were compared to population data, LFS datasets, ClinVar annotations and to TCGA survival data. Very high accuracy was shown through all methods of validation. TP53_PROF allows accurate classification of TP53 missense mutations applicable for clinical practice. Our gene specific approach integrated machine learning, highly reliable features and biological knowledge, to create an unprecedented, thoroughly validated and clinically oriented classification model. This approach currently addresses TP53 mutations and will be applied in the future to other important cancer genes.

Ben-Cohen Gil, Doffe Flora, Devir Michal, Leroy Bernard, Soussi Thierry, Rosenberg Shai

2022-Jan-18

\n TP53, Li-Fraumeni syndrome, genetic counseling, machine learning, personalized oncology, precision medicine

oncology Oncology

SRDFM: Siamese Response Deep Factorization Machine to improve anti-cancer drug recommendation.

In Briefings in bioinformatics

Predicting the response of cancer patients to a particular treatment is a major goal of modern oncology and an important step toward personalized treatment. In the practical clinics, the clinicians prefer to obtain the most-suited drugs for a particular patient instead of knowing the exact values of drug sensitivity. Instead of predicting the exact value of drug response, we proposed a deep learning-based method, named Siamese Response Deep Factorization Machines (SRDFM) Network, for personalized anti-cancer drug recommendation, which directly ranks the drugs and provides the most effective drugs. A Siamese network (SN), a type of deep learning network that is composed of identical subnetworks that share the same architecture, parameters and weights, was used to measure the relative position (RP) between drugs for each cell line. Through minimizing the difference between the real RP and the predicted RP, an optimal SN model was established to provide the rank for all the candidate drugs. Specifically, the subnetwork in each side of the SN consists of a feature generation level and a predictor construction level. On the feature generation level, both drug property and gene expression, were adopted to build a concatenated feature vector, which even enables the recommendation for newly designed drugs with only chemical property known. Particularly, we developed a response unit here to generate weighted genetic feature vector to simulate the biological interaction mechanism between a specific drug and the genes. For the predictor construction level, we built this level integrating a factorization machine (FM) component with a deep neural network component. The FM can well handle the discrete chemical information and both low-order and high-order feature interactions could be sufficiently learned. Impressively, the SRDFM works well on both single-drug recommendation and synergic drug combination. Experiment result on both single-drug and synergetic drug data sets have shown the efficiency of the SRDFM. The Python implementation for the proposed SRDFM is available at at https://github.com/RanSuLab/SRDFM Contact: ran.su@tju.edu.cn, gbx@mju.edu.cn and weileyi@sdu.edu.cn.

Su Ran, Huang YiXuan, Zhang De-Gan, Xiao Guobao, Wei Leyi

2022-Jan-18

General General

MSLibrarian: Optimized Predicted Spectral Libraries for Data-Independent Acquisition Proteomics.

In Journal of proteome research

Data-independent acquisition-mass spectrometry (DIA-MS) is the method of choice for deep, consistent, and accurate single-shot profiling in bottom-up proteomics. While classic workflows for targeted quantification from DIA-MS data require auxiliary data-dependent acquisition (DDA) MS analysis of subject samples to derive prior-knowledge spectral libraries, library-free approaches based on in silico prediction promise deep DIA-MS profiling with reduced experimental effort and cost. Coverage and sensitivity in such analyses are however limited, in part, by the large library size and persistent deviations from the experimental data. We present MSLibrarian, a new workflow and tool to obtain optimized predicted spectral libraries by the integrated usage of spectrum-centric DIA data interpretation via the DIA-Umpire approach to inform and calibrate the in silico predicted library and analysis approach. Predicted-vs-observed comparisons enabled optimization of intensity prediction parameters, calibration of retention time prediction for deviating chromatographic setups, and optimization of the library scope and sample representativeness. Benchmarking via a dedicated ground-truth-embedded experiment of species-mixed proteins and quantitative ratio-validation confirmed gains of up to 13% on peptide and 8% on protein level at equivalent FDR control and validation criteria. MSLibrarian is made available as an open-source R software package, including step-by-step user instructions, at https://github.com/MarcIsak/MSLibrarian.

Isaksson Marc, Karlsson Christofer, Laurell Thomas, Kirkeby Agnete, Heusel Moritz

2022-Jan-19

R-software, data-independent acquisition, deep-learning, proteomics, spectral predictions

General General

Monitoring of COVID-19 Pandemic-related Psychopathology using Machine Learning.

In Acta neuropsychiatrica

The COVID-19 pandemic is believed to have a major negative impact on global mental health due to the viral disease itself as well as the associated lockdowns, social distancing, isolation, fear, and increased uncertainty. Individuals with preexisting mental illness are likely to be particularly vulnerable to these conditions and may develop outright "COVID-19 related psychopathology". Here, we trained a machine learning model on structured and natural text data from electronic health records to identify COVID-19 pandemic-related psychopathology among patients receiving care in the Psychiatric Services of the Central Denmark Region. Subsequently, applying this model, we found that pandemic-related psychopathology covaries with the pandemic pressure over time. These findings may aid psychiatric services in their planning during the ongoing and future pandemics. Furthermore, the results are a testament to the potential of applying machine learning to data from electronic health records.

Enevoldsen Kenneth C, Danielsen Andreas A, Rohde Christopher, Jefsen Oskar H, Nielbo Kristoffer L, Østergaard Søren D

2022-Jan-19

COVID-19, Coronavirus, Machine Learning, Mental Disorders, Natural Language Processing

General General

Statistical Learning for Individualized Asset Allocation

ArXiv Preprint

We establish a high-dimensional statistical learning framework for individualized asset allocation. Our proposed methodology addresses continuous-action decision-making with a large number of characteristics. We develop a discretization approach to model the effect from continuous actions and allow the discretization level to be large and diverge with the number of observations. The value function of continuous-action is estimated using penalized regression with generalized penalties that are imposed on linear transformations of the model coefficients. We show that our estimators using generalized folded concave penalties enjoy desirable theoretical properties and allow for statistical inference of the optimal value associated with optimal decision-making. Empirically, the proposed framework is exercised with the Health and Retirement Study data in finding individualized optimal asset allocation. The results show that our individualized optimal strategy improves individual financial well-being and surpasses benchmark strategies.

Yi Ding, Yingying Li, Rui Song

2022-01-20

General General

Prediction of Major Adverse Cardiovascular Events From Retinal, Clinical, and Genomic Data in Individuals With Type 2 Diabetes: A Population Cohort Study.

In Diabetes care ; h5-index 125.0

OBJECTIVES : Improved identification of individuals with type 2 diabetes at high cardiovascular (CV) risk could help in selection of newer CV risk-reducing therapies. The aim of this study was to determine whether retinal vascular parameters, derived from retinal screening photographs, alone and in combination with a genome-wide polygenic risk score for coronary heart disease (CHD PRS) would have independent prognostic value over traditional CV risk assessment in patients without prior CV disease.

RESEARCH DESIGN AND METHODS : Patients in the Genetics of Diabetes Audit and Research Tayside Scotland (GoDARTS) study were linked to retinal photographs, prescriptions, and outcomes. Retinal photographs were analyzed using VAMPIRE (Vascular Assessment and Measurement Platform for Images of the Retina) software, a semiautomated artificial intelligence platform, to compute arterial and venous fractal dimension, tortuosity, and diameter. CHD PRS was derived from previously published data. Multivariable Cox regression was used to evaluate the association between retinal vascular parameters and major adverse CV events (MACE) at 10 years compared with the pooled cohort equations (PCE) risk score.

RESULTS : Among 5,152 individuals included in the study, a MACE occurred in 1,017 individuals. Reduced arterial fractal dimension and diameter and increased venous tortuosity each independently predicted MACE. A risk score combining these parameters significantly predicted MACE after adjustment for age, sex, PCE, and the CHD PRS (hazard ratio 1.11 per SD increase, 95% CI 1.04-1.18, P = 0.002) with similar accuracy to PCE (area under the curve [AUC] 0.663 vs. 0.658, P = 0.33). A model incorporating retinal parameters and PRS improved MACE prediction compared with PCE (AUC 0.686 vs. 0.658, P < 0.001).

CONCLUSIONS : Retinal parameters alone and in combination with genome-wide CHD PRS have independent and incremental prognostic value compared with traditional CV risk assessment in type 2 diabetes.

Mordi Ify R, Trucco Emanuele, Syed Mohammad Ghouse, MacGillivray Tom, Nar Adi, Huang Yu, George Gittu, Hogg Stephen, Radha Venkatesan, Prathiba Vijayaraghavan, Anjana Ranjit Mohan, Mohan Viswanathan, Palmer Colin N A, Pearson Ewan R, Lang Chim C, Doney Alex S F

2022-Jan-17

General General

Evidence for Peroxisomal Dysfunction and Dysregulation of the CDP-Choline Pathway in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.

In medRxiv : the preprint server for health sciences

Background : Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic and debilitating disease that is characterized by unexplained physical fatigue unrelieved by rest. Symptoms also include cognitive and sensory dysfunction, sleeping disturbances, orthostatic intolerance, and gastrointestinal problems. A syndrome clinically similar to ME/CFS has been reported following well-documented infections with the coronaviruses SARS-CoV and MERS-CoV. At least 10% of COVID-19 survivors develop post acute sequelae of SARS-CoV-2 infection (PASC). Although many individuals with PASC have evidence of structural organ damage, a subset have symptoms consistent with ME/CFS including fatigue, post exertional malaise, cognitive dysfunction, gastrointestinal disturbances, and postural orthostatic intolerance. These common features in ME/CFS and PASC suggest that insights into the pathogenesis of either may enrich our understanding of both syndromes, and could expedite the development of strategies for identifying those at risk and interventions that prevent or mitigate disease.

Methods : Using regression, Bayesian and enrichment analyses, we conducted targeted and untargeted metabolomic analysis of 888 metabolic analytes in plasma samples of 106 ME/CFS cases and 91 frequency-matched healthy controls.

Results : In ME/CFS cases, regression, Bayesian and enrichment analyses revealed evidence of peroxisomal dysfunction with decreased levels of plasmalogens. Other findings included decreased levels of several membrane lipids, including phosphatidylcholines and sphingomyelins, that may indicate dysregulation of the cytidine-5’-diphosphocholine pathway. Enrichment analyses revealed decreased levels of choline, ceramides and carnitines, and increased levels of long chain triglycerides (TG) and hydroxy-eicosapentaenoic acid. Elevated levels of dicarboxylic acids were consistent with abnormalities in the tricarboxylic acid cycle. Using machine learning algorithms with selected metabolites as predictors, we were able to differentiate female ME/CFS cases from female controls (highest AUC=0.794) and ME/CFS cases without self-reported irritable bowel syndrome (sr-IBS) from controls without sr-IBS (highest AUC=0.873).

Conclusion : Our findings are consistent with earlier ME/CFS work indicating compromised energy metabolism and redox imbalance, and highlight new abnormalities that may provide insights into the pathogenesis of ME/CFS.

One Sentence Summary : Plasma levels of plasmalogens are decreased in patients with myalgic encephalomyelitis/chronic fatigue syndrome suggesting peroxisome dysfunction.

Che Xiaoyu, Brydges Christopher R, Yu Yuanzhi, Price Adam, Joshi Shreyas, Roy Ayan, Lee Bohyun, Barupal Dinesh K, Cheng Aaron, Palmer Dana March, Levine Susan, Peterson Daniel L, Vernon Suzanne D, Bateman Lucinda, Hornig Mady, Montoya Jose G, Komaroff Anthony L, Fiehn Oliver, Lipkin W Ian

2022-Jan-11

General General

AI ethics and systemic risks in finance.

In AI and ethics

The paper suggests that AI ethics should pay attention to morally relevant systemic effects of AI use. It draws the attention of ethicists and practitioners to systemic risks that have been neglected so far in professional AI-related codes of conduct, industrial standards and ethical discussions more generally. The paper uses the financial industry as an example to ask: how can AI-enhanced systemic risks be ethically accounted for? Which specific issues does AI use raise for ethics that takes systemic effects into account? The paper (1) relates the literature about AI ethics to the ethics of systemic risks to clarify the moral relevance of AI use with respect to the imposition of systemic risks, (2) proposes a theoretical framework based on the ethics of complexity and (3) applies this framework to discuss implications for AI ethics concerned with AI-enhanced systemic risks.

Svetlova Ekaterina

2022-Jan-13

AI ethics, Artificial intelligence, Finance, Responsibility, Systemic risks

General General

Forecasting Covid-19 Transmission with ARIMA and LSTM Techniques in Morocco.

In SN computer science

In this paper, we are interested to forecast and predict the time evolution of the Covid-19 in Morocco based on two different time series forecasting models. We used Auto-Regressive Integrated Moving Average (ARIMA) and Long short-term memory (LSTM) models to predict the outbreak of Covid-19 in the upcoming 2 months in Morocco. In this work, we measured the effective reproduction number using the real data and also the fitted forecasted data produced by the two used approaches, to reveal how effective the measures taken by the Moroccan government have been controlling the Covid-19 outbreak. The prediction results for the next 2 months show a strong evolution in the number of confirmed and death cases in Morocco. According to the measures of the effective reproduction number, the transmissibility of the disease will continue to expand in the next 2 months, but fortunately, the higher value of the effective reproduction number is not considered to be dramatic and, therefore, may give hope for controlling the disease.

Rguibi Mohamed Amine, Moussa Najem, Madani Abdellah, Aaroud Abdessadak, Zine-Dine Khalid

2022

Covid-19, Epidemic transmission, Machine learning, Time series forecasting

General General

The influence of fake accounts on sentiment analysis related to COVID-19 in Indonesia.

In Procedia computer science

Cases of the spread of COVID-19 that continue to increase in Indonesia have made the level of public satisfaction with the government in dealing with this virus fairly low. One way to measure the level of community satisfaction is by analyzing social media. Sentiment analysis can be used to analyze feedback from the public. Research related to sentiment analysis has been mostly carried out, but so far, it has focused more on opinions contained in sentences and comments and has not considered the subject of the account that posted it. On the other hand, the use of fake accounts or bots on social media is becoming more and more prevalent, so that the credibility of opinion makers is reduced. Based on these problems, this research conducted several experiments related to sentiment analysis using a machine learning approach and fake account categories to see the influence of fake accounts on sentiment analysis. The data used in this research were taken from social media Twitter. The results showed that there was an influence from fake accounts that can reduce the performance of sentiment classification. The experimental results of the two algorithms also prove that the Support Vector Machine algorithm has a better performance than the Naïve-Bayes algorithm for this case with the highest Accuracy value of 80.6%. In addition, the results of the sentiment visualization showed that there was an influence from fake accounts which actually leads to positive sentiment although it is not significant.

Pratama Rivanda Putra, Tjahyanto Aris

2022

Sentiment analysis, fake account, machine learning, twitter

General General

A comprehensive study of the COVID-19 impact on PM2.5 levels over the contiguous United States: A deep learning approach.

In Atmospheric environment (Oxford, England : 1994)

We investigate the impact of the COVID-19 outbreak on PM2.5 levels in eleven urban environments across the United States: Washington DC, New York, Boston, Chicago, Los Angeles, Houston, Dallas, Philadelphia, Detroit, Phoenix, and Seattle. We estimate daily PM2.5 levels over the contiguous U.S. in March-May 2019 and 2020, and leveraging a deep convolutional neural network, we find a correlation coefficient, an index of agreement, a mean absolute bias, and a root mean square error of 0.90 (0.90), 0.95 (0.95), 1.34 (1.24) μg/m3, and 2.04 (1.87) μg/m3, respectively. Results from Google Community Mobility Reports and estimated PM2.5 concentrations show a greater reduction of PM2.5 in regions with larger decreases in human mobility and those in which individuals remain in their residential areas longer. The relationship between vehicular PM2.5 (i.e., the ratio of vehicular PM2.5 to other sources of PM2.5) emissions and PM2.5 reductions (R = 0.77) in various regions indicates that regions with higher emissions of vehicular PM2.5 generally experience greater decreases in PM2.5. While most of the urban environments ⸺ Washington DC, New York, Boston, Chicago, Los Angeles, Houston, Dallas, Philadelphia, Detroit, and Seattle ⸺ show a decrease in PM2.5 levels by 21.1%, 20.7%, 18.5%, 8.05%, 3.29%, 3.63%, 6.71%, 4.82%, 13.5%, and 7.73%, respectively, between March-May of 2020 and 2019, Phoenix shows a 5.5% increase during the same period. Similar to their PM2.5 reductions, Washington DC, New York, and Boston, compared to other cities, exhibit the highest reductions in human mobility and the highest vehicular PM2.5 emissions, highlighting the great impact of human activity on PM2.5 changes in eleven regions. Moreover, compared to changes in meteorological factors, changes in pollutant concentrations, including those of black carbon, organic carbon, SO2, SO4, and especially NO2, appear to have had a significantly greater impact on PM2.5 changes during the study period.

Ghahremanloo Masoud, Lops Yannic, Choi Yunsoo, Jung Jia, Mousavinezhad Seyedali, Hammond Davyda

2022-Jan-14

COVID-19, Community multiscale air quality (CMAQ) model, Deep convolutional neural network, Google mobility reports, PM2.5 estimation, United States

General General

When people are defeated by artificial intelligence in a competition task requiring logical thinking, how do they make causal attribution?

In Current psychology (New Brunswick, N.J.)

** : Given that artificial intelligence (AI) has been predicted to eventually take on human tasks demanding logical thinking, it makes sense that we should examine psychological responses of humans when their performance is inferior to AI. Research has demonstrated that after people fail a task, whether they reorient their behavior towards success depends on what they attribute the failure to. This study investigated the causal attributions people made in a competition task requiring such thinking. We also recorded whether they wanted to re-challenge the games after they were defeated by AI. Experiments 1 (N = 74) and 2 (N = 788) recruited Japanese participants, while Experiment 3 (N = 500) comprised American participants. There were two conditions: in the first, participants competed against an AI opponent and in the other, they believed they were competing against a human. The results of the three experiments showed that participants attributed the loss to their own and their opponent's abilities more than any other factor, irrespective of the opponent type. The number of participants choosing to re-challenge the game did not differ significantly between the AI and human conditions in Experiments 1 and 3, although the number was lower in the AI condition than in the human condition in Experiment 2. Besides providing fresh insight on how people make causal attributions when competing against AI, our findings also predict how people will respond after their jobs are replaced by AI.

Supplementary Information : The online version contains supplementary material available at 10.1007/s12144-021-02559-w.

Yokoi Ryosuke, Nakayachi Kazuya

2022-Jan-14

Artificial intelligence, Behavioral response, Causal attribution, Competition game, Self-effacing bias

General General

Rock mass classification prediction model using heuristic algorithms and support vector machines: a case study of Chambishi copper mine.

In Scientific reports ; h5-index 158.0

The rock mass is one of the key parameters in engineering design. Accurate rock mass classification is also essential to ensure operational safety. Over the past decades, various models have been proposed to evaluate and predict rock mass. Among these models, artificial intelligence (AI) based models are becoming more popular due to their outstanding prediction results and generalization ability for multiinfluential factors. In order to develop an easy-to-use rock mass classification model, support vector machine (SVM) techniques are adopted as the basic prediction tools, and three types of optimization algorithms, i.e., particle swarm optimization (PSO), genetic algorithm (GA) and grey wolf optimization (GWO), are implemented to improve the prediction classification and optimize the hyper-parameters. A database was assembled, consisting of 80 sets of real engineering data, involving four influencing factors. The three combined models are compared in accuracy, precision, recall, F1 value and computational time. The results reveal that among three models, the GWO-SVC-based model shows the best classification performance by training. The accuracy of training and testing sets of GWO-SVC are 90.6250% (58/64) and 93.7500% (15/16), respectively. For Grades I, II, III, IV and V, the precision value is 1, 0.93, 0.90, 0.92, 0.83, the recall value is 1, 1, 0.93, 0.73, 0.83, and the F1 value is 1, 0.96, 0.92, 0.81, 0.83, respectively. Sensitivity analysis is performed to understand the influence of input parameters on rock mass classification. It shows that the sensitive factor in rock mass quality is the RQD. Finally, the GWO-SVC is employed to assess the quality of rocks from the southeastern ore body of the Chambishi copper mine. Overall, the current study demonstrates the potential of using artificial intelligence methods in rock mass assessment, rendering far better results than the previous reports.

Hu Jianhua, Zhou Tan, Ma Shaowei, Yang Dongjie, Guo Mengmeng, Huang Pengli

2022-Jan-18

Ophthalmology Ophthalmology

Automated evaluation of retinal pigment epithelium disease area in eyes with age-related macular degeneration.

In Scientific reports ; h5-index 158.0

The retinal pigment epithelium (RPE) is essential for the survival and function of retinal photoreceptor cells. RPE dysfunction causes various retinal diseases including age-related macular degeneration (AMD). Clinical studies on ES/iPS cell-derived RPE transplantation for RPE dysfunction-triggered diseases are currently underway. Quantification of the diseased RPE area is important to evaluate disease progression or the therapeutic effect of RPE transplantation. However, there are no standard protocols. To address this issue, we developed a 2-step software that enables objective and efficient quantification of RPE-disease area changes by analyzing the early-phase hyperfluorescent area in fluorescein angiography (FA) images. We extracted the Abnormal region. This extraction was based on deep learning-based discrimination. We scored the binarized extracted area using an automated program. Our program's performance for the same eye from the serial image captures was within 3.1 ± 7.8% error. In progressive AMD, the trend was consistent with human assessment, even when FA images from two different visits were compared. This method was applicable to quantifying RPE-disease area changes over time, evaluating iPSC-RPE transplantation images, and a disease other than AMD. Our program may contribute to the assessment of the clinical course of RPE-disease areas in routine clinics and reduce the workload of researchers.

Motozawa Naohiro, Miura Takuya, Ochiai Koji, Yamamoto Midori, Horinouchi Takaaki, Tsuzuki Taku, Kanda Genki N, Ozawa Yosuke, Tsujikawa Akitaka, Takahashi Koichi, Takahashi Masayo, Kurimoto Yasuo, Maeda Tadao, Mandai Michiko

2022-Jan-18

oncology Oncology

Machine learning assisted real-time deformability cytometry of CD34+ cells allows to identify patients with myelodysplastic syndromes.

In Scientific reports ; h5-index 158.0

Diagnosis of myelodysplastic syndrome (MDS) mainly relies on a manual assessment of the peripheral blood and bone marrow cell morphology. The WHO guidelines suggest a visual screening of 200 to 500 cells which inevitably turns the assessor blind to rare cell populations and leads to low reproducibility. Moreover, the human eye is not suited to detect shifts of cellular properties of entire populations. Hence, quantitative image analysis could improve the accuracy and reproducibility of MDS diagnosis. We used real-time deformability cytometry (RT-DC) to measure bone marrow biopsy samples of MDS patients and age-matched healthy individuals. RT-DC is a high-throughput (1000 cells/s) imaging flow cytometer capable of recording morphological and mechanical properties of single cells. Properties of single cells were quantified using automated image analysis, and machine learning was employed to discover morpho-mechanical patterns in thousands of individual cells that allow to distinguish healthy vs. MDS samples. We found that distribution properties of cell sizes differ between healthy and MDS, with MDS showing a narrower distribution of cell sizes. Furthermore, we found a strong correlation between the mechanical properties of cells and the number of disease-determining mutations, inaccessible with current diagnostic approaches. Hence, machine-learning assisted RT-DC could be a promising tool to automate sample analysis to assist experts during diagnosis or provide a scalable solution for MDS diagnosis to regions lacking sufficient medical experts.

Herbig Maik, Jacobi Angela, Wobus Manja, Weidner Heike, Mies Anna, Kräter Martin, Otto Oliver, Thiede Christian, Weickert Marie-Theresa, Götze Katharina S, Rauner Martina, Hofbauer Lorenz C, Bornhäuser Martin, Guck Jochen, Ader Marius, Platzbecker Uwe, Balaian Ekaterina

2022-Jan-18

General General

A machine learning application for raising WASH awareness in the times of COVID-19 pandemic.

In Scientific reports ; h5-index 158.0

The COVID-19 pandemic has revealed the power of internet disinformation in influencing global health. The deluge of information travels faster than the epidemic itself and is a threat to the health of millions across the globe. Health apps need to leverage machine learning for delivering the right information while constantly learning misinformation trends and deliver these effectively in vernacular languages in order to combat the infodemic at the grassroot levels in the general public. Our application, WashKaro, is a multi-pronged intervention that uses conversational Artificial Intelligence (AI), machine translation, and natural language processing to combat misinformation (NLP). WashKaro uses AI to provide accurate information matched against WHO recommendations and delivered in an understandable format in local languages. The primary aim of this study was to assess the use of neural models for text summarization and machine learning for delivering WHO matched COVID-19 information to mitigate the misinfodemic. The secondary aim of this study was to develop a symptom assessment tool and segmentation insights for improving the delivery of information. A total of 5026 people downloaded the app during the study window; among those, 1545 were actively engaged users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot "Satya" increased thus proving the usefulness of a mHealth platform to mitigate health misinformation. We conclude that a machine learning application delivering bite-sized vernacular audios and conversational AI is a practical approach to mitigate health misinformation.

Pandey Rohan, Gautam Vaibhav, Pal Ridam, Bandhey Harsh, Dhingra Lovedeep Singh, Misra Vihaan, Sharma Himanshu, Jain Chirag, Bhagat Kanav, Arushi Patel, Lajjaben Agarwal, Mudit Agrawal, Samprati Jalan, Rishabh Wadhwa, Akshat Garg, Ayush Agrawal, Yashwin Rana, Bhavika Kumaraguru, Ponnurangam Sethi

2022-Jan-17

Radiology Radiology

Automated grading of enlarged perivascular spaces in clinical imaging data of an acute stroke cohort using an interpretable, 3D deep learning framework.

In Scientific reports ; h5-index 158.0

Enlarged perivascular spaces (EPVS), specifically in stroke patients, has been shown to strongly correlate with other measures of small vessel disease and cognitive impairment at 1 year follow-up. Typical grading of EPVS is often challenging and time consuming and is usually based on a subjective visual rating scale. The purpose of the current study was to develop an interpretable, 3D neural network for grading enlarged perivascular spaces (EPVS) severity at the level of the basal ganglia using clinical-grade imaging in a heterogenous acute stroke cohort, in the context of total cerebral small vessel disease (CSVD) burden. T2-weighted images from a retrospective cohort of 262 acute stroke patients, collected in 2015 from 5 regional medical centers, were used for analyses. Patients were given a label of 0 for none-to-mild EPVS (< 10) and 1 for moderate-to-severe EPVS (≥ 10). A three-dimensional residual network of 152 layers (3D-ResNet-152) was created to predict EPVS severity and 3D gradient class activation mapping (3DGradCAM) was used for visual interpretation of results. Our model achieved an accuracy 0.897 and area-under-the-curve of 0.879 on a hold-out test set of 15% of the total cohort (n = 39). 3DGradCAM showed areas of focus that were in physiologically valid locations, including other prevalent areas for EPVS. These maps also suggested that distribution of class activation values is indicative of the confidence in the model's decision. Potential clinical implications of our results include: (1) support for feasibility of automated of EPVS scoring using clinical-grade neuroimaging data, potentially alleviating rater subjectivity and improving confidence of visual rating scales, and (2) demonstration that explainable models are critical for clinical translation.

Williamson Brady J, Khandwala Vivek, Wang David, Maloney Thomas, Sucharew Heidi, Horn Paul, Haverbusch Mary, Alwell Kathleen, Gangatirkar Shantala, Mahammedi Abdelkader, Wang Lily L, Tomsick Thomas, Gaskill-Shipley Mary, Cornelius Rebecca, Khatri Pooja, Kissela Brett, Vagal Achala

2022-Jan-17

General General

Contribution of the medial eye field network to the voluntary deployment of visuospatial attention.

In Nature communications ; h5-index 260.0

Historically, the study of patients with spatial neglect has provided fundamental insights into the neural basis of spatial attention. However, lesion mapping studies have been unsuccessful in establishing the potential role of associative networks spreading on the dorsal-medial axis, mainly because they are uncommonly targeted by vascular injuries. Here we combine machine learning-based lesion-symptom mapping, disconnection analyses and the longitudinal behavioral data of 128 patients with well-delineated surgical resections. The analyses show that surgical resections in a location compatible with both the supplementary and the cingulate eye fields, and disrupting the dorsal-medial fiber network, are specifically associated with severely diminished performance on a visual search task (i.e., visuo-motor exploratory neglect) with intact performance on a task probing the perceptual component of neglect. This general finding provides causal evidence for a role of the frontal-medial network in the voluntary deployment of visuo-spatial attention.

Herbet Guillaume, Duffau Hugues

2022-Jan-17

Dermatology Dermatology

Baseline Clinical and Biomarker Characteristics of Biobank Innovations for Chronic Cerebrovascular Disease With Alzheimer's Disease Study: BICWALZS.

In Psychiatry investigation

OBJECTIVE : We aimed to present the study design and baseline cross-sectional participant characteristics of biobank innovations for chronic cerebrovascular disease with Alzheimer's disease study (BICWALZS) participants.

METHODS : A total of 1,013 participants were enrolled in BICWALZS from October 2016 to December 2020. All participants underwent clinical assessments, basic blood tests, and standardized neuropsychological tests (n=1,013). We performed brain magnetic resonance imaging (MRI, n=817), brain amyloid positron emission tomography (PET, n=713), single nucleotide polymorphism microarray chip (K-Chip, n=949), locomotor activity assessment (actigraphy, n=200), and patient-derived dermal fibroblast sampling (n=175) on a subset of participants.

RESULTS : The mean age was 72.8 years, and 658 (65.0%) were females. Based on clinical assessments, total of 168, 534, 211, 80, and 20 had subjective cognitive decline, mild cognitive impairment (MCI), Alzheimer's dementia, vascular dementia, and other types of dementia or not otherwise specified, respectively. Based on neuroimaging biomarkers and cognition, 199, 159, 78, and 204 were cognitively normal (CN), Alzheimer's disease (AD)-related cognitive impairment, vascular cognitive impairment, and not otherwise specified due to mixed pathology (NOS). Each group exhibited many differences in various clinical, neuropsychological, and neuroimaging results at baseline. Baseline characteristics of BICWALZS participants in the MCI, AD, and vascular dementia groups were generally acceptable and consistent with 26 worldwide dementia cohorts and another independent AD cohort in Korea.

CONCLUSION : The BICWALZS is a prospective and longitudinal study assessing various clinical and biomarker characteristics in older adults with cognitive complaints. Details of the recruitment process, methodology, and baseline assessment results are described in this paper.

Roh Hyun Woong, Kim Na-Rae, Lee Dong-Gi, Cheong Jae-Youn, Seo Sang Won, Choi Seong Hye, Kim Eun-Joo, Cho Soo Hyun, Kim Byeong C, Kim Seong Yoon, Kim Eun Young, Chang Jaerak, Lee Sang Yoon, Yoon Dukyong, Choi Jin Wook, An Young-Sil, Kang Hee Young, Shin Hyunjung, Park Bumhee, Son Sang Joon, Hong Chang Hyung

2022-Jan-19

Alzheimer’s disease, Cohort, Dementia, Mild cognitive impairment, Subcortical vascular cognitive impairment, Vascular dementia

General General

Deep multimodal learning for lymph node metastasis prediction of primary thyroid cancer.

In Physics in medicine and biology

Incidence of primary thyroid cancer rises steadily over the past decades because of overdiagnosis and overtreatment through the improvement in imaging techniques for screening, especially in ultrasound examination. Metastatic status of lymph nodes is important for staging the type of primary thyroid cancer. Deep learning algorithms based on ultrasound images were thus developed to assist radiologists on the diagnosis of lymph node metastasis. To integrate more clinical context (e.g., health records and various image modalities) into, and explore more interpretable patterns discovered by, these algorithms, a deep multimodal learning network was developed in this study for the prediction of lymph node metastasis in primary thyroid cancer patients. The result indicates that the proposed network achieved an average F1 score of 0.888 and an average AUC value of 0.973 in two independent validation sets, and the performance was significantly better than three single-modality deep learning networks. Furthermore, a novel index was proposed to compare the contribution of different modalities in making the predictions, which showed that among three modalities used in this study, the deep multimodal learning network relied generally more on image modalities than the data modality of clinic records. Our work is beneficial to prospective clinic trials of the radiologists, and will better help them understand how the predictions are made in deep multimodal learning algorithms.

Wu Xing Long, Li Mengying, Cui Xin-Wu, Xu Guoping

2022-Jan-18

convolutional neural network, deep multimodal learning, lymph node metastasis, medical imaging, thyroid cancer

General General

Large-scale recognition of natural landmarks with deep learning based on biomimetic sonar echoes.

In Bioinspiration & biomimetics

The ability to identify natural landmarks on a regional scale could contribute to the navigation skills of echolocating bats and also advance the quest for autonomy in natural environments with man-made systems. However, recognizing natural landmarks based on biosonar echoes has to deal with the unpredictable nature of echoes that are typically superpositions of contributions from many different reflectors with unknown properties. The results presented here show that a deep neural network (ResNet50) was able to classify 10 different field sites and 20 different tracks (2 at each site) distributed over an area about 40 kilometers in diameter. Based on spectrogram representations of single echoes, classification accuracies up to 99.6% for different sites and 94.7% for different tracks have been achieved. Classification performance was found to depend on the used pulse component (constant-frequency - CF vs. frequency-modulated - FM) and the trade-off between time and frequency resolution in the spectrogram representations of the echoes. For the former, classification performance increased monotonically with better time resolution. For the latter, classification performance peaked at an intermediate trade-off point between time and frequency resolution indicating that both dimensions contained relevant information. Future work will be needed to further characterize the quality of the spatial information contained in the echoes, e.g., in terms of spatial resolution and potential ambiguities.

Zhang Liujun, Mueller Rolf

2022-Jan-18

Biomimetic Robot, Biosonar Navigation, Machine Learning

General General

Supervised machine learning to predict reduced depression severity in people with epilepsy through epilepsy self-management intervention.

In Epilepsy & behavior : E&B

OBJECTIVE : To develop a classifier that predicts reductions in depression severity in people with epilepsy after participation in an epilepsy self-management intervention.

METHODS : Ninety-three people with epilepsy from three epilepsy self-management randomized controlled trials from the Managing Epilepsy Well (MWE) Network integrated research database met the inclusion criteria. Supervised machine learning algorithms were utilized to develop prediction models for changes in self-reported depression symptom severity. Features considered by the machine learning classifiers include age, gender, race, ethnicity, education, study type, baseline quality of life, and baseline depression symptom severity. The models were trained and evaluated on their ability to predict clinically meaningful improvement (i.e., a reduction of greater than three points on the nine-item Patient Health Questionnaire (PHQ-9)) between baseline and follow-up (<=12 weeks) depression scores. Models tested were a Multilayer Perceptron (ML), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression with Stochastic Gradient Descent (SGD), K-nearest Neighbors (KNN), and Gradient Boosting (GB). A separate, outside dataset of 41 people with epilepsy was used in a validation exercise to examine the top-performing model's generalizability and performance with external data.

RESULTS : All six classifiers performed better than our baseline mode classifier. Support Vector Machine had the best overall performance (average area under the curve [AUC] = 0.754, highest subpopulation AUC = 0.963). Our analysis of the SVM features revealed that higher baseline depression symptom severity, study type (i.e., intervention program goals), higher baseline quality of life, and race had the strongest influence on increasing the likelihood that a subject would experience a clinically meaningful improvement in depression scores. From the validation exercise, our top-performing SVM model performed similarly or better than the average SVM model with the outside dataset (average AUC = 0.887).

SIGNIFICANCE : We trained an SVM classifier that offers novel insight into subject-specific features that are important for predicting a clinically meaningful improvement in subjective depression scores after enrollment in a self-management program. We provide evidence for machine learning to select subjects that may benefit most from a self-management program and indicate important factors that self-management programs should collect to develop improved digital tools.

Camp Edward J, Quon Robert J, Sajatovic Martha, Briggs Farren, Brownrigg Brittany, Janevic Mary R, Meisenhelter Stephen, Steimel Sarah A, Testorf Markus E, Kiriakopoulos Elaine, Mazanec Morgan T, Fraser Robert T, Johnson Erica K, Jobst Barbara C

2022-Jan-15

Depression, Epilepsy, Machine learning, Quality of life, Self-management, Support Vector Machine

General General

AI-based carcinoma detection and classification using histopathological images: A systematic review.

In Computers in biology and medicine

Histopathological image analysis is the gold standard to diagnose cancer. Carcinoma is a subtype of cancer that constitutes more than 80% of all cancer cases. Squamous cell carcinoma and adenocarcinoma are two major subtypes of carcinoma, diagnosed by microscopic study of biopsy slides. However, manual microscopic evaluation is a subjective and time-consuming process. Many researchers have reported methods to automate carcinoma detection and classification. The increasing use of artificial intelligence (AI) in the automation of carcinoma diagnosis also reveals a significant rise in the use of deep network models. In this systematic literature review, we present a comprehensive review of the state-of-the-art approaches reported in carcinoma diagnosis using histopathological images. Studies are selected from well-known databases with strict inclusion/exclusion criteria. We have categorized the articles and recapitulated their methods based on specific organs of carcinoma origin. Further, we have summarized pertinent literature on AI methods, highlighted critical challenges and limitations, and provided insights on future research direction in automated carcinoma diagnosis. Out of 101 articles selected, most of the studies experimented on private datasets with varied image sizes, obtaining accuracy between 63% and 100%. Overall, this review highlights the need for a generalized AI-based carcinoma diagnostic system. Additionally, it is desirable to have accountable approaches to extract microscopic features from images of multiple magnifications that should mimic pathologists' evaluations.

Prabhu Swathi, Prasad Keerthana, Robels-Kelly Antonio, Lu Xuequan

2022-Jan-05

Adenocarcinoma, Artificial intelligence, Deep learning, Diagnostic system, Histopathology, Squamous cell carcinoma

General General

A Clinician's Guide to Artificial Intelligence (AI): Why and How Primary Care Should Lead the Health Care AI Revolution.

In Journal of the American Board of Family Medicine : JABFM

Artificial intelligence (AI) in health care is the future that is already here. Despite its potential as a transformational force for primary care, most primary care providers (PCPs) do not know what it is, how it will impact them and their patients, and what its key limitations and ethical pitfalls are. This article is a beginner's guide to health care AI, written for the frontline PCP. Primary care-as the dominant force at the base of the health care pyramid, with its unrivaled interconnectedness to every part of the health system and its deep relationship with patients and communities-is the most uniquely suited specialty to lead the health care AI revolution. PCPs can advance health care AI by partnering with technologists to ensure that AI use cases are relevant and human-centered, applying quality improvement methods to health care AI implementations, and advocating for inclusive and ethical AI that combats, rather than worsens, health inequities.

Lin Steven

Artificial Intelligence, Deep Learning, Delivery of Health Care, Health Equity, Information Technology, Machine Learning, Primary Health Care, Quality Improvement, Social Justice, Technology

General General

A machine learning approach for modelling the occurrence of Galba truncatula as the major intermediate host for Fasciola hepatica in Switzerland.

In Preventive veterinary medicine ; h5-index 37.0

Fasciolosis caused by the trematode Fasciola hepatica is an important parasitosis in both livestock and humans across the globe. Chronic infections in cattle are associated with considerable economic losses. As a prerequisite for an effective control and prevention of fasciolosis in cattle fine-scale predictive models on farm-level are needed. Since disease transmission will only occur where the mollusc intermediate host is present, the objective of our research was to develop a regression model that allows to predict the local presence or absence of Galba truncatula as principal intermediate host for Fasciola hepatica in Switzerland. By implementing generalized linear mixed models (GLMMs) a total amount of 70 variables were analysed for their potential influence on the likelihood πi of finding Galba truncatula at a certain site. Important site-specific features could be considered by selecting suitable modelling procedures. The statistical software R was used to conduct regression analysis, performing the grplasso and the glmmLasso method. The selection of parameters was based on 10-fold cross validation and the Bayesian Information Criterion (BIC). This yielded a total number of 19 potential predictor variables for the grplasso and 13 variables for the glmmLasso model, which also included random effects. Nine variables appeared to be relevant predictors for the occurrence of Galba truncatula in both models. These included reed/humid area, spring water, water bodies within a 100 m radius, and trees/bushes as powerful positive predictors. High soil depth, temperatures frequently exceeding 30 °C in the year preceding the search for snails and temperatures below 0 °C especially in the second year before were identified to exert an adverse effect on the occurrence of Galba truncatula. Temperatures measured near ground level proved to be more powerful predictors than macroclimatic parameters. Precipitation values seemed to be of minor impact in the given setting. Both regression models may be convenient for a fine-scale prediction of the occurrence of Galba truncatula, and thus provide useful approaches for the development of future spatial transmission models, mapping the risk of fasciolosis in Switzerland on farm-level.

Roessler Anne S, Oehm Andreas W, Knubben-Schweizer Gabriela, Groll Andreas

2022-Jan-05

cattle diseases, intermediate host, liver fluke, snail habitats, spatial risk model, trematodes

oncology Oncology

Primary results of STRONG: An open-label, multicenter, phase 3b study of fixed-dose durvalumab monotherapy in previously treated patients with urinary tract carcinoma.

In European journal of cancer (Oxford, England : 1990)

BACKGROUND : Prior durvalumab (anti-PD-L1 agent) studies in platinum-refractory metastatic urothelial carcinoma evaluated a dose of 10 mg/kg administered every two weeks. The nonrandomised phase 3b STRONG study (NCT03084471) evaluated the safety and efficacy of fixed-dose durvalumab at a more convenient dosing schedule in a previously treated patient population, more similar to a real-world clinical setting.

PATIENTS AND METHODS : 867 patients with urothelial or nonurothelial urinary tract carcinoma (UTC) who progressed on or after platinum or nonplatinum chemotherapy were treated with durvalumab 1500 mg every four weeks; 87% had an Eastern Cooperative Oncology Group performance status (ECOG PS) of 0-1, and 13% had an ECOG PS of 2. The primary end-point was the incidence of adverse events of special interest (AESIs), including immune-mediated AEs (imAEs). Secondary and exploratory end-points included overall survival (OS), objective response rate (ORR) and disease control rate (at six and 12 months) (DCR).

RESULTS : AESIs of any grade were reported in 51% of patients (8% grade ≥ 3). The incidence of imAEs was 11% (2% grade ≥ 3). The median OS was 7.0 months (95% confidence interval [CI]: 6.4-8.2) and ORR was 18% (95% CI: 14.8-20.6), with complete responses in 5% of patients and a DCR at six months of 19% (95% CI: 16.1-22.1).

CONCLUSION : Fixed-dose durvalumab monotherapy every four weeks has an acceptable safety profile and yields durable clinical activity in previously chemotherapy-treated patients with UTC. Safety and efficacy are consistent with previous durvalumab studies and other anti-PD-1/PD-L1 agents in this setting. CLINICALTRIALS.

GOV IDENTIFIER : NCT03084471https://clinicaltrials.gov/ct2/show/NCT03084471.

Sonpavde Guru P, Sternberg Cora N, Loriot Yohann, Marabelle Aurelien, Lee Jae Lyun, Fléchon Aude, Roubaud Guilhem, Pouessel Damien, Zagonel Vittorina, Calabro Fabio, Banna Giuseppe L, Shin Sang Joon, Vera-Badillo Francisco E, Powles Thomas, Hellmis Eva, Miranda Paulo A P, Lima Ana Rita, Emeribe Ugochi, Oh Sun Min, Hotte Sebastien J

2022-Jan-15

Adverse events of special interest, Durvalumab, Fixed dose, Immune checkpoint inhibitor, Immune-mediated adverse events, Immune-related adverse events, Overall survival, PD-L1, Urinary tract carcinoma, Urothelial carcinoma

General General

Evaluating Pointwise Reliability of Machine Learning prediction.

In Journal of biomedical informatics ; h5-index 55.0

Interest in Machine Learning applications to tackle clinical and biological problems is increasing. This is driven by promising results reported in many research papers, the increasing number of AI-based software products, and by the general interest in Artificial Intelligence to solve complex problems. It is therefore of importance to improve the quality of machine learning output and add safeguards to support their adoption. In addition to regulatory and logistical strategies, a crucial aspect is to detect when a Machine Learning model is not able to generalize to new unseen instances, which may originate from a population distant to that of the training population or from an under-represented subpopulation. As a result, the prediction of the machine learning model for these instances may be often wrong, given that the model is applied outside its "reliable" space of work, leading to a decreasing trust of the final users, such as clinicians. For this reason, when a model is deployed in practice, it would be important to advise users when the model's predictions may be unreliable, especially in high-stakes applications, including those in healthcare. Yet, reliability assessment of each machine learning prediction is still poorly addressed. Here, we review approaches that can support the identification of unreliable predictions, we harmonize the notation and terminology of relevant concepts, and we highlight and extend possible interrelationships and overlap among concepts. We then demonstrate, on simulated and real data for ICU in-hospital death prediction, a possible integrative framework for the identification of reliable and unreliable predictions. To do so, our proposed approach implements two complementary principles, namely the density principle and the local fit principle. The density principle verifies that the instance we want to evaluate is similar to the training set. The local fit principle verifies that the trained model performs well on training subsets that are more similar to the instance under evaluation. Our work can contribute to consolidating work in machine learning especially in medicine.

Nicora Giovanna, Rios Miguel, Abu-Hanna Ameen, Bellazzi Riccardo

2022-Jan-15

Machine Learning Trustworthiness, Predictive Reliability, Uncertainty

Public Health Public Health

Prediction of sudden cardiac arrest in the general population: Review of traditional and emerging risk factors.

In The Canadian journal of cardiology

Sudden cardiac death (SCD) is the most common and devastating outcome of sudden cardiac arrest (SCA), defined as an abrupt and unexpected cessation of cardiovascular function leading to circulatory collapse. The incidence of SCD is relatively infrequent for individuals in the general population, in the range of 0.03-0.10% per year. Yet, the absolute number of cases around the world is high due to the sheer size of the population at risk, making SCA/SCD a major global health issue. Based on conservative estimates, there are at least 2 million cases of SCA occurring worldwide on a yearly basis. As such, identification of risk factors associated with SCA in the general population is an important objective from a clinical and public health standpoint. This review will provide an in-depth discussion of established and emerging factors predictive of SCA/SCD in the general population beyond coronary artery disease and impaired left ventricular ejection fraction. Contemporary studies evaluating the association between age, sex, race, socioeconomic status and the emerging contribution of diabetes and obesity to SCD risk beyond their role as atherosclerotic risk factors will be reviewed. In addition, the role of biomarkers, particularly electrocardiographic ones, on SCA/SCD risk prediction in the general population will be discussed. Finally, the use of machine learning as a tool to facilitate SCA/SCD risk prediction will be examined.

Ha Andrew C T, Doumouras Barbara S, Wang Chang Nancy, Tranmer Joan, Lee Douglas S

2022-Jan-15

Radiology Radiology

Deep learning of early brain imaging to predict post-arrest electroencephalography.

In Resuscitation ; h5-index 66.0

INTRODUCTION : Guidelines recommend use of computerized tomography (CT) and electroencephalography (EEG) in post-arrest prognostication. Strong associations between CT and EEG might obviate the need to acquire both modalities. We quantified these associations via deep learning.

METHODS : We performed a single-center, retrospective study including comatose patients hospitalized after cardiac arrest. We extracted brain CT DICOMs, resized and registered each to a standard anatomical atlas, performed skull stripping and windowed images to optimize contrast of the gray-white junction. We classified initial EEG as generalized suppression, other highly pathological findings or benign activity. We extracted clinical information available on presentation from our prospective registry. We trained three machine learning (ML) models to predict EEG from clinical covariates. We used three state-of-the-art approaches to build multi-headed deep learning models using similar model architectures. Finally, we combined the best performing clinical and imaging models. We evaluated discrimination in test sets.

RESULTS : We included 500 patients, of whom 218 (44%) had benign EEG findings, 135 (27%) showed generalized suppression and 147 (29%) had other highly pathological findings that were most commonly (93%) burst suppression with identical bursts. Clinical ML models had moderate discrimination (test set AUCs 0.73 - 0.80). Image-based deep learning performed worse (test set AUCs 0.51 - 0.69), particularly discriminating benign from highly pathological findings. Adding image-based deep learning to clinical models improved prediction of generalized suppression due to accurate detection of severe cerebral edema.

DISCUSSION : CT and EEG provide complementary information about post-arrest brain injury. Our results do not support selective acquisition of only one of these modalities, except in the most severely injured patients.

Elmer Jonathan, Liu Chang, Pease Matthew, Arefan Dooman, Coppler Patrick J, Flickinger Katharyn, Mettenburg Joseph M, Baldwin Maria E, Barot Niravkumar, Wu Shandong

2022-Jan-15

CT imaging, brain injury, cardiac arrest, electroencephalography, machine learning

General General

Planning in the brain.

In Neuron ; h5-index 148.0

Recent breakthroughs in artificial intelligence (AI) have enabled machines to plan in tasks previously thought to be uniquely human. Meanwhile, the planning algorithms implemented by the brain itself remain largely unknown. Here, we review neural and behavioral data in sequential decision-making tasks that elucidate the ways in which the brain does-and does not-plan. To systematically review available biological data, we create a taxonomy of planning algorithms by summarizing the relevant design choices for such algorithms in AI. Across species, recording techniques, and task paradigms, we find converging evidence that the brain represents future states consistent with a class of planning algorithms within our taxonomy-focused, depth-limited, and serial. However, we argue that current data are insufficient for addressing more detailed algorithmic questions. We propose a new approach leveraging AI advances to drive experiments that can adjudicate between competing candidate algorithms.

Mattar Marcelo G, Lengyel Máté

2022-Jan-14

General General

Prediction model of acute kidney injury induced by cisplatin in older adults using a machine learning algorithm.

In PloS one ; h5-index 176.0

BACKGROUND : Early detection and prediction of cisplatin-induced acute kidney injury (Cis-AKI) are essential for the management of patients on chemotherapy with cisplatin. This study aimed to evaluate the performance of a prediction model for Cis-AKI.

METHODS : Japanese patients, who received cisplatin as the first-line chemotherapy at Fujita Health University Hospital, were enrolled in the study. The main metrics for evaluating the machine learning model were the area under the curve (AUC), accuracy, precision, recall, and F-measure. In addition, the rank of contribution as a predictive factor of Cis-AKI was determined by machine learning.

RESULTS : A total of 1,014 and 226 patients were assigned to the development and validation data groups, respectively. The current prediction model showed the highest performance in patients 65 years old and above (AUC: 0.78, accuracy: 0.77, precision: 0.38, recall: 0.70, F-measure: 0.49). The maximum daily cisplatin dose and serum albumin levels contributed the most to the prediction of Cis-AKI.

CONCLUSION : Our prediction model for Cis-AKI performed effectively in older patients.

Okawa Takaya, Mizuno Tomohiro, Hanabusa Shogo, Ikeda Takeshi, Mizokami Fumihiro, Koseki Takenao, Takahashi Kazuo, Yuzawa Yukio, Tsuboi Naotake, Yamada Shigeki, Kameya Yoshitaka

2022

General General

DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics.

In PLoS computational biology

Improvements in microscopy software and hardware have dramatically increased the pace of image acquisition, making analysis a major bottleneck in generating quantitative, single-cell data. Although tools for segmenting and tracking bacteria within time-lapse images exist, most require human input, are specialized to the experimental set up, or lack accuracy. Here, we introduce DeLTA 2.0, a purely Python workflow that can rapidly and accurately analyze images of single cells on two-dimensional surfaces to quantify gene expression and cell growth. The algorithm uses deep convolutional neural networks to extract single-cell information from time-lapse images, requiring no human input after training. DeLTA 2.0 retains all the functionality of the original version, which was optimized for bacteria growing in the mother machine microfluidic device, but extends results to two-dimensional growth environments. Two-dimensional environments represent an important class of data because they are more straightforward to implement experimentally, they offer the potential for studies using co-cultures of cells, and they can be used to quantify spatial effects and multi-generational phenomena. However, segmentation and tracking are significantly more challenging tasks in two-dimensions due to exponential increases in the number of cells. To showcase this new functionality, we analyze mixed populations of antibiotic resistant and susceptible cells, and also track pole age and growth rate across generations. In addition to the two-dimensional capabilities, we also introduce several major improvements to the code that increase accessibility, including the ability to accept many standard microscopy file formats as inputs and the introduction of a Google Colab notebook so users can try the software without installing the code on their local machine. DeLTA 2.0 is rapid, with run times of less than 10 minutes for complete movies with hundreds of cells, and is highly accurate, with error rates around 1%, making it a powerful tool for analyzing time-lapse microscopy data.

O’Connor Owen M, Alnahhas Razan N, Lugagne Jean-Baptiste, Dunlop Mary J

2022-Jan-18

General General

Motor Imagery Classification Using Inter-Task Transfer Learning via A Channel-Wise Variational Autoencoder-based Convolutional Neural Network.

In IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

Highly sophisticated control based on a brain-computer interface (BCI) requires decoding kinematic information from brain signals. The forearm is a region of the upper limb that is often used in everyday life, but intuitive movements within the same limb have rarely been investigated in previous BCI studies. In this study, we focused on various forearm movement decoding from electroencephalography (EEG) signals using a small number of samples. Ten healthy participants took part in an experiment and performed motor execution (ME) and motor imagery (MI) of the intuitive movement tasks (Dataset I). We propose a convolutional neural network using a channel-wise variational autoencoder (CVNet) based on inter-task transfer learning. We approached that training the reconstructed ME-EEG signals together will also achieve more sufficient classification performance with only a small amount of MI-EEG signals. The proposed CVNet was validated on our own Dataset I and a public dataset, BNCI Horizon 2020 (Dataset II). The classification accuracies of various movements are confirmed to be 0.83 (±0.04) and 0.69 (±0.04) for Dataset I and II, respectively. The results show that the proposed method exhibits performance increases of approximately 0.09~0.27 and 0.08~0.24 compared with the conventional models for Dataset I and II, respectively. The outcomes suggest that the training model for decoding imagined movements can be performed using data from ME and a small number of data samples from MI. Hence, it is presented the feasibility of BCI learning strategies that can sufficiently learn deep learning with a few amount of calibration dataset and time only, with stable performance.

Lee Do-Yeun, Jeong Ji-Hoon, Lee Byeong-Hoo, Lee Seong-Whan

2022-Jan-18

General General

NC-PDNet: a Density-Compensated Unrolled Network for 2D and 3D non-Cartesian MRI Reconstruction.

In IEEE transactions on medical imaging ; h5-index 74.0

Deep Learning has become a very promising avenue for magnetic resonance image (MRI) reconstruction. In this work, we explore the potential of unrolled networks for non-Cartesian acquisition settings. We design the NC-PDNet (Non-Cartesian Primal Dual Netwok), the first density-compensated (DCp) unrolled neural network, and validate the need for its key components via an ablation study. Moreover, we conduct some generalizability experiments to test this network in out-of-distribution settings, for example training on knee data and validating on brain data. The results show that NC-PDNet outperforms baseline (U-Net, Deep image prior) models both visually and quantitatively in all settings. In particular, in the 2D multi-coil acquisition scenario, the NC-PDNet provides up to a 1.2dB improvement in peak signal-to-noise ratio (PSNR) over baseline networks, while also allowing a gain of at least 1dB in PSNR in generalization settings. We provide the open-source implementation of NC-PDNet, and in particular the Non-uniform Fourier Transform in TensorFlow, tested on 2D multi-coil and 3D single-coil k-space data.

Ramzi Zaccharie, Chaithya G R, Starck Jean-Luc, Ciuciu Philippe

2022-Jan-18

Public Health Public Health

Graphical calibration curves and the integrated calibration index (ICI) for competing risk models.

In Diagnostic and prognostic research

BACKGROUND : Assessing calibration-the agreement between estimated risk and observed proportions-is an important component of deriving and validating clinical prediction models. Methods for assessing the calibration of prognostic models for use with competing risk data have received little attention.

METHODS : We propose a method for graphically assessing the calibration of competing risk regression models. Our proposed method can be used to assess the calibration of any model for estimating incidence in the presence of competing risk (e.g., a Fine-Gray subdistribution hazard model; a combination of cause-specific hazard functions; or a random survival forest). Our method is based on using the Fine-Gray subdistribution hazard model to regress the cumulative incidence function of the cause-specific outcome of interest on the predicted outcome risk of the model whose calibration we want to assess. We provide modifications of the integrated calibration index (ICI), of E50 and of E90, which are numerical calibration metrics, for use with competing risk data. We conducted a series of Monte Carlo simulations to evaluate the performance of these calibration measures when the underlying model has been correctly specified and when the model was mis-specified and when the incidence of the cause-specific outcome differed between the derivation and validation samples. We illustrated the usefulness of calibration curves and the numerical calibration metrics by comparing the calibration of a Fine-Gray subdistribution hazards regression model with that of random survival forests for predicting cardiovascular mortality in patients hospitalized with heart failure.

RESULTS : The simulations indicated that the method for constructing graphical calibration curves and the associated calibration metrics performed as desired. We also demonstrated that the numerical calibration metrics can be used as optimization criteria when tuning machine learning methods for competing risk outcomes.

CONCLUSIONS : The calibration curves and numeric calibration metrics permit a comprehensive comparison of the calibration of different competing risk models.

Austin Peter C, Putter Hein, Giardiello Daniele, van Klaveren David

2022-Jan-17

Calibration, Competing risks, Model validation, Random forests, Survival analysis, Time-to-event model

Public Health Public Health

School Closures During Social Lockdown and Mental Health, Health Behaviors, and Well-being Among Children and Adolescents During the First COVID-19 Wave: A Systematic Review.

In JAMA pediatrics ; h5-index 85.0

Importance : School closures as part of broader social lockdown measures during the COVID-19 pandemic may be associated with the health and well-being of children and adolescents.

Objective : To review published reports on the association of school closures during broader social lockdown with mental health, health behaviors, and well-being in children and adolescents aged 0 to 19 years, excluding associations with transmission of infection.

Evidence Review : Eleven databases were searched from inception to September 2020, and machine learning was applied for screening articles. A total of 16 817 records were screened, 151 were reviewed in full text, and 36 studies were included. Quality assessment was tailored to study type. A narrative synthesis of results was undertaken because data did not allow meta-analysis.

Findings : A total of 36 studies from 11 countries were identified, involving a total of 79 781 children and adolescents and 18 028 parents, which occurred during the first wave of the COVID-19 pandemic (February to July 2020). All evaluated school closure as part of broader social lockdown during the first COVID-19 wave, and the duration of school closure ranged from 1 week to 3 months. Of those, 9 (25%) were longitudinal pre-post studies, 5 (14%) were cohort, 21 (58%) were cross-sectional, and 1 (3%) was a modeling study. Thirteen studies (36%) were high quality, 17 (47%) were medium quality, and 6 (17%) were low quality. Twenty-three studies (64%) were published, 8 (22%) were online reports, and 5 (14%) were preprints. Twenty-five studies (69%) concerning mental health identified associations across emotional, behavioral, and restlessness/inattention problems; 18% to 60% of children and adolescents scored above risk thresholds for distress, particularly anxiety and depressive symptoms, and 2 studies reported no significant association with suicide. Three studies reported that child protection referrals were lower than expected number of referrals originating in schools. Three studies suggested higher screen time usage, 2 studies reported greater social media use, and 6 studies reported lower physical activity. Studies on sleep (10 studies) and diet (5 studies) provided inconclusive evidence on harms.

Conclusions and Relevance : In this narrative synthesis of reports from the first wave of the COVID-19 pandemic, studies of short-term school closures as part of social lockdown measures reported adverse mental health symptoms and health behaviors among children and adolescents. Associations between school closure and health outcomes and behaviors could not be separated from broader lockdown measures.

Viner Russell, Russell Simon, Saulle Rosella, Croker Helen, Stansfield Claire, Packer Jessica, Nicholls Dasha, Goddings Anne-Lise, Bonell Chris, Hudson Lee, Hope Steven, Ward Joseph, Schwalbe Nina, Morgan Antony, Minozzi Silvia

2022-Jan-18

General General

Pooled and person-specific machine learning models for predicting future alcohol consumption, craving, and wanting to drink: A demonstration of parallel utility.

In Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors

BACKGROUND AND AIMS : The specific factors driving alcohol consumption, craving, and wanting to drink, are likely different for different people. The present study sought to apply statistical classification methods to idiographic time series data in order to identify person-specific predictors of future drinking-relevant behavior, affect, and cognitions in a college student sample.

DESIGN : Participants were sent 8 mobile phone surveys per day for 15 days. Each survey assessed the number of drinks consumed since the previous survey, as well as positive affect, negative affect, alcohol craving, drinking expectancies, perceived alcohol consumption norms, impulsivity, and social and situational context. Each individual's data were split into training and testing sets, so that trained models could be validated using person-specific out-of-sample data. Elastic net regularization was used to select a subset of a set of 40 variables to be used to predict either alcohol consumption, craving, or wanting to drink, forward in time.

SETTING : A west-coast university.

PARTICIPANTS : Thirty-three university students who had consumed alcohol in their lifetime.

MEASUREMENTS : Mobile phone surveys.

FINDINGS : Averaging across participants, accurate out-of-sample predictions of future drinking were made 76% of the time. For craving, the mean out-of-sample R² value was .27. For wanting to drink, the mean out-of-sample R² value was .27.

CONCLUSION : Using a person-specific constellation of psychosocial and temporal variables, it may be possible to accurately predict drinking behavior, affect, and cognitions before they occur. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Soyster Peter D, Ashlock Leighann, Fisher Aaron J

2021-Apr-22

General General

High Accuracy Real-Time Multi-Gas Identification by a Batch-Uniform Gas Sensor Array and Deep Learning Algorithm.

In ACS sensors

Semiconductor metal oxide (SMO) gas sensors are attracting great attention as next-generation environmental monitoring sensors. However, there are limitations to the actual application of SMO gas sensors due to their low selectivity. Although the electronic nose (E-nose) systems based on a sensor array are regarded as a solution for the selectivity issue, poor accuracy caused by the nonuniformity of the fabricated gas sensors and difficulty of real-time gas detection have yet to be resolved. In this study, these problems have been solved by fabricating uniform gas sensor arrays and applying the deep learning algorithm to the data from the sensor arrays. Nanocolumnar films of metal oxides (SnO2, In2O3, WO3, and CuO) with a high batch uniformity deposited through glancing angle deposition were used as the sensing materials. The convolutional neural network (CNN) using the input data as a matrix form was adopted as a learning algorithm, which could conduct pattern recognition of the sensor responses. Finally, real-time selective gas detection for CO, NH3, NO2, CH4, and acetone (C3H6O) gas was achieved (minimum response time of 1, 8, 5, 19, and 2 s, respectively) with an accuracy of 98% by applying preprocessed response data to the CNN.

Kang Mingu, Cho Incheol, Park Jaeho, Jeong Jaeseok, Lee Kichul, Lee Byeongju, Del Orbe Henriquez Dionisio, Yoon Kukjin, Park Inkyu

2022-Jan-18

deep learning, electronic nose, gas sensor array, glancing angle deposition, real-time gas identification

General General

Molecular Mechanism for the Allosteric Inhibition of the Human Serotonin Transporter by Antidepressant Escitalopram.

In ACS chemical neuroscience

Human serotine transporter (hSERT) is one of the most influential drug targets, and its allosteric modulators (e.g., escitalopram) have emerged to be the next-generation medication for psychiatric disorders. However, the molecular mechanism underlying the allosteric modulation of hSERT is still elusive. Here, the simulation strategies of conventional (cMD) and steered (SMD) molecular dynamics were applied to investigate this molecular mechanism from distinct perspectives. First, cMD simulations revealed that escitalopram's binding to hSERT's allosteric site simultaneously enhanced its binding to the orthosteric site. Then, SMD simulation identified that the occupation of hSERT's allosteric site by escitalopram could also block its dissociation from the orthosteric site. Finally, by comparing the simulated structures of two hSERT-escitalopram complexes with and without allosteric modulation, a new conformational coupling between an extracellular (Arg104-Glu494) and an intracellular (Lys490-Glu494) salt bridge was identified. In summary, this study explored the mechanism underlying the allosteric modulation of hSERT by collectively applying two MD simulation strategies, which could facilitate our understanding of the allosteric modulations of not only hSERT but also other clinically important therapeutic targets.

Xue Weiwei, Fu Tingting, Deng Shengzhe, Yang Fengyuan, Yang Jingyi, Zhu Feng

2022-Jan-18

allostery, drug design, escitalopram, molecular dynamics, serotonin transporter

General General

[Comparative chemoreactome analysis of the synergism of vinpocetine, piracetam, and cinnarizine molecules].

In Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova

OBJECTIVE : Neuroprotective and nootropic drugs often exhibit complementary, «synergistic» effects, the consideration of which is important for choosing the most effective and safe drug combinations.

MATERIAL AND METHODS : Chemoinformatic analysis of vinpocetine, piracetam and cinnarizine on neuron cultures, on model organisms (mice, rats) based on modern data mining and machine learning methods.

RESULTS : The paper presents the results of the chemoreactom analysis of vinpocetine, piracetam, and cinnarizine. Estimates of various biological activities of molecules on neuronal cultures, on model organisms (mice, rats) and estimates of modulation of the activity of target proteins in rats and humans were obtained. The data obtained made it possible to quantify the value of the synergism score for the combination «vinpocetine + piracetam» (54 points) compared with the combination «piracetam + cinnarizine» (25 points).

CONCLUSIONS : The combination of «vinpocetine + piracetam» in the fixed combination (Vinpotropil) is thus more preferable for combined use than for the combination of «piracetam + cinnarizine».

Gromova O A, Torshin I Yu

2021

Vinpotropil, chemoinformatics, cinnarizine, combination therapy, data mining, neuroprotection, piracetam, synergism, vinpocetine

Ophthalmology Ophthalmology

Monitoring Retinoblastoma by Machine Learning of Aqueous Humor Metabolic Fingerprinting.

In Small methods

The most common intraocular pediatric malignancy, retinoblastoma (RB), accounts for ≈10% of cancer in children. Efficient monitoring can enhance living quality of patients and 5-year survival ratio of RB up to 95%. However, RB monitoring is still insufficient in regions with limited resources and the mortality may even reach over 70% in such areas. Here, an RB monitoring platform by machine learning of aqueous humor metabolic fingerprinting (AH-MF) is developed, using nanoparticle enhanced laser desorption/ionization mass spectrometry (LDI MS). The direct AH-MF of RB free of sample pre-treatment is recorded, with both high reproducibility (coefficient of variation < 10%) and sensitivity (low to 0.3 pmol) at sample volume down to 40 nL only. Further, early and advanced RB patients with area-under-the-curve over 0.9 and accuracy over 80% are differentiated, through machine learning of AH-MF. Finally, a metabolic biomarker panel of 7 metabolites through accurate MS and tandem MS (MS/MS) with pathway analysis to monitor RB is identified. This work can contribute to advanced metabolic analysis of eye diseases including but not limited to RB and screening of new potential metabolic targets toward therapeutic intervention.

Liu Wanshan, Luo Yingxiu, Dai Jingjing, Yang Ludi, Huang Lin, Wang Ruimin, Chen Wei, Huang Yida, Sun Shiyu, Cao Jing, Wu Jiao, Han Minglei, Fan Jiayan, He Mengjia, Qian Kun, Fan Xianqun, Jia Renbing

2022-Jan

aqueous humor, biomarkers, mass spectrometry, metabolic fingerprinting, retinoblastoma

General General

Identification of glomerulosclerosis using IBM Watson and shallow neural networks.

In Journal of nephrology

BACKGROUND : Advanced stages of different renal diseases feature glomerular sclerosis at a histological level which is observed by light microscopy on tissue samples obtained by performing a kidney biopsy. Computer-aided diagnosis (CAD) systems leverage the potential of artificial intelligence (AI) in healthcare to support physicians in the diagnostic process.

METHODS : We propose a novel CAD system that processes histological images and discriminates between sclerotic and non-sclerotic glomeruli. To this goal, we designed, tested, and compared two artificial neural network (ANN) classifiers. The former implements a shallow ANN classifying hand-crafted features extracted from Regions of Interest (ROIs) by means of image-processing procedures. The latter, instead, employs the IBM Watson Visual Recognition System, which uses a deep artificial neural network making decisions taking the images as input, without the need to design any procedure for describing images with features. The input dataset consisted of 428 sclerotic glomeruli and 2344 non-sclerotic glomeruli derived from images of kidney biopsies scanned by the Aperio ScanScope System.

RESULTS : Both AI approaches allowed to very accurately distinguish (mean MCC 0.95 and mean Accuracy 0.99) between sclerotic and non-sclerotic glomeruli. Although the systems may seem interchangeable, the approach based on feature extraction and classification would allow clinicians to gain information on the most discriminating features. In fact, further procedures could explain the classifier's decision by analysing which subset of features impacted the most on the final decision.

CONCLUSIONS : We developed a customizable support system that can facilitate the work of renal pathologists both in clinical and research settings.

Pesce Francesco, Albanese Federica, Mallardi Davide, Rossini Michele, Pasculli Giuseppe, Suavo-Bulzis Paola, Granata Antonio, Brunetti Antonio, Cascarano Giacomo Donato, Bevilacqua Vitoantonio, Gesualdo Loreto

2022-Jan-18

Artificial intelligence, Glomerulosclerosis, IBM Watson, Renal biopsy

Public Health Public Health

School Closures During Social Lockdown and Mental Health, Health Behaviors, and Well-being Among Children and Adolescents During the First COVID-19 Wave: A Systematic Review.

In JAMA pediatrics ; h5-index 85.0

Importance : School closures as part of broader social lockdown measures during the COVID-19 pandemic may be associated with the health and well-being of children and adolescents.

Objective : To review published reports on the association of school closures during broader social lockdown with mental health, health behaviors, and well-being in children and adolescents aged 0 to 19 years, excluding associations with transmission of infection.

Evidence Review : Eleven databases were searched from inception to September 2020, and machine learning was applied for screening articles. A total of 16 817 records were screened, 151 were reviewed in full text, and 36 studies were included. Quality assessment was tailored to study type. A narrative synthesis of results was undertaken because data did not allow meta-analysis.

Findings : A total of 36 studies from 11 countries were identified, involving a total of 79 781 children and adolescents and 18 028 parents, which occurred during the first wave of the COVID-19 pandemic (February to July 2020). All evaluated school closure as part of broader social lockdown during the first COVID-19 wave, and the duration of school closure ranged from 1 week to 3 months. Of those, 9 (25%) were longitudinal pre-post studies, 5 (14%) were cohort, 21 (58%) were cross-sectional, and 1 (3%) was a modeling study. Thirteen studies (36%) were high quality, 17 (47%) were medium quality, and 6 (17%) were low quality. Twenty-three studies (64%) were published, 8 (22%) were online reports, and 5 (14%) were preprints. Twenty-five studies (69%) concerning mental health identified associations across emotional, behavioral, and restlessness/inattention problems; 18% to 60% of children and adolescents scored above risk thresholds for distress, particularly anxiety and depressive symptoms, and 2 studies reported no significant association with suicide. Three studies reported that child protection referrals were lower than expected number of referrals originating in schools. Three studies suggested higher screen time usage, 2 studies reported greater social media use, and 6 studies reported lower physical activity. Studies on sleep (10 studies) and diet (5 studies) provided inconclusive evidence on harms.

Conclusions and Relevance : In this narrative synthesis of reports from the first wave of the COVID-19 pandemic, studies of short-term school closures as part of social lockdown measures reported adverse mental health symptoms and health behaviors among children and adolescents. Associations between school closure and health outcomes and behaviors could not be separated from broader lockdown measures.

Viner Russell, Russell Simon, Saulle Rosella, Croker Helen, Stansfield Claire, Packer Jessica, Nicholls Dasha, Goddings Anne-Lise, Bonell Chris, Hudson Lee, Hope Steven, Ward Joseph, Schwalbe Nina, Morgan Antony, Minozzi Silvia

2022-Jan-18

General General

Harnessing Artificial Intelligence for Health Message Generation: The Folic Acid Message Engine.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Communication campaigns using social media can raise public awareness; however, they are difficult to sustain. A barrier is the need to generate and constantly post novel but on-topic messages, which creates a resource-intensive bottleneck.

OBJECTIVE : In this study, we aim to harness the latest advances in artificial intelligence (AI) to build a pilot system that can generate many candidate messages, which could be used for a campaign to suggest novel, on-topic candidate messages. The issue of folic acid, a B-vitamin that helps prevent major birth defects, serves as an example; however, the system can work with other issues that could benefit from higher levels of public awareness.

METHODS : We used the Generative Pretrained Transformer-2 architecture, a machine learning model trained on a large natural language corpus, and fine-tuned it using a data set of autodownloaded tweets about #folicacid. The fine-tuned model was then used as a message engine, that is, to create new messages about this topic. We conducted a web-based study to gauge how human raters evaluate AI-generated tweet messages compared with original, human-crafted messages.

RESULTS : We found that the Folic Acid Message Engine can easily create several hundreds of new messages that appear natural to humans. Web-based raters evaluated the clarity and quality of a human-curated sample of AI-generated messages as on par with human-generated ones. Overall, these results showed that it is feasible to use such a message engine to suggest messages for web-based campaigns that focus on promoting awareness.

CONCLUSIONS : The message engine can serve as a starting point for more sophisticated AI-guided message creation systems for health communication. Beyond the practical potential of such systems for campaigns in the age of social media, they also hold great scientific potential for the quantitative analysis of message characteristics that promote successful communication. We discuss future developments and obvious ethical challenges that need to be addressed as AI technologies for health persuasion enter the stage.

Schmälzle Ralf, Wilcox Shelby

2022-Jan-18

NLP, campaigns, health communication, health promotion, human-centered AI

General General

Improving Diabetes-Related Biomedical Literature Exploration in the Clinical Decision-making Process via Interactive Classification and Topic Discovery: Methodology Development Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The amount of available textual health data such as scientific and biomedical literature is constantly growing and becoming more and more challenging for health professionals to properly summarize those data and practice evidence-based clinical decision making. Moreover, the exploration of unstructured health text data is challenging for professionals without computer science knowledge due to limited time, resources, and skills. Current tools to explore text data lack ease of use, require high computational efforts, and incorporate domain knowledge and focus on topics of interest with difficulty.

OBJECTIVE : We developed a methodology able to explore and target topics of interest via an interactive user interface for health professionals with limited computer science knowledge. We aim to reach near state-of-the-art performance while reducing memory consumption, increasing scalability, and minimizing user interaction effort to improve the clinical decision-making process. The performance was evaluated on diabetes-related abstracts from PubMed.

METHODS : The methodology consists of 4 parts: (1) a novel interpretable hierarchical clustering of documents where each node is defined by headwords (words that best represent the documents in the node), (2) an efficient classification system to target topics, (3) minimized user interaction effort through active learning, and (4) a visual user interface. We evaluated our approach on 50,911 diabetes-related abstracts providing a hierarchical Medical Subject Headings (MeSH) structure, a unique identifier for a topic. Hierarchical clustering performance was compared against the implementation in the machine learning library scikit-learn. On a subset of 2000 randomly chosen diabetes abstracts, our active learning strategy was compared against 3 other strategies: random selection of training instances, uncertainty sampling that chooses instances about which the model is most uncertain, and an expected gradient length strategy based on convolutional neural networks (CNNs).

RESULTS : For the hierarchical clustering performance, we achieved an F1 score of 0.73 compared to 0.76 achieved by scikit-learn. Concerning active learning performance, after 200 chosen training samples based on these strategies, the weighted F1 score of all MeSH codes resulted in a satisfying 0.62 F1 score using our approach, 0.61 using the uncertainty strategy, 0.63 using the CNN, and 0.45 using the random strategy. Moreover, our methodology showed a constant low memory use with increased number of documents.

CONCLUSIONS : We proposed an easy-to-use tool for health professionals with limited computer science knowledge who combine their domain knowledge with topic exploration and target specific topics of interest while improving transparency. Furthermore, our approach is memory efficient and highly parallelizable, making it interesting for large Big Data sets. This approach can be used by health professionals to gain deep insights into biomedical literature to ultimately improve the evidence-based clinical decision making process.

Ahne Adrian, Fagherazzi Guy, Tannier Xavier, Czernichow Thomas, Orchard Francisco

2022-Jan-18

active learning, classification, clinical decision making, clinical decision support, digital health, evidence-based medicine, hierarchical clustering, medical informatics, memory consumption, natural language processing, transparency

Public Health Public Health

Crowdsourcing for Machine Learning in Public Health Surveillance: Lessons Learned From Amazon Mechanical Turk.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Crowdsourcing services, such as Amazon Mechanical Turk (AMT), allow researchers to use the collective intelligence of a wide range of web users for labor-intensive tasks. As the manual verification of the quality of the collected results is difficult because of the large volume of data and the quick turnaround time of the process, many questions remain to be explored regarding the reliability of these resources for developing digital public health systems.

OBJECTIVE : This study aims to explore and evaluate the application of crowdsourcing, generally, and AMT, specifically, for developing digital public health surveillance systems.

METHODS : We collected 296,166 crowd-generated labels for 98,722 tweets, labeled by 610 AMT workers, to develop machine learning (ML) models for detecting behaviors related to physical activity, sedentary behavior, and sleep quality among Twitter users. To infer the ground truth labels and explore the quality of these labels, we studied 4 statistical consensus methods that are agnostic of task features and only focus on worker labeling behavior. Moreover, to model the meta-information associated with each labeling task and leverage the potential of context-sensitive data in the truth inference process, we developed 7 ML models, including traditional classifiers (offline and active), a deep learning-based classification model, and a hybrid convolutional neural network model.

RESULTS : Although most crowdsourcing-based studies in public health have often equated majority vote with quality, the results of our study using a truth set of 9000 manually labeled tweets showed that consensus-based inference models mask underlying uncertainty in data and overlook the importance of task meta-information. Our evaluations across 3 physical activity, sedentary behavior, and sleep quality data sets showed that truth inference is a context-sensitive process, and none of the methods studied in this paper were consistently superior to others in predicting the truth label. We also found that the performance of the ML models trained on crowd-labeled data was sensitive to the quality of these labels, and poor-quality labels led to incorrect assessment of these models. Finally, we have provided a set of practical recommendations to improve the quality and reliability of crowdsourced data.

CONCLUSIONS : Our findings indicate the importance of the quality of crowd-generated labels in developing ML models designed for decision-making purposes, such as public health surveillance decisions. A combination of inference models outlined and analyzed in this study could be used to quantitatively measure and improve the quality of crowd-generated labels for training ML models.

Shakeri Hossein Abad Zahra, Butler Gregory P, Thompson Wendy, Lee Joon

2022-Jan-18

crowdsourcing, digital public health surveillance, machine learning, public health database, social media analysis

General General

Diagnosis of schizophrenia with functional connectome data: a graph-based convolutional neural network approach.

In BMC neuroscience

Previous deep learning methods have not captured graph or network representations of brain structural or functional connectome data. To address this, we developed the BrainNet-Global Covariance Pooling-Attention Convolutional Neural Network (BrainNet-GA CNN) by incorporating BrainNetCNN and global covariance pooling into the self-attention mechanism. Resting-state functional magnetic resonance imaging data were obtained from 171 patients with schizophrenia spectrum disorders (SSDs) and 161 healthy controls (HCs). We conducted an ablation analysis of the proposed BrainNet-GA CNN and quantitative performance comparisons with competing methods using the nested tenfold cross validation strategy. The performance of our model was compared with competing methods. Discriminative connections were visualized using the gradient-based explanation method and compared with the results obtained using functional connectivity analysis. The BrainNet-GA CNN showed an accuracy of 83.13%, outperforming other competing methods. Among the top 10 discriminative connections, some were associated with the default mode network and auditory network. Interestingly, these regions were also significant in the functional connectivity analysis. Our findings suggest that the proposed BrainNet-GA CNN can classify patients with SSDs and HCs with higher accuracy than other models. Visualization of salient regions provides important clinical information. These results highlight the potential use of the BrainNet-GA CNN in the diagnosis of schizophrenia.

Oh Kang-Han, Oh Il-Seok, Tsogt Uyanga, Shen Jie, Kim Woo-Sung, Liu Congcong, Kang Nam-In, Lee Keon-Hak, Sui Jing, Kim Sung-Wan, Chung Young-Chul

2022-Jan-17

Brain network, Convolutional neural network, Functional connectome, Global covariance pooling, Schizophrenia, Self-attention mechanism

Internal Medicine Internal Medicine

Digital Healthcare for Airway Diseases from Personal Environmental Exposure.

In Yonsei medical journal

Digital technologies have emerged in various dimensions of human life, ranging from education to professional services to well-being. In particular, health products and services have expanded by the use and development of artificial intelligence, mobile health applications, and wearable electronic devices. Such advancements have enabled accurate and updated tracking and modeling of health conditions. For instance, digital health technologies are capable of measuring environmental pollution and predicting its adverse health effects. Several health conditions, including chronic airway diseases such as asthma and chronic obstructive pulmonary disease, can be exacerbated by pollution. These diseases impose substantial health burdens with high morbidity and mortality. Recently, efforts have been made to develop digital technologies to alleviate such conditions. Moreover, the COVID-19 pandemic has facilitated the application of telemedicine and telemonitoring for patients with chronic airway diseases. This article reviews current trends and studies in digital technology utilization for investigating and managing environmental exposure and chronic airway diseases. First, we discussed the recent progression of digital technologies in general environmental healthcare. Then, we summarized the capacity of digital technologies in predicting exacerbation and self-management of airway diseases. Concluding these reviews, we provided suggestions to improve digital health technologies' abilities to reduce the adverse effects of environmental exposure in chronic airway diseases, based on personal exposure-response modeling.

Park Youngmok, Lee Chanho, Jung Ji Ye

2022-Jan

Asthma, chronic obstructive pulmonary disease, digital technology, environment, wearable electronic devices

General General

Hippocampal sharp wave-ripples and the associated sequence replay emerge from structured synaptic interactions in a network model of area CA3.

In eLife

Hippocampal place cells are activated sequentially as an animal explores its environment. These activity sequences are internally recreated ('replayed'), either in the same or reversed order, during bursts of activity (sharp wave-ripples; SWRs) that occur in sleep and awake rest. SWR-associated replay is thought to be critical for the creation and maintenance of long-term memory. In order to identify the cellular and network mechanisms of SWRs and replay, we constructed and simulated a data-driven model of area CA3 of the hippocampus. Our results show that the chain-like structure of recurrent excitatory interactions established during learning not only determines the content of replay, but is essential for the generation of the SWRs as well. We find that bidirectional replay requires the interplay of the experimentally confirmed, temporally symmetric plasticity rule, and cellular adaptation. Our model provides a unifying framework for diverse phenomena involving hippocampal plasticity, representations, and dynamics, and suggests that the structured neural codes induced by learning may have greater influence over cortical network states than previously appreciated.

Ecker András, Bagi Bence, Vértes Eszter, Steinbach-Németh Orsolya, Karlocai Maria Rita, Papp Orsolya I, Miklós István, Hájos Norbert, Freund Tamás, Gulyás Attila I, Káli Szabolcs

2022-Jan-18

computational biology, mouse, neuroscience, systems biology

General General

Human gut microbiome aging clocks based on taxonomic and functional signatures through multi-view learning.

In Gut microbes

The human gut microbiome is a complex ecosystem that is closely related to the aging process. However, there is currently no reliable method to make full use of the metagenomics data of the gut microbiome to determine the age of the host. In this study, we considered the influence of geographical factors on the gut microbiome, and a total of 2604 filtered metagenomics data from the gut microbiome were used to construct an age prediction model. Then, we developed an ensemble model with multiple heterogeneous algorithms and combined species and pathway profiles for multi-view learning. By integrating gut microbiome metagenomics data and adjusting host confounding factors, the model showed high accuracy (R2 = 0.599, mean absolute error = 8.33 years). Besides, we further interpreted the model and identify potential biomarkers for the aging process. Among these identified biomarkers, we found that Finegoldia magna, Bifidobacterium dentium, and Clostridium clostridioforme had increased abundance in the elderly. Moreover, the utilization of amino acids by the gut microbiome undergoes substantial changes with increasing age which have been reported as the risk factors for age-associated malnutrition and inflammation. This model will be helpful for the comprehensive utilization of multiple omics data, and will allow greater understanding of the interaction between microorganisms and age to realize the targeted intervention of aging.

Chen Yutao, Wang Hongchao, Lu Wenwei, Wu Tong, Yuan Weiwei, Zhu Jinlin, Lee Yuan Kun, Zhao Jianxin, Zhang Hao, Chen Wei

Gut microbiome, age, ensemble, machine learning, metagenomic, multi-view, regression

General General

In silico prediction of post-translational modifications in therapeutic antibodies.

In mAbs

Monoclonal antibodies are susceptible to chemical and enzymatic modifications during manufacturing, storage, and shipping. Deamidation, isomerization, and oxidation can compromise the potency, efficacy, and safety of therapeutic antibodies. Recently, in silico tools have been used to identify liable residues and engineer antibodies with better chemical stability. Computational approaches for predicting deamidation, isomerization, oxidation, glycation, carbonylation, sulfation, and hydroxylation are reviewed here. Although liable motifs have been used to improve the chemical stability of antibodies, the accuracy of in silico predictions can be improved using machine learning and molecular dynamic simulations. In addition, there are opportunities to improve predictions for specific stress conditions, develop in silico prediction of novel modifications in antibodies, and predict the impact of modifications on physical stability and antigen-binding.

Vatsa Shabdita

In silico prediction, chemical stability, developability, post-translational modifications, therapeutic antibody development

General General

Revealing General Patterns of Microbiomes That Transcend Systems: Potential and Challenges of Deep Transfer Learning.

In mSystems

A growing body of research has established that the microbiome can mediate the dynamics and functional capacities of diverse biological systems. Yet, we understand little about what governs the response of these microbial communities to host or environmental changes. Most efforts to model microbiomes focus on defining the relationships between the microbiome, host, and environmental features within a specified study system and therefore fail to capture those that may be evident across multiple systems. In parallel with these developments in microbiome research, computer scientists have developed a variety of machine learning tools that can identify subtle, but informative, patterns from complex data. Here, we recommend using deep transfer learning to resolve microbiome patterns that transcend study systems. By leveraging diverse public data sets in an unsupervised way, such models can learn contextual relationships between features and build on those patterns to perform subsequent tasks (e.g., classification) within specific biological contexts.

David Maude M, Tataru Christine, Pope Quintin, Baker Lydia J, English Mary K, Epstein Hannah E, Hammer Austin, Kent Michael, Sieler Michael J, Mueller Ryan S, Sharpton Thomas J, Tomas Fiona, Vega Thurber Rebecca, Fern Xiaoli Z

2022-Jan-18

deep learning, embeddings, machine learning, microbial ecology

Radiology Radiology

Artificial Intelligence for Reducing Workload in Breast Cancer Screening with Digital Breast Tomosynthesis.

In Radiology ; h5-index 91.0

Background Digital breast tomosynthesis (DBT) has higher diagnostic accuracy than digital mammography, but interpretation time is substantially longer. Artificial intelligence (AI) could improve reading efficiency. Purpose To evaluate the use of AI to reduce workload by filtering out normal DBT screens. Materials and Methods The retrospective study included 13 306 DBT examinations from 9919 women performed between June 2013 and November 2018 from two health care networks. The cohort was split into training, validation, and test sets (3948, 1661, and 4310 women, respectively). A workflow was simulated in which the AI model classified cancer-free examinations that could be dismissed from the screening worklist and used the original radiologists' interpretations on the rest of the worklist examinations. The AI system was also evaluated with a reader study of five breast radiologists reading the DBT mammograms of 205 women. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and recall rate were evaluated in both studies. Statistics were computed across 10 000 bootstrap samples to assess 95% CIs, noninferiority, and superiority tests. Results The model was tested on 4310 screened women (mean age, 60 years ± 11 [standard deviation]; 5182 DBT examinations). Compared with the radiologists' performance (417 of 459 detected cancers [90.8%], 477 recalls in 5182 examinations [9.2%]), the use of AI to automatically filter out cases would result in 39.6% less workload, noninferior sensitivity (413 of 459 detected cancers; 90.0%; P = .002), and 25% lower recall rate (358 recalls in 5182 examinations; 6.9%; P = .002). In the reader study, AUC was higher in the standalone AI compared with the mean reader (0.84 vs 0.81; P = .002). Conclusion The artificial intelligence model was able to identify normal digital breast tomosynthesis screening examinations, which decreased the number of examinations that required radiologist interpretation in a simulated clinical workflow. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Philpotts in this issue.

Shoshan Yoel, Bakalo Ran, Gilboa-Solomon Flora, Ratner Vadim, Barkan Ella, Ozery-Flato Michal, Amit Mika, Khapun Daniel, Ambinder Emily B, Oluyemi Eniola T, Panigrahi Babita, DiCarlo Philip A, Rosen-Zvi Michal, Mullen Lisa A

2022-Jan-18

Radiology Radiology

Semisupervised Training of a Brain MRI Tumor Detection Model Using Mined Annotations.

In Radiology ; h5-index 91.0

Background Artificial intelligence (AI) applications for cancer imaging conceptually begin with automated tumor detection, which can provide the foundation for downstream AI tasks. However, supervised training requires many image annotations, and performing dedicated post hoc image labeling is burdensome and costly. Purpose To investigate whether clinically generated image annotations can be data mined from the picture archiving and communication system (PACS), automatically curated, and used for semisupervised training of a brain MRI tumor detection model. Materials and Methods In this retrospective study, the cancer center PACS was mined for brain MRI scans acquired between January 2012 and December 2017 and included all annotated axial T1 postcontrast images. Line annotations were converted to boxes, excluding boxes shorter than 1 cm or longer than 7 cm. The resulting boxes were used for supervised training of object detection models using RetinaNet and Mask region-based convolutional neural network (R-CNN) architectures. The best-performing model trained from the mined data set was used to detect unannotated tumors on training images themselves (self-labeling), automatically correcting many of the missing labels. After self-labeling, new models were trained using this expanded data set. Models were scored for precision, recall, and F1 using a held-out test data set comprising 754 manually labeled images from 100 patients (403 intra-axial and 56 extra-axial enhancing tumors). Model F1 scores were compared using bootstrap resampling. Results The PACS query extracted 31 150 line annotations, yielding 11 880 boxes that met inclusion criteria. This mined data set was used to train models, yielding F1 scores of 0.886 for RetinaNet and 0.908 for Mask R-CNN. Self-labeling added 18 562 training boxes, improving model F1 scores to 0.935 (P < .001) and 0.954 (P < .001), respectively. Conclusion The application of semisupervised learning to mined image annotations significantly improved tumor detection performance, achieving an excellent F1 score of 0.954. This development pipeline can be extended for other imaging modalities, repurposing unused data silos to potentially enable automated tumor detection across radiologic modalities. © RSNA, 2022 Online supplemental material is available for this article.

Swinburne Nathaniel C, Yadav Vivek, Kim Julie, Choi Ye R, Gutman David C, Yang Jonathan T, Moss Nelson, Stone Jacqueline, Tisnado Jamie, Hatzoglou Vaios, Haque Sofia S, Karimi Sasan, Lyo John, Juluru Krishna, Pichotta Karl, Gao Jianjiong, Shah Sohrab P, Holodny Andrei I, Young Robert J

2022-Jan-18

Radiology Radiology

Deep Learning Reconstruction Shows Better Lung Nodule Detection for Ultra-Low-Dose Chest CT.

In Radiology ; h5-index 91.0

Background Ultra-low-dose (ULD) CT could facilitate the clinical implementation of large-scale lung cancer screening while minimizing the radiation dose. However, traditional image reconstruction methods are associated with image noise in low-dose acquisitions. Purpose To compare the image quality and lung nodule detectability of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-V (ASIR-V) in ULD CT. Materials and Methods Patients who underwent noncontrast ULD CT (performed at 0.07 or 0.14 mSv, similar to a single chest radiograph) and contrast-enhanced chest CT (CECT) from April to June 2020 were included in this prospective study. ULD CT images were reconstructed with filtered back projection (FBP), ASIR-V, and DLIR. Three-dimensional segmentation of lung tissue was performed to evaluate image noise. Radiologists detected and measured nodules with use of a deep learning-based nodule assessment system and recognized malignancy-related imaging features. Bland-Altman analysis and repeated-measures analysis of variance were used to evaluate the differences between ULD CT images and CECT images. Results A total of 203 participants (mean age ± standard deviation, 61 years ± 12; 129 men) with 1066 nodules were included, with 100 scans at 0.07 mSv and 103 scans at 0.14 mSv. The mean lung tissue noise ± standard deviation was 46 HU ± 4 for CECT and 59 HU ± 4, 56 HU ± 4, 53 HU ± 4, 54 HU ± 4, and 51 HU ± 4 in FBP, ASIR-V level 40%, ASIR-V level 80% (ASIR-V-80%), medium-strength DLIR, and high-strength DLIR (DLIR-H), respectively, of ULD CT scans (P < .001). The nodule detection rates of FBP reconstruction, ASIR-V-80%, and DLIR-H were 62.5% (666 of 1066 nodules), 73.3% (781 of 1066 nodules), and 75.8% (808 of 1066 nodules), respectively (P < .001). Bland-Altman analysis showed the percentage difference in long diameter from that of CECT was 9.3% (95% CI of the mean: 8.0, 10.6), 9.2% (95% CI of the mean: 8.0, 10.4), and 6.2% (95% CI of the mean: 5.0, 7.4) in FBP reconstruction, ASIR-V-80%, and DLIR-H, respectively (P < .001). Conclusion Compared with adaptive statistical iterative reconstruction-V, deep learning image reconstruction reduced image noise, increased nodule detection rate, and improved measurement accuracy on ultra-low-dose chest CT images. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Lee in this issue.

Jiang Beibei, Li Nianyun, Shi Xiaomeng, Zhang Shuai, Li Jianying, de Bock Geertruida H, Vliegenthart Rozemarijn, Xie Xueqian

2022-Jan-18

Dermatology Dermatology

Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis.

In Yonsei medical journal

PURPOSE : Several artificial intelligence (AI) models for the detection and prediction of cardiovascular-related diseases, including arrhythmias, diabetes, and sleep apnea, have been reported. This systematic review and meta-analysis aimed to identify AI models developed for or applicable to wearable and mobile devices for diverse cardiovascular-related diseases.

MATERIALS AND METHODS : The searched databases included Medline, Embase, and Cochrane Library. For AI models for atrial fibrillation (AF) detection, a meta-analysis of diagnostic accuracy was performed to summarize sensitivity and specificity.

RESULTS : A total of 102 studies were included in the qualitative review. There were AI models for the detection of arrythmia (n=62), followed by sleep apnea (n=11), peripheral vascular diseases (n=6), diabetes mellitus (n=5), hyper/hypotension (n=5), valvular heart disease (n=4), heart failure (n=3), myocardial infarction and cardiac arrest (n=2), and others (n=4). For quantitative analysis of 26 studies reporting AI models for AF detection, meta-analyzed sensitivity was 94.80% and specificity was 96.96%. Deep neural networks showed superior performance [meta-analyzed area under receiver operating characteristics curve (AUROC) of 0.981] compared to conventional machine learning algorithms (meta-analyzed AUROC of 0.961). However, AI models tested with proprietary dataset (meta-analyzed AUROC of 0.972) or data acquired from wearable devices (meta-analyzed AUROC of 0.977) showed inferior performance than those with public dataset (meta-analyzed AUROC of 0.986) or data from in-hospital devices (meta-analyzed AUROC of 0.983).

CONCLUSION : This review found that AI models for diverse cardiovascular-related diseases are being developed, and that they are gradually developing into a form that is suitable for wearable and mobile devices.

Lee Solam, Chu Yuseong, Ryu Jiseung, Park Young Jun, Yang Sejung, Koh Sang Baek

2022-Jan

Electrocardiography, artificial intelligence, cardiovascular disease, deep learning, machine learning, photoplethysmography

General General

Concept and Proof of the Lifelog Bigdata Platform for Digital Healthcare and Precision Medicine on the Cloud.

In Yonsei medical journal

PURPOSE : We propose the Lifelog Bigdata Platform as a sustainable digital healthcare system based on individual-centric lifelog datasets and describe the standardization of lifelog and clinical data in its full-cycle management system.

MATERIALS AND METHODS : The Lifelog Bigdata Platform was developed by Yonsei Wonju Health System on the cloud to support digital healthcare and precision medicine. It consists of five core components: data acquisition system, de-identification of individual information, lifelog integration, analyzer, and service. We designed a gathering system into a dedicated virtual machine to save lifelog or clinical outcomes and established standard guidelines for maintaining the quality of gathering procedures. We used standard integration keys to integrate the lifelog and clinical data. Metadata were generated from the data warehouse after loading combined or fragmented data on it. We analyzed the de-identified lifelog and clinical data using the lifelog analyzer to prevent and manage acute and chronic diseases through providing results of statistics on analysis.

RESULTS : The big data centers were built in four hospitals and seven companies for integrating lifelog and clinical data to develop the Lifelog Bigdata Platform. We integrated and loaded lifelog big data and clinical data for 3 years. In the first year, we uploaded 94 types of data on the platform with a total capacity of 221 GB.

CONCLUSION : The Lifelog Bigdata Platform is the first to combine lifelog and clinical data. The proposed standardization guidelines can be used for future platforms to achieve a virtuous cycle structure of lifelogging big data and an industrial ecosystem.

Lee Kyu Hee, Urtnasan Erdenebayar, Hwang Sangwon, Lee Hee Young, Lee Jung Hun, Koh Sang Baek, Youk Hyun

2022-Jan

Lifelog, big data, digital health, precision medicine

Radiology Radiology

Development and Validation of the Radiology Common Data Model (R-CDM) for the International Standardization of Medical Imaging Data.

In Yonsei medical journal

PURPOSE : Digital Imaging and Communications in Medicine (DICOM), a standard file format for medical imaging data, contains metadata describing each file. However, metadata are often incomplete, and there is no standardized format for recording metadata, leading to inefficiency during the metadata-based data retrieval process. Here, we propose a novel standardization method for DICOM metadata termed the Radiology Common Data Model (R-CDM).

MATERIALS AND METHODS : R-CDM was designed to be compatible with Health Level Seven International (HL7)/Fast Healthcare Interoperability Resources (FHIR) and linked with the Observational Medical Outcomes Partnership (OMOP)-CDM to achieve a seamless link between clinical data and medical imaging data. The terminology system was standardized using the RadLex playbook, a comprehensive lexicon of radiology. As a proof of concept, the R-CDM conversion process was conducted with 41.7 TB of data from the Ajou University Hospital. The R-CDM database visualizer was developed to visualize the main characteristics of the R-CDM database.

RESULTS : Information from 2801360 cases and 87203226 DICOM files was organized into two tables constituting the R-CDM. Information on imaging device and image resolution was recorded with more than 99.9% accuracy. Furthermore, OMOP-CDM and R-CDM were linked to efficiently extract specific types of images from specific patient cohorts.

CONCLUSION : R-CDM standardizes the structure and terminology for recording medical imaging data to eliminate incomplete and unstandardized information. Successful standardization was achieved by the extract, transform, and load process and image classifier. We hope that the R-CDM will contribute to deep learning research in the medical imaging field by enabling the securement of large-scale medical imaging data from multinational institutions.

Park ChulHyoung, You Seng Chan, Jeon Hokyun, Jeong Chang Won, Choi Jin Wook, Park Rae Woong

2022-Jan

Metadata, radiology information system, standardization

Dermatology Dermatology

scAnnotatR: framework to accurately classify cell types in single-cell RNA-sequencing data.

In BMC bioinformatics

BACKGROUND : Automatic cell type identification is essential to alleviate a key bottleneck in scRNA-seq data analysis. While most existing classification tools show good sensitivity and specificity, they often fail to adequately not-classify cells that are missing in the used reference. Additionally, many tools do not scale to the continuously increasing size of current scRNA-seq datasets. Therefore, additional tools are needed to solve these challenges.

RESULTS : scAnnotatR is a novel R package that provides a complete framework to classify cells in scRNA-seq datasets using pre-trained classifiers. It supports both Seurat and Bioconductor's SingleCellExperiment and is thereby compatible with the vast majority of R-based analysis workflows. scAnnotatR uses hierarchically organised SVMs to distinguish a specific cell type versus all others. It shows comparable or even superior accuracy, sensitivity and specificity compared to existing tools while being able to not-classify unknown cell types. Moreover, scAnnotatR is the only of the best performing tools able to process datasets containing more than 600,000 cells.

CONCLUSIONS : scAnnotatR is freely available on GitHub ( https://github.com/grisslab/scAnnotatR ) and through Bioconductor (from version 3.14). It is consistently among the best performing tools in terms of classification accuracy while scaling to the largest datasets.

Nguyen Vy, Griss Johannes

2022-Jan-17

Bioconductor, Cell classification, Machine learning, R, SVM, scAnnotatR, scRNAseq

Radiology Radiology

Using artificial intelligence to risk stratify COVID-19 patients based on chest X-ray findings.

In Intelligence-based medicine

Background : Deep learning-based radiological image analysis could facilitate use of chest x-rays as a triaging tool for COVID-19 diagnosis in resource-limited settings. This study sought to determine whether a modified commercially available deep learning algorithm (M-qXR) could risk stratify patients with suspected COVID-19 infections.

Methods : A dual track clinical validation study was designed to assess the clinical accuracy of M-qXR. The algorithm evaluated all Chest-X-rays (CXRs) performed during the study period for abnormal findings and assigned a COVID-19 risk score. Four independent radiologists served as radiological ground truth. The M-qXR algorithm output was compared against radiological ground truth and summary statistics for prediction accuracy were calculated. In addition, patients who underwent both PCR testing and CXR for suspected COVID-19 infection were included in a co-occurrence matrix to assess the sensitivity and specificity of the M-qXR algorithm.

Results : 625 CXRs were included in the clinical validation study. 98% of total interpretations made by M-qXR agreed with ground truth (p = 0.25). M-qXR correctly identified the presence or absence of pulmonary opacities in 94% of CXR interpretations. M-qXR's sensitivity, specificity, PPV, and NPV for detecting pulmonary opacities were 94%, 95%, 99%, and 88% respectively. M-qXR correctly identified the presence or absence of pulmonary consolidation in 88% of CXR interpretations (p = 0.48). M-qXR's sensitivity, specificity, PPV, and NPV for detecting pulmonary consolidation were 91%, 84%, 89%, and 86% respectively. Furthermore, 113 PCR-confirmed COVID-19 cases were used to create a co-occurrence matrix between M-qXR's COVID-19 risk score and COVID-19 PCR test results. The PPV and NPV of a medium to high COVID-19 risk score assigned by M-qXR yielding a positive COVID-19 PCR test result was estimated to be 89.7% and 80.4% respectively.

Conclusion : M-qXR was found to have comparable accuracy to radiological ground truth in detecting radiographic abnormalities on CXR suggestive of COVID-19.

Hipolito Canario Diego A, Fromke Eric, Patetta Matthew A, Eltilib Mohamed T, Reyes-Gonzalez Juan P, Rodriguez Georgina Cornelio, Fusco Cornejo Valeria A, Dunckner Seymour, Stewart Jessica K

2022-Jan-13

AI, artificial intelligence, Artificial intelligence, COVID-19, COVID-19, coronavirus disease of 2019, CXR, chest x-rays, Chest X-ray, Deep learning algorithm, M-qXR, modified qXR deep learning algorithm, Patient risk stratification

General General

Visualization and Analysis of Wearable Health Data From COVID-19 Patients

ArXiv Preprint

Effective visualizations were evaluated to reveal relevant health patterns from multi-sensor real-time wearable devices that recorded vital signs from patients admitted to hospital with COVID-19. Furthermore, specific challenges associated with wearable health data visualizations, such as fluctuating data quality resulting from compliance problems, time needed to charge the device and technical problems are described. As a primary use case, we examined the detection and communication of relevant health patterns visible in the vital signs acquired by the technology. Customized heat maps and bar charts were used to specifically highlight medically relevant patterns in vital signs. A survey of two medical doctors, one clinical project manager and seven health data science researchers was conducted to evaluate the visualization methods. From a dataset of 84 hospitalized COVID-19 patients, we extracted one typical COVID-19 patient history and based on the visualizations showcased the health history of two noteworthy patients. The visualizations were shown to be effective, simple and intuitive in deducing the health status of patients. For clinical staff who are time-constrained and responsible for numerous patients, such visualization methods can be an effective tool to enable continuous acquisition and monitoring of patients' health statuses even remotely.

Susanne K. Suter, Georg R. Spinner, Bianca Hoelz, Sofia Rey, Sujeanthraa Thanabalasingam, Jens Eckstein, Sven Hirsch

2022-01-19

General General

Visualization and Analysis of Wearable Health Data From COVID-19 Patients

ArXiv Preprint

Effective visualizations were evaluated to reveal relevant health patterns from multi-sensor real-time wearable devices that recorded vital signs from patients admitted to hospital with COVID-19. Furthermore, specific challenges associated with wearable health data visualizations, such as fluctuating data quality resulting from compliance problems, time needed to charge the device and technical problems are described. As a primary use case, we examined the detection and communication of relevant health patterns visible in the vital signs acquired by the technology. Customized heat maps and bar charts were used to specifically highlight medically relevant patterns in vital signs. A survey of two medical doctors, one clinical project manager and seven health data science researchers was conducted to evaluate the visualization methods. From a dataset of 84 hospitalized COVID-19 patients, we extracted one typical COVID-19 patient history and based on the visualizations showcased the health history of two noteworthy patients. The visualizations were shown to be effective, simple and intuitive in deducing the health status of patients. For clinical staff who are time-constrained and responsible for numerous patients, such visualization methods can be an effective tool to enable continuous acquisition and monitoring of patients' health statuses even remotely.

Susanne K. Suter, Georg R. Spinner, Bianca Hoelz, Sofia Rey, Sujeanthraa Thanabalasingam, Jens Eckstein, Sven Hirsch

2022-01-19

General General

Detection and Weak Segmentation of Masses in Gray-Scale Breast Mammogram Images Using Deep Learning.

In Yonsei medical journal

PURPOSE : In this paper, we propose deep-learning methodology with which to enhance the mass differentiation performance of convolutional neural network (CNN)-based architecture.

MATERIALS AND METHODS : We differentiated breast mass lesions from gray-scale X-ray mammography images based on regions of interest (ROIs). Our dataset comprised breast mammogram images for 150 cases of malignant masses from which we extracted the mass ROI, and we composed a CNN-based deep learning model trained on this dataset to identify ROI mass lesions. The test dataset was created by shifting some of the training data images. Thus, although both datasets were different, they retained a deep structural similarity. We then applied our trained deep-learning model to detect masses on 8-bit mammogram images containing malignant masses. The input images were preprocessed by applying a scaling parameter of intensity before being used to train the CNN model for mass differentiation.

RESULTS : The highest area under the receiver operating characteristic curve was 0.897 (Î 20).

CONCLUSION : Our results indicated that the proposed patch-wise detection method can be utilized as a mass detection and segmentation tool.

Kim Young Jae, Kim Kwang Gi

2022-Jan

Breast mammogram, convolutional neural network, data normalization, deep learning, detection lesion of mass

General General

Real-Time Exercise Feedback through a Convolutional Neural Network: A Machine Learning-Based Motion-Detecting Mobile Exercise Coaching Application.

In Yonsei medical journal

PURPOSE : Mobile applications are widely used in the healthcare market. This study aimed to determine whether exercise using a machine learning-based motion-detecting mobile exercise coaching application (MDMECA) is superior to video streaming-based exercise for improving quality of life and decreasing lower back pain.

MATERIALS AND METHODS : The same 14-day daily workout program consisting of five exercises was performed by 104 participants using the MDMECA and another 72 participants using video streaming. The Medical Outcomes Study Short Form 36-Item Health Survey (SF-36) and lower back pain scores were assess as pre- and post-workout measurements. Scores for the treatment-satisfaction subscale of the visual analog scale (TS-VAS), intention to use a disease-oriented exercise program, intention to recommend the program to others, and available expenses for a disease-oriented exercise program were determined after the workout.

RESULTS : The MDMECA group showed a higher increase in SF-36 score (MDMECA, 9.10; control, 1.09; p<0.01) and a greater reduction in lower back pain score (MDMECA, -0.96; control, -0.26; p<0.01). Scores for TS-VAS, intention to use a disease-oriented exercise program, and intention to recommend the program to others were all higher (p<0.01) in the MDMECA group. However, the available expenses for a disease-oriented program were not significantly different between the two groups.

CONCLUSION : The MDMECA is more effective than video streaming-based exercise in increasing exercise adherence, improving QoL, and reducing lower back pain. MDMECAs could be promising tools of use to achieve better medical outcomes and higher treatment satisfaction.

Park Jinyoung, Chung Seok Young, Park Jung Hyun

2022-Jan

Coaching, exercise, machine learning, mobile application, motion, neural network

Internal Medicine Internal Medicine

Digital Healthcare for Airway Diseases from Personal Environmental Exposure.

In Yonsei medical journal

Digital technologies have emerged in various dimensions of human life, ranging from education to professional services to well-being. In particular, health products and services have expanded by the use and development of artificial intelligence, mobile health applications, and wearable electronic devices. Such advancements have enabled accurate and updated tracking and modeling of health conditions. For instance, digital health technologies are capable of measuring environmental pollution and predicting its adverse health effects. Several health conditions, including chronic airway diseases such as asthma and chronic obstructive pulmonary disease, can be exacerbated by pollution. These diseases impose substantial health burdens with high morbidity and mortality. Recently, efforts have been made to develop digital technologies to alleviate such conditions. Moreover, the COVID-19 pandemic has facilitated the application of telemedicine and telemonitoring for patients with chronic airway diseases. This article reviews current trends and studies in digital technology utilization for investigating and managing environmental exposure and chronic airway diseases. First, we discussed the recent progression of digital technologies in general environmental healthcare. Then, we summarized the capacity of digital technologies in predicting exacerbation and self-management of airway diseases. Concluding these reviews, we provided suggestions to improve digital health technologies' abilities to reduce the adverse effects of environmental exposure in chronic airway diseases, based on personal exposure-response modeling.

Park Youngmok, Lee Chanho, Jung Ji Ye

2022-Jan

Asthma, chronic obstructive pulmonary disease, digital technology, environment, wearable electronic devices

General General

Identification of Sclareol As a Natural Neuroprotective Cav 1.3-Antagonist Using Synthetic Parkinson-Mimetic Gene Circuits and Computer-Aided Drug Discovery.

In Advanced science (Weinheim, Baden-Wurttemberg, Germany)

Parkinson's disease (PD) results from selective loss of substantia nigra dopaminergic (SNc DA) neurons, and is primarily caused by excessive activity-related Ca2+ oscillations. Although L-type voltage-gated calcium channel blockers (CCBs) selectively inhibiting Cav 1.3 are considered promising candidates for PD treatment, drug discovery is hampered by the lack of high-throughput screening technologies permitting isoform-specific assessment of Cav-antagonistic activities. Here, a synthetic-biology-inspired drug-discovery platform enables identification of PD-relevant drug candidates. By deflecting Cav-dependent activation of nuclear factor of activated T-cells (NFAT)-signaling to repression of reporter gene translation, they engineered a cell-based assay where reporter gene expression is activated by putative CCBs. By using this platform in combination with in silico virtual screening and a trained deep-learning neural network, sclareol is identified from a essential oils library as a structurally distinctive compound that can be used for PD pharmacotherapy. In vitro studies, biochemical assays and whole-cell patch-clamp recordings confirmed that sclareol inhibits Cav 1.3 more strongly than Cav 1.2 and decreases firing responses of SNc DA neurons. In a mouse model of PD, sclareol treatment reduced DA neuronal loss and protected striatal network dynamics as well as motor performance. Thus, sclareol appears to be a promising drug candidate for neuroprotection in PD patients.

Wang Hui, Xie Mingqi, Rizzi Giorgio, Li Xin, Tan Kelly, Fussenegger Martin

2022-Jan-18

“Parkinsons disease”, drug discovery, neuroprotection, sclareol, synthetic biology, voltage-gated calcium channels

General General

Hybrid Cathode Interlayer Enables 17.4% Efficiency Binary Organic Solar Cells.

In Advanced science (Weinheim, Baden-Wurttemberg, Germany)

With the emergence of fused ring electron acceptors, the power conversion efficiency of organic solar cells reached 19%. In comparison with the electron donor and acceptor materials progress, the development of cathode interlayers lags. As a result, charge extraction barriers, interfacial trap states, and significant transport resistance may be induced due to the unfavorable cathode interlayer, limiting the device performances. Herein, a hybrid cathode interlayer composed of PNDIT-F3N and PDIN is adopted to investigate the interaction between the photoexcited acceptor and cathode interlayer. The state of art acceptor Y6 is chosen and blended with PM6 as the active layer. The device with hybrid interlayer, PNDIT-F3N:PDIN (0.6:0.4, in wt%), attains a power conversion efficiency of 17.4%, outperforming devices with other cathode interlayer such as NDI-M, PDINO, and Phen-DPO. It is resulted from enhanced exciton dissociation, reduced trap-assisted recombination, and smaller transfer resistance. Therefore, the hybrid interlayer strategy is demonstrated as an efficient approach to improve device performance, shedding light on the selection and engineering of cathode interlayers for pairing the increasing number of fused ring electron acceptors.

Song Hang, Hu Dingqin, Lv Jie, Lu Shirong, Haiyan Chen, Kan Zhipeng

2022-Jan-18

cathode interlayer, charge transfer, hybrid interface, organic solar cells

General General

Recent Advances in Classification of Brain Tumor from MR Images - State of the Art Review from 2017 to 2021.

In Current medical imaging

BACKGROUND : The task of identifying a tumor in the brain is a complex problem that requires sophisticated skills and inference mechanisms to accurately locate the tumor region. The complex nature of the brain tissue makes the problem of locating, segmenting, and ultimately classifying Magnetic Resonance (MR) images a complex problem. The aim of this review paper is to consolidate the details of the most relevant and recent approaches proposed in this domain for the binary and multi-class classification of brain tumors using brain MR images.

OBJECTIVE : In this review paper, a detailed summary of the latest techniques used for brain MR image feature extraction and classification is presented. A lot of research papers have been published recently with various techniques proposed for identifying an efficient method for the correct recognition and diagnosis of brain MR images. The review paper allows researchers in the field to familiarize themselves with the latest developments and be able to propose novel techniques that have not yet been explored in this research domain. In addition, the review paper will facilitate researchers, who are new to machine learning algorithms for brain tumor recognition, to understand the basics of the field and pave the way for them to be able to contribute to this vital field of medical research.

RESULTS : In this paper, the review is performed for all recently proposed methods for both feature extraction and classification. It also identifies the combination of feature extraction methods and classification methods that when combined would be the most efficient technique for the recognition and diagnosis of brain tumor from MR images. In addition, the paper presents the performance metrics particularly the recognition accuracy, of selected research published between 2017- 2021.

Latif Ghazanfar, Al Falanezi Pmu Edu saAnezi Faisal Yousif, Iskandar D N F Awang, Bashar Abul, Alghazo Jaafar

2022-Jan-17

Pomegranate peel, disease preventive, eco-friendly, feasible, phytochemicals, vital bioactive compound

Ophthalmology Ophthalmology

Normal-tension glaucoma: Current concepts and approaches - A review.

In Clinical & experimental ophthalmology

Normal tension glaucoma (NTG) has remained a challenging disease. We review, from an epidemiological perspective, why we should redefine normality, act earlier at lower pre-treatment intraocular pressure (IOP) level, and the role of ocular perfusion pressures, noting that perfusion is affected by defective vascular bed autoregulation and endothelial dysfunction. The correlation of silent cerebral infarcts (SCI) and NTG may indicate that NTG belongs to a wider spectrum of small vessel diseases (SVD), with its main pathology being also on vascular endothelium. Epidemiological studies also suggested that vascular geometry, such as fractal dimension, may affect perfusion efficiency, occurrence of SCI, SVD and glaucoma. Artificial intelligence with deep learning, may help predicting NTG progression from vascular geometry. Finally we review latest evidence on the role of minimally-invasive glaucoma surgery, lasers, and newer drugs. We conclude that IOP is not the only modifiable risk factors as, many vascular risk factors are readily modifiable.

Leung Dexter Y L, Tham Clement C

2022-Jan-18

Artificial intelligence, Normal tension glaucoma, Silent cerebral infarcts, Small vessel disease, Vascular geometry

General General

Full-flexible Intelligent Thermal Touch Panel Based on Intrinsically Plastic Ag2 S Semiconductor.

In Advanced materials (Deerfield Beach, Fla.)

Wearable touch panel, a typical flexible electronic device, can recognize and feedback the information of finger touch and movement. Excellent wearable touch panels are required to accurately and quickly monitor the signals of finger movement as well as the capacity of bearing various deformation. High-performance thermistor materials are one of the key functional components, but to date, a long-standing bottleneck is that inorganic semiconductors are typically brittle while the electrical properties of organic semiconductors are quite low. Herein, we report a high-performance flexible temperature sensor by using plastic Ag2 S with ultrahigh temperature coefficient of resistance of -4.7%/K and resolution of 0.05 K, and rapid response/recovery time of 0.11/0.11 s. Moreover, the temperature sensor shows excellent durability without performance damage or lose during force stimuli tests. In addition, full-flexible intelligent touch panel composed of 16×10 Ag2 S film-based temperature sensor array, as well as flexible printed circuit board and deep learning algorithm is designed for perceive finger touch signals in real-time and intelligent feedback Chinese characters and letters on App. These results strongly show that high-performance flexible inorganic semiconductors can be widely used in flexible electronics. This article is protected by copyright. All rights reserved.

Zhao Xue-Feng, Yang Shi-Qi, Wen Xiao-Hong, Huang Qi-Wei, Qiu Peng-Fei, Wei Tian-Ran, Zhang Hao, Wang Jia-Cheng, Zhang David Wei, Shi Xun, Lu Hong-Liang

2022-Jan-17

Ag2S film, TCR, deep learning algorithm, flexible inorganic semiconductor, full-flexible temperature sensor array

General General

Classifying Idiopathic Rapid Eye Movement Sleep Behavior Disorder, Controls, and Mild Parkinson's Disease Using Gait Parameters.

In Movement disorders : official journal of the Movement Disorder Society

BACKGROUND : Subtle gait changes associated with idiopathic rapid eye movement sleep behavior disorder (iRBD) could allow early detection of subjects with future synucleinopathies.

OBJECTIVE : The aim of this study was to create a multiclass model, using statistical learning from probability distribution of gait parameters, to distinguish between patients with iRBD, healthy control subjects (HCs), and patients with Parkinson's disease (PD).

METHODS : Gait parameters were collected in 21 participants with iRBD, 21 with PD, and 21 HCs, matched for age, sex, and education level. Lasso sparse linear regression explored gait features able to classify the three groups.

RESULTS : The final model classified iRBD from HCs and from patients with PD equally well, with 95% accuracy, 100% sensitivity, and 90% specificity.

CONCLUSIONS : Gait parameters and a pretrained statistical model can robustly distinguish participants with iRBD from HCs and patients with PD. This could be used to screen subjects with future synucleinopathies in the general population and to identify a conversion threshold to PD. © 2022 International Parkinson and Movement Disorder Society.

Cochen De Cock Valérie, Dotov Dobromir, Lacombe Sandy, Picot Marie Christine, Galtier Florence, Driss Valérie, Giovanni Castelnovo, Geny Christian, Abril Beatriz, Damm Loic, Janaqi Stefan

2022-Jan-17

“Parkinsons disease”, conversion, gait parameters, iRBD, idiopathic REM sleep behavior disorder, machine learning classification

General General

Annual Research Review: Translational machine learning for child and adolescent psychiatry.

In Journal of child psychology and psychiatry, and allied disciplines

Children and adolescents could benefit from the use of predictive tools that facilitate personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been deployed using traditional statistical methods, potentially due to the limitations of the paradigm and the need to leverage large amounts of digital data. This review will suggest that a machine learning approach could address these challenges and is designed to introduce new readers to the background, methods, and results in the field. A rationale is first introduced followed by an outline of fundamental elements of machine learning approaches. To provide an overview of the use of the techniques in child and adolescent literature, a scoping review of broad trends is then presented. Selected studies are also highlighted in order to draw attention to research areas that are closest to translation and studies that exhibit a high degree of experimental innovation. Limitations to the research, and machine learning approaches generally, are outlined in the penultimate section highlighting issues related to sample sizes, validation, clinical utility, and ethical challenges. Finally, future directions are discussed that could enhance the possibility of clinical implementation and address specific questions relevant to the child and adolescent psychiatry. The review gives a broad overview of the machine learning paradigm in order to highlight the benefits of a shift in perspective towards practically oriented statistical solutions that aim to improve clinical care of children and adolescents.

Dwyer Dominic, Koutsouleris Nikolaos

2022-Jan-17

ADHD, Machine learning, artificial intelligence, autism spectrum disorders, depression, psychosis

General General

Automatic Measurement of Postural Abnormalities With a Pose Estimation Algorithm in Parkinson's Disease.

In Journal of movement disorders

Objective : This study aims to develop an automated and objective tool to evaluate postural abnormalities in Parkinson's disease (PD) patients.

Methods : We applied a deep learning-based pose-estimation algorithm to lateral photos of prospectively enrolled PD patients (n = 28). We automatically measured the anterior flexion angle (AFA) and dropped head angle (DHA), which were validated with conventional manual labeling methods.

Results : The automatically measured DHA and AFA were in excellent agreement with manual labeling methods (intraclass correlation coefficient > 0.95) with mean bias equal to or less than 3 degrees.

Conclusion : The deep learning-based pose-estimation algorithm objectively measured postural abnormalities in PD patients.

Shin Jung Hwan, Woo Kyung Ah, Lee Chan Young, Jeon Seung Ho, Kim Han-Joon, Jeon Beomseok

2022-Jan-19

Camptocormia, Parkinson’s disease, Pose estimation

General General

Fake or real news about COVID-19? Pretrained transformer model to detect potential misleading news.

In The European physical journal. Special topics

The World Health Organization declared the novel coronavirus disease 2019 a pandemic on March 11, 2020. Along with the coronavirus pandemic, a new crisis has emerged, characterized by widespread fear and panic caused by a lack of information or, in some cases, outright fake messages. In these circumstances, Twitter is one of the most eminent and trusted social media platforms. Fake tweets, on the other hand, are challenging to detect and differentiate. The primary goal of this paper is to educate society about the importance of accurate information and prevent the spread of fake information. This paper has investigated COVID-19 fake data from various social media platforms such as Twitter, Facebook, and Instagram. The objective of this paper is to categorize given tweets as either fake or real news. The authors have tested various deep learning models on the COVID-19 fake dataset. Finally, the CT-BERT and RoBERTa deep learning models outperformed other deep learning models like BERT, BERTweet, AlBERT, and DistlBERT. The proposed ensemble deep learning architecture outperformed CT-BERT and RoBERTa on the COVID-19 fake news dataset using the multiplicative fusion technique. The proposed model's performance in this technique was determined by the multiplicative product of the final predictive values of CT-BERT and RoBERTa. This technique overcomes the disadvantage of these CT-BERT and RoBERTa models' incorrect predictive nature. The proposed architecture outperforms both well-known ML and DL models, with 98.88% accuracy and a 98.93% F1-score.

Malla SreeJagadeesh, Alphonse P J A

2022-Jan-13

General General

Real-time Recognition of Yoga Poses using computer Vision for Smart Health Care

ArXiv Preprint

Nowadays, yoga has become a part of life for many people. Exercises and sports technological assistance is implemented in yoga pose identification. In this work, a self-assistance based yoga posture identification technique is developed, which helps users to perform Yoga with the correction feature in Real-time. The work also presents Yoga-hand mudra (hand gestures) identification. The YOGI dataset has been developed which include 10 Yoga postures with around 400-900 images of each pose and also contain 5 mudras for identification of mudras postures. It contains around 500 images of each mudra. The feature has been extracted by making a skeleton on the body for yoga poses and hand for mudra poses. Two different algorithms have been used for creating a skeleton one for yoga poses and the second for hand mudras. Angles of the joints have been extracted as a features for different machine learning and deep learning models. among all the models XGBoost with RandomSearch CV is most accurate and gives 99.2\% accuracy. The complete design framework is described in the present paper.

Abhishek Sharma, Yash Shah, Yash Agrawal, Prateek Jain

2022-01-19

General General

Real-time Recognition of Yoga Poses using computer Vision for Smart Health Care

ArXiv Preprint

Nowadays, yoga has become a part of life for many people. Exercises and sports technological assistance is implemented in yoga pose identification. In this work, a self-assistance based yoga posture identification technique is developed, which helps users to perform Yoga with the correction feature in Real-time. The work also presents Yoga-hand mudra (hand gestures) identification. The YOGI dataset has been developed which include 10 Yoga postures with around 400-900 images of each pose and also contain 5 mudras for identification of mudras postures. It contains around 500 images of each mudra. The feature has been extracted by making a skeleton on the body for yoga poses and hand for mudra poses. Two different algorithms have been used for creating a skeleton one for yoga poses and the second for hand mudras. Angles of the joints have been extracted as a features for different machine learning and deep learning models. among all the models XGBoost with RandomSearch CV is most accurate and gives 99.2\% accuracy. The complete design framework is described in the present paper.

Abhishek Sharma, Yash Shah, Yash Agrawal, Prateek Jain

2022-01-19

Pathology Pathology

Characterizing breast masses using an integrative framework of machine learning and CEUS-based radiomics.

In Journal of ultrasound

AIMS : We evaluated the performance of contrast-enhanced ultrasound (CEUS) based on radiomics analysis to distinguish benign from malignant breast masses.

METHODS : 131 women with suspicious breast masses (BI-RADS 4a, 4b, or 4c) who underwent CEUS examinations (using intravenous injection of perflutren lipid microsphere or sulfur hexafluoride lipid-type A microspheres) prior to ultrasound-guided biopsies were retrospectively identified. Post biopsy pathology showed 115 benign and 16 malignant masses. From the cine clip of the CEUS exams obtained using the built-in GE scanner software, breast masses and adjacent normal tissue were then manually segmented using the ImageJ software. One frame representing each of the four phases: precontrast, early, peak, and delay enhancement were selected post segmentation from each CEUS clip. 112 radiomic metrics were extracted from each segmented tissue normalized breast mass using custom Matlab® code. Linear and nonlinear machine learning (ML) methods were used to build the prediction model to distinguish benign from malignant masses. tenfold cross-validation evaluated model performance. Area under the curve (AUC) was used to quantify prediction accuracy.

RESULTS : Univariate analysis found 35 (38.5%) radiomic variables with p < 0.05 in differentiating between benign from malignant masses. No feature selection was performed. Predictive models based on AdaBoost reported an AUC = 0.72 95% CI (0.56, 0.89), followed by Random Forest with an AUC = 0.71 95% CI (0.56, 0.87).

CONCLUSIONS : CEUS based texture metrics can distinguish between benign and malignant breast masses, which can, in turn, lead to reduced unnecessary breast biopsies.

Varghese Bino A, Lee Sandy, Cen Steven, Talebi Amir, Mohd Passant, Stahl Daniel, Perkins Melissa, Desai Bhushan, Duddalwar Vinay A, Larsen Linda H

2022-Jan-17

Breast masses, CEUS, Machine learning, Malignancy, Radiomics

General General

Forensic bone age estimation of adolescent pelvis X-rays based on two-stage convolutional neural network.

In International journal of legal medicine

In the forensic estimation of bone age, the pelvis is important for identifying the bone age of teenagers. However, studies on this topic remain insufficient as a result of lower accuracy due to the overlapping of pelvic organs in X-ray images. Segmentation networks have been used to automate the location of key pelvic areas and minimize restrictions like doubling images of pelvic organs to increase the accuracy of estimation. This study conducted a retrospective analysis of 2164 pelvis X-ray images of Chinese Han teenagers ranging from 11 to 21 years old. Key areas of the pelvis were detected with a U-Net segmentation network, and the findings were combined with the original X-ray image for regional augmentation. Bone age estimation was conducted with the enhanced and not enhanced pelvis X-ray images by separately using three convolutional neural networks (CNNs). The root mean square errors (RMSE) of the Inception-V3, Inception-ResNet-V2, and VGG19 convolutional neural networks were 0.93 years, 1.12 years, and 1.14 years, respectively, and the mean absolute errors (MAE) of these networks were 0.67 years, 0.77 years, and 0.88 years, respectively. For comparison, a network without segmentation was employed to conduct the estimation, and it was found that the RMSE of the three CNNs above became 1.22 years, 1.25 years, and 1.63 years, respectively, and the MAE became 0.93 years, 0.96 years, and 1.23 years. Bland-Altman plots and attention maps were also generated to provide a visual comparison. The proposed segmentation network can be used to reduce the influence of restrictions like image overlapping of organs and can thus increase the accuracy of pelvic bone age estimation.

Peng Li-Qin, Guo Yu-Cheng, Wan Lei, Liu Tai-Ang, Wang Peng, Zhao Hu, Wang Ya-Hui

2022-Jan-18

Adolescent, Bone age estimation, Convolutional neural networks, Deep learning, Image recognition, Pelvis

General General

Molecular substructure tree generative model for de novo drug design.

In Briefings in bioinformatics

Deep learning shortens the cycle of the drug discovery for its success in extracting features of molecules and proteins. Generating new molecules with deep learning methods could enlarge the molecule space and obtain molecules with specific properties. However, it is also a challenging task considering that the connections between atoms are constrained by chemical rules. Aiming at generating and optimizing new valid molecules, this article proposed Molecular Substructure Tree Generative Model, in which the molecule is generated by adding substructure gradually. The proposed model is based on the Variational Auto-Encoder architecture, which uses the encoder to map molecules to the latent vector space, and then builds an autoregressive generative model as a decoder to generate new molecules from Gaussian distribution. At the same time, for the molecular optimization task, a molecular optimization model based on CycleGAN was constructed. Experiments showed that the model could generate valid and novel molecules, and the optimized model effectively improves the molecular properties.

Wang Shuang, Song Tao, Zhang Shugang, Jiang Mingjian, Wei Zhiqiang, Li Zhen

2022-Jan-18

VAE, drug design, generative model, molecule generation, molecule optimization

General General

ADENet: a novel network-based inference method for prediction of drug adverse events.

In Briefings in bioinformatics

Identification of adverse drug events (ADEs) is crucial to reduce human health risks and improve drug safety assessment. With an increasing number of biological and medical data, computational methods such as network-based methods were proposed for ADE prediction with high efficiency and low cost. However, previous network-based methods rely on the topological information of known drug-ADE networks, and hence cannot make predictions for novel compounds without any known ADE. In this study, we introduced chemical substructures to bridge the gap between the drug-ADE network and novel compounds, and developed a novel network-based method named ADENet, which can predict potential ADEs for not only drugs within the drug-ADE network, but also novel compounds outside the network. To show the performance of ADENet, we collected drug-ADE associations from a comprehensive database named MetaADEDB and constructed a series of network-based prediction models. These models obtained high area under the receiver operating characteristic curve values ranging from 0.871 to 0.947 in 10-fold cross-validation. The best model further showed high performance in external validation, which outperformed a previous network-based and a recent deep learning-based method. Using several approved drugs as case studies, we found that 32-54% of the predicted ADEs can be validated by the literature, indicating the practical value of ADENet. Moreover, ADENet is freely available at our web server named NetInfer (http://lmmd.ecust.edu.cn/netinfer). In summary, our method would provide a promising tool for ADE prediction and drug safety assessment in drug discovery and development.

Yu Zhuohang, Wu Zengrui, Li Weihua, Liu Guixia, Tang Yun

2022-Jan-18

adverse drug event, chemical substructure, computational prediction, network-based inference

General General

A weighted bilinear neural collaborative filtering approach for drug repositioning.

In Briefings in bioinformatics

Drug repositioning is an efficient and promising strategy for traditional drug discovery and development. Many research efforts are focused on utilizing deep-learning approaches based on a heterogeneous network for modeling complex drug-disease associations. Similar to traditional latent factor models, which directly factorize drug-disease associations, they assume the neighbors are independent of each other in the network and thus tend to be ineffective to capture localized information. In this study, we propose a novel neighborhood and neighborhood interaction-based neural collaborative filtering approach (called DRWBNCF) to infer novel potential drugs for diseases. Specifically, we first construct three networks, including the known drug-disease association network, the drug-drug similarity and disease-disease similarity networks (using the nearest neighbors). To take the advantage of localized information in the three networks, we then design an integration component by proposing a new weighted bilinear graph convolution operation to integrate the information of the known drug-disease association, the drug's and disease's neighborhood and neighborhood interactions into a unified representation. Lastly, we introduce a prediction component, which utilizes the multi-layer perceptron optimized by the α-balanced focal loss function and graph regularization to model the complex drug-disease associations. Benchmarking comparisons on three datasets verified the effectiveness of DRWBNCF for drug repositioning. Importantly, the unknown drug-disease associations predicted by DRWBNCF were validated against clinical trials and three authoritative databases and we listed several new DRWBNCF-predicted potential drugs for breast cancer (e.g. valrubicin and teniposide) and small cell lung cancer (e.g. valrubicin and cytarabine).

Meng Yajie, Lu Changcheng, Jin Min, Xu Junlin, Zeng Xiangxiang, Yang Jialiang

2022-Jan-18

disease, drug, drug repositioning, drug–disease association prediction, neighborhood interactions

Radiology Radiology

Using artificial intelligence to risk stratify COVID-19 patients based on chest X-ray findings.

In Intelligence-based medicine

Background : Deep learning-based radiological image analysis could facilitate use of chest x-rays as a triaging tool for COVID-19 diagnosis in resource-limited settings. This study sought to determine whether a modified commercially available deep learning algorithm (M-qXR) could risk stratify patients with suspected COVID-19 infections.

Methods : A dual track clinical validation study was designed to assess the clinical accuracy of M-qXR. The algorithm evaluated all Chest-X-rays (CXRs) performed during the study period for abnormal findings and assigned a COVID-19 risk score. Four independent radiologists served as radiological ground truth. The M-qXR algorithm output was compared against radiological ground truth and summary statistics for prediction accuracy were calculated. In addition, patients who underwent both PCR testing and CXR for suspected COVID-19 infection were included in a co-occurrence matrix to assess the sensitivity and specificity of the M-qXR algorithm.

Results : 625 CXRs were included in the clinical validation study. 98% of total interpretations made by M-qXR agreed with ground truth (p = 0.25). M-qXR correctly identified the presence or absence of pulmonary opacities in 94% of CXR interpretations. M-qXR's sensitivity, specificity, PPV, and NPV for detecting pulmonary opacities were 94%, 95%, 99%, and 88% respectively. M-qXR correctly identified the presence or absence of pulmonary consolidation in 88% of CXR interpretations (p = 0.48). M-qXR's sensitivity, specificity, PPV, and NPV for detecting pulmonary consolidation were 91%, 84%, 89%, and 86% respectively. Furthermore, 113 PCR-confirmed COVID-19 cases were used to create a co-occurrence matrix between M-qXR's COVID-19 risk score and COVID-19 PCR test results. The PPV and NPV of a medium to high COVID-19 risk score assigned by M-qXR yielding a positive COVID-19 PCR test result was estimated to be 89.7% and 80.4% respectively.

Conclusion : M-qXR was found to have comparable accuracy to radiological ground truth in detecting radiographic abnormalities on CXR suggestive of COVID-19.

Hipolito Canario Diego A, Fromke Eric, Patetta Matthew A, Eltilib Mohamed T, Reyes-Gonzalez Juan P, Rodriguez Georgina Cornelio, Fusco Cornejo Valeria A, Dunckner Seymour, Stewart Jessica K

2022-Jan-13

AI, artificial intelligence, Artificial intelligence, COVID-19, COVID-19, coronavirus disease of 2019, CXR, chest x-rays, Chest X-ray, Deep learning algorithm, M-qXR, modified qXR deep learning algorithm, Patient risk stratification

General General

Fake or real news about COVID-19? Pretrained transformer model to detect potential misleading news.

In The European physical journal. Special topics

The World Health Organization declared the novel coronavirus disease 2019 a pandemic on March 11, 2020. Along with the coronavirus pandemic, a new crisis has emerged, characterized by widespread fear and panic caused by a lack of information or, in some cases, outright fake messages. In these circumstances, Twitter is one of the most eminent and trusted social media platforms. Fake tweets, on the other hand, are challenging to detect and differentiate. The primary goal of this paper is to educate society about the importance of accurate information and prevent the spread of fake information. This paper has investigated COVID-19 fake data from various social media platforms such as Twitter, Facebook, and Instagram. The objective of this paper is to categorize given tweets as either fake or real news. The authors have tested various deep learning models on the COVID-19 fake dataset. Finally, the CT-BERT and RoBERTa deep learning models outperformed other deep learning models like BERT, BERTweet, AlBERT, and DistlBERT. The proposed ensemble deep learning architecture outperformed CT-BERT and RoBERTa on the COVID-19 fake news dataset using the multiplicative fusion technique. The proposed model's performance in this technique was determined by the multiplicative product of the final predictive values of CT-BERT and RoBERTa. This technique overcomes the disadvantage of these CT-BERT and RoBERTa models' incorrect predictive nature. The proposed architecture outperforms both well-known ML and DL models, with 98.88% accuracy and a 98.93% F1-score.

Malla SreeJagadeesh, Alphonse P J A

2022-Jan-13

General General

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda.

In Journal of ambient intelligence and humanized computing

Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.

Kumar Yogesh, Koul Apeksha, Singla Ruchi, Ijaz Muhammad Fazal

2022-Jan-13

Alzheimer, Artificial intelligence, Cancer disease, Chronic disease, Heart disease, Tuberculosis

General General

A survey on extremism analysis using natural language processing: definitions, literature review, trends and challenges.

In Journal of ambient intelligence and humanized computing

Extremism has grown as a global problem for society in recent years, especially after the apparition of movements such as jihadism. This and other extremist groups have taken advantage of different approaches, such as the use of Social Media, to spread their ideology, promote their acts and recruit followers. The extremist discourse, therefore, is reflected on the language used by these groups. Natural language processing (NLP) provides a way of detecting this type of content, and several authors make use of it to describe and discriminate the discourse held by these groups, with the final objective of detecting and preventing its spread. Following this approach, this survey aims to review the contributions of NLP to the field of extremism research, providing the reader with a comprehensive picture of the state of the art of this research area. The content includes a first conceptualization of the term extremism, the elements that compose an extremist discourse and the differences with other terms. After that, a review description and comparison of the frequently used NLP techniques is presented, including how they were applied, the insights they provided, the most frequently used NLP software tools, descriptive and classification applications, and the availability of datasets and data sources for research. Finally, research questions are approached and answered with highlights from the review, while future trends, challenges and directions derived from these highlights are suggested towards stimulating further research in this exciting research area.

Torregrosa Javier, Bello-Orgaz Gema, Martínez-Cámara Eugenio, Ser Javier Del, Camacho David

2022-Jan-12

Deep learning, Extremism, Machine learning, Natural language processing, Radicalization

General General

Unlocking digital archives: cross-disciplinary perspectives on AI and born-digital data.

In AI & society

Co-authored by a Computer Scientist and a Digital Humanist, this article examines the challenges faced by cultural heritage institutions in the digital age, which have led to the closure of the vast majority of born-digital archival collections. It focuses particularly on cultural organizations such as libraries, museums and archives, used by historians, literary scholars and other Humanities scholars. Most born-digital records held by cultural organizations are inaccessible due to privacy, copyright, commercial and technical issues. Even when born-digital data are publicly available (as in the case of web archives), users often need to physically travel to repositories such as the British Library or the Bibliothèque Nationale de France to consult web pages. Provided with enough sample data from which to learn and train their models, AI, and more specifically machine learning algorithms, offer the opportunity to improve and ease the access to digital archives by learning to perform complex human tasks. These vary from providing intelligent support for searching the archives to automate tedious and time-consuming tasks.  In this article, we focus on sensitivity review as a practical solution to unlock digital archives that would allow archival institutions to make non-sensitive information available. This promise to make archives more accessible does not come free of warnings for potential pitfalls and risks: inherent errors, "black box" approaches that make the algorithm inscrutable, and risks related to bias, fake, or partial information. Our central argument is that AI can deliver its promise to make digital archival collections more accessible, but it also creates new challenges - particularly in terms of ethics. In the conclusion, we insist on the importance of fairness, accountability and transparency in the process of making digital archives more accessible.

Jaillant Lise, Caputo Annalina

2022-Jan-12

Artificial Intelligence, Born-digital archives, Copyright, Ethics, Privacy, Sensitivity Review

oncology Oncology

Histopathologic and Molecular Biomarkers of PD-1/PD-L1 Inhibitor Treatment Response Among Patients with Microsatellite Instability‒High Colon Cancer.

In Cancer research and treatment

Purpose : Recent clinical trials have reported response rates < 50% among patients treated with programmed death-1 (PD-1)/programmed death-ligand 1 (PD-L1) inhibitors for microsatellite instability‒high (MSI-H) colorectal cancer (CRC), and factors predicting treatment response have not been fully identified. This study aimed to identify potential biomarkers of PD-1/PD-L1 inhibitor treatment response among patients with MSI-H CRC.

Materials and Methods : MSI-H CRC patients enrolled in three clinical trials of PD-1/PD-L1 blockade at Asan Medical Center (Seoul, Republic of Korea) were screened and classified into two groups according to treatment response. Their histopathologic features and expression of 730 immune-related genes from the NanoString platform were evaluated, and a machine learning-based classification model was built to predict treatment response among MSI-H CRCs patients.

Results : A total of 27 patients (15 responders, 12 non-responders) were included. A high degree of lymphocytic/neutrophilic infiltration and an expansile tumor border were associated with treatment response and prolonged progression-free survival (PFS), while mucinous/signet-ring cell carcinoma was associated with a lack of treatment response and short PFS. Gene expression profiles revealed that the interferon-γ response pathway was enriched in the responder group. Of the top eight differentially expressed immune-related genes, PRAME had the highest fold change in the responder group. Higher expression of PRAME was independently associated with better PFS along with histologic subtypes in the multivariate analysis. The classification model using these genes showed good performance for predicting treatment response.

Conclusion : We identified histologic and immune-related gene expression characteristics associated with treatment response in MSI-H CRC, which may contribute to optimal patient stratification.

Hyung Jaewon, Cho Eun Jeong, Kim Jihun, Kim Jwa Hoon, Kim Jeong Eun, Hong Yong Sang, Kim Tae Won, Sung Chang Ohk, Kim Sun Young

2022-Jan-12

Biomarker, Colonic neoplasms, Histology, Immune checkpoint inhibitors, Machine learning, Microsatellite instability, Transcriptome profiles

General General

A machine learning application for raising WASH awareness in the times of COVID-19 pandemic.

In Scientific reports ; h5-index 158.0

The COVID-19 pandemic has revealed the power of internet disinformation in influencing global health. The deluge of information travels faster than the epidemic itself and is a threat to the health of millions across the globe. Health apps need to leverage machine learning for delivering the right information while constantly learning misinformation trends and deliver these effectively in vernacular languages in order to combat the infodemic at the grassroot levels in the general public. Our application, WashKaro, is a multi-pronged intervention that uses conversational Artificial Intelligence (AI), machine translation, and natural language processing to combat misinformation (NLP). WashKaro uses AI to provide accurate information matched against WHO recommendations and delivered in an understandable format in local languages. The primary aim of this study was to assess the use of neural models for text summarization and machine learning for delivering WHO matched COVID-19 information to mitigate the misinfodemic. The secondary aim of this study was to develop a symptom assessment tool and segmentation insights for improving the delivery of information. A total of 5026 people downloaded the app during the study window; among those, 1545 were actively engaged users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot "Satya" increased thus proving the usefulness of a mHealth platform to mitigate health misinformation. We conclude that a machine learning application delivering bite-sized vernacular audios and conversational AI is a practical approach to mitigate health misinformation.

Pandey Rohan, Gautam Vaibhav, Pal Ridam, Bandhey Harsh, Dhingra Lovedeep Singh, Misra Vihaan, Sharma Himanshu, Jain Chirag, Bhagat Kanav, Arushi Patel, Lajjaben Agarwal, Mudit Agrawal, Samprati Jalan, Rishabh Wadhwa, Akshat Garg, Ayush Agrawal, Yashwin Rana, Bhavika Kumaraguru, Ponnurangam Sethi

2022-Jan-17

General General

Many Ways to be Lonely: Fine-grained Characterization of Loneliness and its Potential Changes in COVID-19

ArXiv Preprint

Loneliness has been associated with negative outcomes for physical and mental health. Understanding how people express and cope with various forms of loneliness is critical for early screening and targeted interventions to reduce loneliness, particularly among vulnerable groups such as young adults. To examine how different forms of loneliness and coping strategies manifest in loneliness self-disclosure, we built a dataset, FIG-Loneliness (FIne-Grained Loneliness) by using Reddit posts in two young adult-focused forums and two loneliness related forums consisting of a diverse age group. We provide annotations by trained human annotators for binary and fine-grained loneliness classifications of the posts. Trained on FIG-Loneliness, two BERT-based models were used to understand loneliness forms and authors' coping strategies in these forums. Our binary loneliness classification archived an accuracy above 97%, and fine-grained loneliness category classification reached an average accuracy of 77% across all labeled categories. With FIG-Loneliness and model predictions, we found that loneliness expressions in the young adult related forums are distinct from other forums. Those in young adult-focused forums are more likely to express concerns pertaining to peer relationship, and are potentially more sensitive to geographical isolation impacted by the COVID-19 pandemic lockdown. Also, we show that different forms of loneliness have differential use in coping strategies.

Yueyi Jiang, Yunfan Jiang, Leqi Liu, Piotr Winkielman

2022-01-19

General General

Many Ways to be Lonely: Fine-grained Characterization of Loneliness and its Potential Changes in COVID-19

ArXiv Preprint

Loneliness has been associated with negative outcomes for physical and mental health. Understanding how people express and cope with various forms of loneliness is critical for early screening and targeted interventions to reduce loneliness, particularly among vulnerable groups such as young adults. To examine how different forms of loneliness and coping strategies manifest in loneliness self-disclosure, we built a dataset, FIG-Loneliness (FIne-Grained Loneliness) by using Reddit posts in two young adult-focused forums and two loneliness related forums consisting of a diverse age group. We provide annotations by trained human annotators for binary and fine-grained loneliness classifications of the posts. Trained on FIG-Loneliness, two BERT-based models were used to understand loneliness forms and authors' coping strategies in these forums. Our binary loneliness classification archived an accuracy above 97%, and fine-grained loneliness category classification reached an average accuracy of 77% across all labeled categories. With FIG-Loneliness and model predictions, we found that loneliness expressions in the young adult related forums are distinct from other forums. Those in young adult-focused forums are more likely to express concerns pertaining to peer relationship, and are potentially more sensitive to geographical isolation impacted by the COVID-19 pandemic lockdown. Also, we show that different forms of loneliness have differential use in coping strategies.

Yueyi Jiang, Yunfan Jiang, Liu Leqi, Piotr Winkielman

2022-01-19

General General

A neural network approach to optimising treatments for depression using data from specialist and community psychiatric services in Australia, New Zealand and Japan.

In Neural computing & applications

This study investigated the application of a recurrent neural network for optimising pharmacological treatment for depression. A clinical dataset of 458 participants from specialist and community psychiatric services in Australia, New Zealand and Japan were extracted from an existing custom-built, web-based tool called Psynary . This data, which included baseline and self-completed reviews, was used to train and refine a novel algorithm which was a fully connected network feature extractor and long short-term memory algorithm was firstly trained in isolation and then integrated and annealed using slow learning rates due to the low dimensionality of the data. The accuracy of predicting depression remission before processing patient review data was 49.8%. After processing only 2 reviews, the accuracy was 76.5%. When considering a change in medication, the precision of changing medications was 97.4% and the recall was 71.4% . The medications with predicted best results were antipsychotics (88%) and selective serotonin reuptake inhibitors (87.9%). This is the first study that has created an all-in-one algorithm for optimising treatments for all subtypes of depression. Reducing treatment optimisation time for patients suffering with depression may lead to earlier remission and hence reduce the high levels of disability associated with the condition. Furthermore, in a setting where mental health conditions are increasing strain on mental health services, the utilisation of web-based tools for remote monitoring and machine/deep learning algorithms may assist clinicians in both specialist and primary care in extending specialist mental healthcare to a larger patient community.

Cousins Aidan, Nakano Lucas, Schofield Emma, Kabaila Rasa

2022-Jan-13

Depression, LSTM, Machine learning, Mental health, Treatment optimisation

General General

SIRA: Scale illumination rotation affine invariant mask R-CNN for pedestrian detection.

In Applied intelligence (Dordrecht, Netherlands)

In this paper, we resolve the challenging obstacle of detecting pedestrians with the ubiquity of irregularities in scale, rotation, and the illumination of the natural scene images natively. Pedestrian instances with such obstacles exhibit significantly unique characteristics. Thus, it strongly influences the performance of pedestrian detection techniques. We propose the new robust Scale Illumination Rotation and Affine invariant Mask R-CNN (SIRA M-RCNN) framework for overcoming the predecessor's difficulties. The first phase of the proposed system deals with illumination variation by histogram analysis. Further, we use the contourlet transformation, and the directional filter bank for the generation of the rotational invariant features. Finally, we use Affine Scale Invariant Feature Transform (ASIFT) to find points that are translation and scale-invariant. Extensive evaluation of the benchmark database will prove the effectiveness of SIRA M-RCNN. The experimental results achieve state-of-the-art performance and show a significant performance improvement in pedestrian detection.

Gawande Ujwalla, Hajari Kamal, Golhar Yogesh

2022-Jan-13

CNN, Computer vision, Deep learning, Mask R-CNN, Neural network, Pedestrian detection

General General

Learning protein fitness models from evolutionary and assay-labeled data.

In Nature biotechnology ; h5-index 151.0

Machine learning-based models of protein fitness typically learn from either unlabeled, evolutionarily related sequences or variant sequences with experimentally measured labels. For regimes where only limited experimental data are available, recent work has suggested methods for combining both sources of information. Toward that goal, we propose a simple combination approach that is competitive with, and on average outperforms more sophisticated methods. Our approach uses ridge regression on site-specific amino acid features combined with one probability density feature from modeling the evolutionary data. Within this approach, we find that a variational autoencoder-based probability density model showed the best overall performance, although any evolutionary density model can be used. Moreover, our analysis highlights the importance of systematic evaluations and sufficient baselines.

Hsu Chloe, Nisonoff Hunter, Fannjiang Clara, Listgarten Jennifer

2022-Jan-17

Radiology Radiology

A data-driven ultrasound approach discriminates pathological high grade prostate cancer.

In Scientific reports ; h5-index 158.0

Accurate prostate cancer screening is imperative for reducing the risk of cancer death. Ultrasound imaging, although easy, tends to have low resolution and high inter-observer variability. Here, we show that our integrated machine learning approach enabled the detection of pathological high-grade cancer by the ultrasound procedure. Our study included 772 consecutive patients and 2899 prostate ultrasound images obtained at the Nippon Medical School Hospital. We applied machine learning analyses using ultrasound imaging data and clinical data to detect high-grade prostate cancer. The area under the curve (AUC) using clinical data was 0.691. On the other hand, the AUC when using clinical data and ultrasound imaging data was 0.835 (p = 0.007). Our data-driven ultrasound approach offers an efficient tool to triage patients with high-grade prostate cancers and expands the possibility of ultrasound imaging for the prostate cancer detection pathway.

Akatsuka Jun, Numata Yasushi, Morikawa Hiromu, Sekine Tetsuro, Kayama Shigenori, Mikami Hikaru, Yanagi Masato, Endo Yuki, Takeda Hayato, Toyama Yuka, Yamaguchi Ruri, Kimura Go, Kondo Yukihiro, Yamamoto Yoichiro

2022-Jan-17

Internal Medicine Internal Medicine

Heterogeneous digital biomarker integration out-performs patient self-reports in predicting Parkinson's disease.

In Communications biology

Parkinson's disease (PD) is one of the first diseases where digital biomarkers demonstrated excellent performance in differentiating disease from healthy individuals. However, no study has systematically compared and leveraged multiple types of digital biomarkers to predict PD. Particularly, machine learning works on the fine-motor skills of PD are limited. Here, we developed deep learning methods that achieved an AUC (Area Under the receiver operator characteristic Curve) of 0.933 in identifying PD patients on 6418 individuals using 75048 tapping accelerometer and position records. Performance of tapping is superior to gait/rest and voice-based models obtained from the same benchmark population. Assembling the three models achieved a higher AUC of 0.944. Notably, the models not only correlated strongly to, but also performed better than patient self-reported symptom scores in diagnosing PD. This study demonstrates the complementary predictive power of tapping, gait/rest and voice data and establishes integrative deep learning-based models for identifying PD.

Deng Kaiwen, Li Yueming, Zhang Hanrui, Wang Jian, Albin Roger L, Guan Yuanfang

2022-Jan-17

General General

Frequency dependence prediction and parameter identification of rubber bushing.

In Scientific reports ; h5-index 158.0

Affected by frequency, amplitude and some other factors, the dynamic mechanical properties of rubber bushing are nonlinear. In order to study the frequency dependence of the rubber bushing, a BP neural network optimized by genetic algorithm (GA-BP neural network) is applied to predict the dynamic stiffness and loss factor under frequency of 61-100 Hz. The training data refers to the test data under frequency of 1-60 Hz. And the algorithm is demonstrated by the elastomer test of rubber bushing under amplitudes 0.2 mm, 0.4 mm and 0.6 mm. The results show that the prediction error of dynamic stiffness is less than 1%, and the prediction error of loss factor is less than 3%. In order to apply the predicted results to the software for simulation, a five-parameter mathematical model (FPM) consisting of three elastic elements and two damping elements is developed, and the model parameters are identified by least squares method. According to the fitting results and test data, the fitting error of dynamic stiffness is less than 2%, and the fitting error of loss factor is less than 3%. The GA-BP neural network and FPM model predict the dynamic mechanical behaviour of rubber bushing without the performance of iterative experiments and the incurrence of a high computational cost, making it applicable to analyze full-size vehicles with numerous rubber bushings under various vibration load conditions.

Li Guang, Wu Liguang, Zhang Shuyu, Liu Fang

2022-Jan-17

Pathology Pathology

Modeling risk of Sclerotinia sclerotiorum-induced disease development on canola and dry bean using machine learning algorithms.

In Scientific reports ; h5-index 158.0

Diseases caused by the fungus Sclerotinia sclerotiorum are managed mainly through fungicide applications in canola and dry bean. Accurate estimation of the risk of disease development on these crops could help farmers make spraying decisions. Five machine learning (ML) models were evaluated in classification and regression modes for predicting disease establishment under different air temperatures and leaf wetness duration conditions. Model algorithms were trained and tested using 20-fold cross validation. Correspondence between predicted and observed values were measured using Cohen's Kappa (classification) and Lin's concordance coefficients (regression). The artificial neural network (ANN) algorithms had average accuracies ≥ 89% (classification) and R2 ≥ 88% (regression) on canola and dry bean and their correspondence agreements were ≥ 0.83, which is considered substantial to almost perfect. In contrast, logistic regression algorithms had accuracies of 88% for dry bean and 78% for canola; other models were similarly inconsistent. Implementation of ANN models in disease warning systems could help farmers with spraying decisions. At the same time, these models provide insights on temperature and leaf wetness requirements for development of S. sclerotiorum diseases in these crops. Results of this study show the potential of ML models as tools for epidemiological studies on other pathosystems.

Shahoveisi F, Riahi Manesh M, Del Río Mendoza L E

2022-Jan-17

Radiology Radiology

Automated segmentation of multiparametric magnetic resonance images for cerebral AVM radiosurgery planning: a deep learning approach.

In Scientific reports ; h5-index 158.0

Stereotactic radiosurgery planning for cerebral arteriovenous malformations (AVM) is complicated by the variability in appearance of an AVM nidus across different imaging modalities. We developed a deep learning approach to automatically segment cerebrovascular-anatomical maps from multiple high-resolution magnetic resonance imaging/angiography (MRI/MRA) sequences in AVM patients, with the goal of facilitating target delineation. Twenty-three AVM patients who were evaluated for radiosurgery and underwent multi-parametric MRI/MRA were included. A hybrid semi-automated and manual approach was used to label MRI/MRAs with arteries, veins, brain parenchyma, cerebral spinal fluid (CSF), and embolized vessels. Next, these labels were used to train a convolutional neural network to perform this task. Imaging from 17 patients (6362 image slices) was used for training, and 6 patients (1224 slices) for validation. Performance was evaluated by Dice Similarity Coefficient (DSC). Classification performance was good for arteries, veins, brain parenchyma, and CSF, with DSCs of 0.86, 0.91, 0.98, and 0.91, respectively in the validation image set. Performance was lower for embolized vessels, with a DSC of 0.75. This demonstrates the proof of principle that accurate, high-resolution cerebrovascular-anatomical maps can be generated from multiparametric MRI/MRA. Clinical validation of their utility in radiosurgery planning is warranted.

Simon Aaron B, Hurt Brian, Karunamuni Roshan, Kim Gwe-Ya, Moiseenko Vitali, Olson Scott, Farid Nikdokht, Hsiao Albert, Hattangadi-Gluth Jona A

2022-Jan-17

General General

Enhancing protein inter-residue real distance prediction by scrutinising deep learning models.

In Scientific reports ; h5-index 158.0

Protein structure prediction (PSP) has achieved significant progress lately via prediction of inter-residue distances using deep learning models and exploitation of the predictions during conformational search. In this context, prediction of large inter-residue distances and also prediction of distances between residues separated largely in the protein sequence remain challenging. To deal with these challenges, state-of-the-art inter-residue distance prediction algorithms have used large sets of coevolutionary and non-coevolutionary features. In this paper, we argue that the more the types of features used, the more the kinds of noises introduced and then the deep learning model has to overcome the noises to improve the accuracy of the predictions. Also, multiple features capturing similar underlying characteristics might not necessarily have significantly better cumulative effect. So we scrutinise the feature space to reduce the types of features to be used, but at the same time, we strive to improve the prediction accuracy. Consequently, for inter-residue real distance prediction, in this paper, we propose a deep learning model named scrutinised distance predictor (SDP), which uses only 2 coevolutionary and 3 non-coevolutionary features. On several sets of benchmark proteins, our proposed SDP method improves mean Local Distance Different Test (LDDT) scores at least by 10% over existing state-of-the-art methods. The SDP program along with its data is available from the website https://gitlab.com/mahnewton/sdp .

Rahman Julia, Newton M A Hakim, Islam Md Khaled Ben, Sattar Abdul

2022-Jan-17

General General

Multimodal deep learning applied to classify healthy and disease states of human microbiome.

In Scientific reports ; h5-index 158.0

Metagenomic sequencing methods provide considerable genomic information regarding human microbiomes, enabling us to discover and understand microbial diseases. Compositional differences have been reported between patients and healthy people, which could be used in the diagnosis of patients. Despite significant progress in this regard, the accuracy of these tools needs to be improved for applications in diagnostics and therapeutics. MDL4Microbiome, the method developed herein, demonstrated high accuracy in predicting disease status by using various features from metagenome sequences and a multimodal deep learning model. We propose combining three different features, i.e., conventional taxonomic profiles, genome-level relative abundance, and metabolic functional characteristics, to enhance classification accuracy. This deep learning model enabled the construction of a classifier that combines these various modalities encoded in the human microbiome. We achieved accuracies of 0.98, 0.76, 0.84, and 0.97 for predicting patients with inflammatory bowel disease, type 2 diabetes, liver cirrhosis, and colorectal cancer, respectively; these are comparable or higher than classical machine learning methods. A deeper analysis was also performed on the resulting sets of selected features to understand the contribution of their different characteristics. MDL4Microbiome is a classifier with higher or comparable accuracy compared with other machine learning methods, which offers perspectives on feature generation with metagenome sequences in deep learning models and their advantages in the classification of host disease status.

Lee Seung Jae, Rho Mina

2022-Jan-17

General General

Differential evolution-assisted salp swarm algorithm with chaotic structure for real-world problems.

In Engineering with computers

There is a new nature-inspired algorithm called salp swarm algorithm (SSA), due to its simple framework, it has been widely used in many fields. But when handling some complicated optimization problems, especially the multimodal and high-dimensional optimization problems, SSA will probably have difficulties in convergence performance or dropping into the local optimum. To mitigate these problems, this paper presents a chaotic SSA with differential evolution (CDESSA). In the proposed framework, chaotic initialization and differential evolution are introduced to enrich the convergence speed and accuracy of SSA. Chaotic initialization is utilized to produce a better initial population aim at locating a better global optimal. At the same time, differential evolution is used to build up the search capability of each agent and improve the sense of balance of global search and intensification of SSA. These mechanisms collaborate to boost SSA in accelerating convergence activity. Finally, a series of experiments are carried out to test the performance of CDESSA. Firstly, IEEE CEC2014 competition fuctions are adopted to evaluate the ability of CDESSA in working out the real-parameter optimization problems. The proposed CDESSA is adopted to deal with feature selection (FS) problems, then five constrained engineering optimization problems are also adopted to evaluate the property of CDESSA in dealing with real engineering scenarios. Experimental results reveal that the proposed CDESSA method performs significantly better than the original SSA and other compared methods.

Zhang Hongliang, Liu Tong, Ye Xiaojia, Heidari Ali Asghar, Liang Guoxi, Chen Huiling, Pan Zhifang

2022-Jan-10

Chaotic initialization, Engineering optimization problems, Feature selection, Global optimization, Salp swarm algorithm

General General

COVID-19 and cyberbullying: deep ensemble model to identify cyberbullying from code-switched languages during the pandemic.

In Multimedia tools and applications

It has been declared by the World Health Organization (WHO) the novel coronavirus a global pandemic due to an exponential spread in COVID-19 in the past months reaching over 100 million cases and resulting in approximately 3 million deaths worldwide. Amid this pandemic, identification of cyberbullying has become a more evolving area of research over posts or comments in social media platforms. In multilingual societies like India, code-switched texts comprise the majority of the Internet. Identifying the online bullying of the code-switched user is bit challenging than monolingual cases. As a first step towards enabling the development of approaches for cyberbullying detection, we developed a new code-switched dataset, collected from Twitter utterances annotated with binary labels. To demonstrate the utility of the proposed dataset, we build different machine learning (Support Vector Machine & Logistic Regression) and deep learning (Multilayer Perceptron, Convolution Neural Network, BiLSTM, BERT) algorithms to detect cyberbullying of English-Hindi (En-Hi) code-switched text. Our proposed model integrates different hand-crafted features and is enriched by sequential and semantic patterns generated by different state-of-the-art deep neural network models. Initial experimental results of the proposed deep ensemble model on our code-switched data reveal that our approach yields state-of-the-art results, i.e., 0.93 in terms of macro-averaged F1 score. The dataset and codes of the present study will be made publicly available on the paper's companion repository [https://github.com/95sayanta/COVID-19-and-Cyberbullying].

Paul Sayanta, Saha Sriparna, Singh Jyoti Prakash

2022-Jan-08

Code-switched language, Cyberbullying, Deep ensemble, Natural language processing

General General

A deep learning classification of metacarpophalangeal joints synovial proliferation in rheumatoid arthritis by ultrasound images.

In Journal of clinical ultrasound : JCU

OBJECTIVE : To evaluate if an automatic classification of rheumatoid arthritis (RA) metacarpophalangeal joint conditions in ultrasound images is feasible by deep learning (DL) method, to provide a more objective, automated, and fast way of RA diagnosis in clinical setting.

MATERIALS AND METHODS : DenseNet-based DL model was used and both training and testing are implemented in TensorFlow 1.13.1 with Keras DL libraries. The area under curve (AUC), accuracy, sensitivity, and specificity values with 95% CIs were reported. The statistical analysis was performed by using scikit-learn libraries in Python 3.7.

RESULTS : A total of 1337 RA ultrasound images were acquired from 208 patients, the number of images is 313, 657, 178, and 189 in OESS Grade L0, L1, L2, and L3, respectively. In Classification Scenario 1 SP-no versus SP-yes, three experiments with region of interest of size 192 × 448 (Group 1), 96 × 224 (Group 2), and 96 × 224 stacked with pre-segmented annotated mask of SP area (Group 3) as input achieve an AUC of 0.863 (95% CI: 0.809, 0.917), 0.861 (95% CI: 0.805, 0.916), and 0.886 (95% CI: 0.836, 0.936), respectively. In Classification Scenario 2 Healthy versus Diseased, experiments in Group 1, Group 2 and Group 3 achieve an AUC of 0.848 (95% CI: 0.799, 0.896), 0.864 (95% CI: 0.819, 0.909), and 0.916 (95% CI: 0.883, 0.952), respectively.

CONCLUSION : We combined DenseNet model with ultrasound images for RA condition assessment. The feasibility of using DL to create an automatic RA condition classification system was also demonstrated. The proposed method can be an alternative to the initial screening of RA patients.

Wu Min, Wu Huaiuy, Wu Lili, Cui Chen, Shi Siyuan, Xu Jinfeng, Liu Yan, Dong Fajin

2022-Jan-17

artificial intelligence, deep learning, rheumatoid arthritis, synovitis, ultrasonography

General General

Automated detection of ulcers and erosions in capsule endoscopy images using a convolutional neural network.

In Medical & biological engineering & computing ; h5-index 32.0

Capsule endoscopy (CE) is an important tool in the management of patients with known or suspected inflammatory bowel disease. Ulcers and erosions of the enteric mucosa are prevalent findings in these patients. They frequently occur together, and their identification in CE is crucial for an accurate evaluation of disease severity. Nevertheless, reviewing CE images is a time-consuming task, and the risk of overlooking lesions is significant.Over the last decade, artificial intelligence (AI) has emerged as a means for overcoming these pitfalls. Of all AI methods, convolutional neural networks (CNN), due to their complex multilayer architecture present the best results in medical image analysis, particularly capsule endoscopy. Therefore, we aimed to develop a CNN for the automatic identification of ulcers and erosions in the small bowel mucosa. A total of 1483 CE exams (PillCam SB3®) performed at a single center between 2015 and 2020 were analysed. From these exams, a total of 6130 frames of the enteric mucosa were obtained, 4233 containing enteric ulcers and erosions, and the remaining containing normal mucosa or other findings. Ulcers and erosions were stratified according to Saurin's classification for bleeding potential: P1E-erosions with intermediate bleeding risk; P1U-ulcers with intermediate bleeding risk; P2U-ulcers with high bleeding risk. For automatic identification of these lesions, these images were inserted into a CNN model with transfer learning. The pool of images was divided for constitution of training and validation datasets, comprising 80% and 20% of the total number of images, respectively. The output provided by the CNN was compared to the classification provided by a consensus of specialists. After optimizing the neural architecture of the algorithm, our model was able to automatically detect and distinguish ulcers and erosions (any bleeding potential) in the small intestine mucosa with an accuracy of 95.6%, sensitivity of 90.8%, and a specificity of 97.1%. We believe that our study lays the foundation for the development and application of effective AI tools to CE. These techniques should improve diagnostic accuracy and reading efficiency. Schematic representation of the workflow and summary of the results.

Afonso João, Saraiva Miguel Mascarenhas, Ferreira J P S, Cardoso Hélder, Ribeiro Tiago, Andrade Patrícia, Parente Marco, Jorge Renato N, Macedo Guilherme

2022-Jan-17

Artificial intelligence, Capsule endoscopy, Convolutional neural network, Gastrointestinal bleeding, Ulcers

General General

Development of an open-source measurement system to assess the areal bone mineral density of the proximal femur from clinical CT images.

In Archives of osteoporosis

** : Commercial software is generally needed to measure the areal bone mineral density (aBMD) of the proximal femur from clinical computed tomography (CT) images. This study developed and verified an open-source reproducible system to quantify CT-aBMD to screen osteoporosis using clinical CT images.

PURPOSE : For existing CT images acquired for various reasons other than osteoporosis, it might be beneficial to estimate areal BMD as assessed by dual-energy X-ray absorptiometry (DXA-based BMD) to ascertain the bone status based on DXA. In this study, we aimed to (1) develop an open-source reproducible measurement system to quantify DXA-based BMD from CT images and (2) validate its accuracy.

METHODS : This study analyzed 75 pairs of hip CT and DXA images of women that were acquired for the preoperative assessment of total hip arthroplasty. From the CT images, the femur and a calibration phantom were automatically segmented using pre-trained codes/models available at https://github.com/keisuke-uemura . The proximal femoral region was isolated by manually selected landmarks and was projected onto the coronal plane to measure the areal density (CT-aHU). The calibration phantom was employed to convert the CT-aHU into CT-aBMD. Each parameter was correlated with DXA-based BMD, and the residual errors of CT images to estimate the T-scores in DXA were calculated using the standard error of estimate (SEE).

RESULTS : The correlation coefficients of DXA-based BMD with CT-aHU and CT-aBMD were 0.947 and 0.950, respectively (both p < 0.001). The SEE for quantifying the T-scores in DXA were 0.51 and 0.50 for CT-aHU and CT-aBMD, respectively.

CONCLUSION : With the method developed herein, CT permits estimation of the DXA-based BMD of the proximal femur within the standard DXA total hip region of interest with an SEE of 0.5 in T-scores. The radiation dose for CT acquisition needs consideration; therefore, our data do not provide a rationale for performing CT for screening osteoporosis. However, on CT images already acquired for clinical indications other than osteoporosis, researchers may use this open-source system to investigate osteoporosis status through the estimated DXA-based BMD of the proximal femur.

Uemura Keisuke, Otake Yoshito, Takao Masaki, Makino Hiroki, Soufi Mazen, Iwasa Makoto, Sugano Nobuhiko, Sato Yoshinobu

2022-Jan-17

Artificial intelligence, Convolutional neural network (CNN), Deep learning, Dual-energy X-ray absorptiometry, Open-source system, Quantitative computed tomography

Surgery Surgery

Association of preterm birth with medications: machine learning analysis using national health insurance data.

In Archives of gynecology and obstetrics ; h5-index 44.0

PURPOSE : To use machine learning and population data for testing the associations of preterm birth with socioeconomic status, gastroesophageal reflux disease (GERD) and medication history including proton pump inhibitors, sleeping pills and antidepressants.

METHODS : Population-based retrospective cohort data came from Korea National Health Insurance Service claims data for all women who aged 25-40 years and gave births for the first time as singleton pregnancy during 2015-2017 (405,586 women). The dependent variable was preterm birth during 2015-2017 and 65 independent variables were included (demographic/socioeconomic determinants, disease information, medication history, obstetric information). Random forest variable importance (outcome measure) was used for identifying major determinants of preterm birth and testing its associations with socioeconomic status, GERD and medication history including proton pump inhibitors, sleeping pills and antidepressants.

RESULTS : Based on random forest variable importance, major determinants of preterm birth during 2015-2017 were socioeconomic status (645.34), age (556.86), proton pump inhibitors (107.61), GERD for the years 2014, 2012 and 2013 (106.78, 105.87 and 104.96), sleeping pills (97.23), GERD for the years 2010, 2011 and 2009 (95.56, 94.84 and 93.81), and antidepressants (90.13).

CONCLUSION : Preterm birth has strong associations with low socioeconomic status, GERD and medication history such as proton pump inhibitors, sleeping pills and antidepressants. For preventing preterm birth, appropriate medication would be needed alongside preventive measures for GERD and the promotion of socioeconomic status for pregnant women.

Lee Kwang-Sig, Song In-Seok, Kim Eun Sun, Kim Hae-In, Ahn Ki Hoon

2022-Jan-17

Antidepressants, Preterm birth, Proton pump inhibitors, Sleeping pills

General General

A deep learning radiomics model may help to improve the prediction performance of preoperative grading in meningioma.

In Neuroradiology

PURPOSE : This study aimed to investigate the clinical usefulness of the enhanced-T1WI-based deep learning radiomics model (DLRM) in differentiating low- and high-grade meningiomas.

METHODS : A total of 132 patients with pathologically confirmed meningiomas were consecutively enrolled (105 in the training cohort and 27 in the test cohort). Radiomics features and deep learning features were extracted from T1 weighted images (T1WI) (both axial and sagittal) and the maximum slice of the axial tumor lesion, respectively. Then, the synthetic minority oversampling technique (SMOTE) was utilized to balance the sample numbers. The optimal discriminative features were selected for model building. LightGBM algorithm was used to develop DLRM by a combination of radiomics features and deep learning features. For comparison, a radiomics model (RM) and a deep learning model (DLM) were constructed using a similar method as well. Differentiating efficacy was determined by using the receiver operating characteristic (ROC) analysis.

RESULTS : A total of 15 features were selected to construct the DLRM with SMOTE, which showed good discrimination performance in both the training and test cohorts. The DLRM outperformed RM and DLM for differentiating low- and high-grade meningiomas (training AUC: 0.988 vs. 0.980 vs. 0.892; test AUC: 0.935 vs. 0.918 vs. 0.718). The accuracy, sensitivity, and specificity of the DLRM with SMOTE were 0.926, 0.900, and 0.924 in the test cohort, respectively.

CONCLUSION : The DLRM with SMOTE based on enhanced T1WI images has favorable performance for noninvasively individualized prediction of meningioma grades, which exhibited favorable clinical usefulness superior over the radiomics features.

Yang Liping, Xu Panpan, Zhang Ying, Cui Nan, Wang Menglu, Peng Mengye, Gao Chao, Wang Tianzuo

2022-Jan-17

Deep learning, Magnetic resonance imaging (MRI), Meningiomas, Radiomics

Radiology Radiology

Development and external validation of a stability machine learning model to identify wake-up stroke onset time from MRI.

In European radiology ; h5-index 62.0

OBJECTIVES : To develop and externally validate a machine learning (ML) model based on diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) to identify the onset time of wake-up stroke from MRI.

METHODS : DWI and FLAIR images of stroke patients within 24 h of clear symptom onset in our hospital (dataset 1, n = 410) and another hospital (dataset 2, n = 177) were included. Seven ML models based on dataset 1 were developed to estimate the stroke onset time for binary classification (≤ 4.5 h or > 4.5 h): Random Forest (RF), support vector machine with kernel (svmLinear) or radial basis function kernel (svmRadial), Bayesian (Bayes), K-nearest neighbor (KNN), adaptive boosting (AdaBoost), and neural network (NNET). ROC analysis and RSD were performed to evaluate the performance and stability of the ML models, respectively, and dataset 2 was externally validated to evaluate the model generalization ability using ROC analysis.

RESULTS : svmRadial achieved the best performance with the highest AUC and accuracy (AUC: 0.896, accuracy: 0.878), and was the most stable (RSD% of AUC: 0.08, RSD% of accuracy: 0.06). The svmRadial model was then selected as the final model, and the AUC of the svmRadial model for predicting the onset time external validation was 0.895, with 0.825 accuracy.

CONCLUSIONS : The svmRadial model using DWI + FLAIR is the most stable and generalizable for identifying the onset time of wake-up stroke patients within 4.5 h of symptom onset.

KEY POINTS : • Machining learning model helps clinicians to identify wake-up stroke patients within 4.5 h of symptom onset. • A prospective study showed that svmRadial model based on DWI + FLAIR was the most stable in predicting the stroke onset time. • External validation showed that svmRadial model has good generalization ability in predicting the stroke onset time.

Jiang Liang, Wang Siyu, Ai Zhongping, Shen Tingwen, Zhang Hong, Duan Shaofeng, Chen Yu-Chen, Yin Xindao, Sun Jun

2022-Jan-17

Diffusion-weighted imaging, Fluid-attenuated inversion recovery, Machine learning, Wake-up stroke

Radiology Radiology

Additive value of epicardial adipose tissue quantification to coronary CT angiography-derived plaque characterization and CT fractional flow reserve for the prediction of lesion-specific ischemia.

In European radiology ; h5-index 62.0

OBJECTIVES : Epicardial adipose tissue (EAT) from coronary CT angiography (CCTA) is strongly associated with coronary artery disease (CAD). We investigated the additive value of EAT volume to coronary plaque quantification and CT-derived fractional flow reserve (CT-FFR) to predict lesion-specific ischemia.

METHODS : Patients (n = 128, 60.6 ± 10.5 years, 61% male) with suspected CAD who had undergone invasive coronary angiography (ICA) and CCTA were retrospectively analyzed. EAT volume and plaque measures were derived from CCTA using a semi-automatic software approach, while CT-FFR was calculated using a machine learning algorithm. The predictive value and discriminatory power of EAT volume, plaque measures, and CT-FFR to identify ischemic CAD were assessed using invasive FFR as the reference standard.

RESULTS : Fifty-five of 152 lesions showed ischemic CAD by invasive FFR. EAT volume, CCTA ≥ 50% stenosis, and CT-FFR were significantly different in lesions with and without hemodynamic significance (all p < 0.05). Multivariate analysis revealed predictive value for lesion-specific ischemia of these parameters: EAT volume (OR 2.93, p = 0.021), CCTA ≥ 50% (OR 4.56, p = 0.002), and CT-FFR (OR 6.74, p < 0.001). ROC analysis demonstrated incremental discriminatory value with the addition of EAT volume to plaque measures alone (AUC 0.84 vs. 0.62, p < 0.05). CT-FFR (AUC 0.89) showed slightly superior performance over EAT volume with plaque measures (AUC 0.84), however without significant difference (p > 0.05).

CONCLUSIONS : EAT volume is significantly associated with ischemic CAD. The combination of EAT volume with plaque quantification demonstrates a predictive value for lesion-specific ischemia similar to that of CT-FFR. Thus, EAT may aid in the identification of hemodynamically significant coronary stenosis.

KEY POINTS : • CT-derived EAT volume quantification demonstrates high discriminatory power to identify lesion-specific ischemia. • EAT volume shows incremental diagnostic performance over CCTA-derived plaque measures in detecting lesion-specific ischemia. • A combination of plaque measures with EAT volume provides a similar discriminatory value for detecting lesion-specific ischemia compared to CT-FFR.

Brandt Verena, Decker Josua, Schoepf U Joseph, Varga-Szemes Akos, Emrich Tilman, Aquino Gilberto, Bayer Richard R, Carson Landin, Sullivan Allison, Ellis Lauren, von Knebel Doeberitz Philipp L, Ebersberger Ullrich, Bekeredjian Raffi, Tesche Christian

2022-Jan-17

Angiography, Computed tomography, Coronary artery disease, Epicardial adipose tissue

General General

A human-based multi-gene signature enables quantitative drug repurposing for metabolic disease.

In eLife

Insulin resistance (IR) contributes to the pathophysiology of diabetes, dementia, viral infection, and cardiovascular disease. Drug repurposing (DR) may identify treatments for IR; however, barriers include uncertainty whether in vitro transcriptomic assays yield quantitative pharmacological data, or how to optimise assay design to best reflect in vivo human disease. We developed a clinical-based human tissue IR signature by combining lifestyle-mediated treatment responses (>500 human adipose and muscle biopsies) with biomarkers of disease status (fasting IR from >1200 biopsies). The assay identified a chemically diverse set of >130 positively acting compounds, highly enriched in true positives, that targeted 73 proteins regulating IR pathways. Our multi-gene RNA assay score reflected the quantitative pharmacological properties of a set of epidermal growth factor receptor-related tyrosine kinase inhibitors, providing insight into drug target specificity; an observation supported by deep learning-based genome-wide predicted pharmacology. Several drugs identified are suitable for evaluation in patients, particularly those with either acute or severe chronic IR.

Timmons James A, Anighoro Andrew, Brogan Robert J, Stahl Jack, Wahlestedt Claes, Farquhar David Gordon, Taylor-King Jake, Volmar Claude-Henry, Kraus William E, Phillips Stuart M

2022-Jan-17

COVID-19, Diabetes, Exercise, Transcriptomics, computational biology, deep learning, drug repurposing, human, insulin biology, medicine, systems biology

Public Health Public Health

Molecular docking, molecular dynamics simulation and MM-GBSA studies of the activity of glycyrrhizin relevant substructures on SARS-CoV-2 RNA-dependent-RNA polymerase.

In Journal of biomolecular structure & dynamics

SARS-CoV-2 is the causative agent of Coronavirus Disease (COVID-19), which is a life-threatening disease. The World Health Organization has classified COVID-19 as a severe worldwide public health pandemic due to its high death rate, quick transmission, and lack of medicines. To counteract the recurrence of the severe acute respiratory syndrome, active antiviral medications are urgently required. Glycyrrhizin was documented with activity on different viral proteins, including SARS-CoV-2; in this study, the activity of glycyrrhizin and its substructures (604 molecules) were screened on SARS-CoV-2 RNA-dependent-RNA polymerase using molecular docking, molecular dynamic (MD) simulation, and MM/GBSA. Sixteen molecules exhibited docking energy higher than -7 kcal/mol; four compounds (10772603, 101088272, 154730753 and glycyrrhizin) showed the highest binding energy, and good stability during MD simulation. The glycyrrhizin compound exhibited favorable docking energy (-7.9 kcal/mol), and it was the most stable complex during MD simulation. The predicted binding free energy of the glycyrrhizin complex was -57 ± 8 kcal/mol. These findings suggest that this molecule, after more validation, could become a good candidate for developing and manufacturing an anti-SARS-CoV-2 medication.Communicated by Ramaswamy H. Sarma.

Zamzami Mazin A

2022-Jan-17

Coronavirus, RdRp, SARS-CoV-2, antiviral agent, molecular docking; natural compound

Internal Medicine Internal Medicine

The importance of association of comorbidities on COVID-19 outcomes: a machine learning approach.

In Current medical research and opinion

BACKGROUND : The individual influence of a variety of comorbidities on COVID-19 patient outcomes has already been analyzed in previous works in an isolated way. We aim to determine if different associations of diseases influence the outcomes of inpatients with COVID-19.

METHODS : Retrospective cohort multicenter study based on clinical practice. Data were taken from the SEMI-COVID-19 Registry, which includes most consecutive patients with confirmed COVID-19 hospitalized and discharged in Spain. Two machine learning algorithms were applied in order to classify comorbidities and patients (Random Forest -RF algorithm, and Gaussian mixed model by clustering -GMM-). The primary endpoint was a composite of either, all-cause death or intensive care unit admission during the period of hospitalization. The sample was randomly divided into training and test sets to determine the most important comorbidities related to the primary endpoint, grow several clusters with these comorbidities based on a discriminant analysis and GMM, and compare these clusters.

RESULTS : A total of 16,455 inpatients (57·4% women and 42·6% men) were analyzed. According to the RF algorithm, the most important comorbidities were heart failure/atrial fibrillation (HF/AF), vascular diseases, and neurodegenerative diseases. There were six clusters: three included patients who met the primary endpoint (clusters 4, 5, and 6) and three included patients who did not (clusters 1, 2, and 3). Patients with HF/AF, vascular diseases, and neurodegenerative diseases were distributed among clusters 3, 4 and 5. Patients in cluster 5 also had kidney, liver, and acid peptic diseases as well as chronic obstructive pulmonary disease; it was the cluster with the worst prognosis.

CONCLUSION : The interplay of several comorbidities may affect the outcome and complications of inpatients with COVID-19.

Arévalo-Lorido José Carlos, Carretero-Gómez Juana, Casas-Rojo Jose Manuel, Antón-Santos Juan Miguel, Melero-Bermejo José Antonio, López-Carmona Maria Dolores, Palacios Lidia Cobos, Sanz-Cánovas Jaime, Pesqueira-Fontán Paula Maria, de la Peña-Fernández Andrés Alberto, de la Sierra Alcántara Navas-Maria, García-García Gema Maria, Torres Peña José David, Magallanes-Gamboa Jeffrey Oskar, Fernández-Madera-Martinez Rosa, Fernández-Fernández Javier, Rubio-Rivas Manuel, Maestro-de la Calle Guillermo, Cervilla-Muñoz Eva, Ramos-Martínez Antonio, Méndez-Bailón Manuel, Ramos-Rincón José Manuel, Gómez-Huelgas Ricardo

2022-Jan-17

COVID-19, Cluster analysis, Comorbidity, Machine learning, SARS-CoV-2

General General

Internationalizing AI: evolution and impact of distance factors.

In Scientometrics

** : International collaboration has become imperative in the field of AI. However, few studies exist concerning how distance factors have affected the international collaboration in AI research. In this study, we investigate this problem by using 1,294,644 AI related collaborative papers harvested from the Microsoft Academic Graph dataset. A framework including 13 indicators to quantify the distance factors between countries from 5 perspectives (i.e., geographic distance, economic distance, cultural distance, academic distance, and industrial distance) is proposed. The relationships were conducted by the methods of descriptive analysis and regression analysis. The results show that international collaboration in the field of AI today is not prevalent (only 15.7%). All the separations in international collaborations have increased over years, except for the cultural distance in masculinity/felinity dimension and the industrial distance. The geographic distance, economic distance and academic distances have shown significantly negative relationships with the degree of international collaborations in the field of AI. The industrial distance has a significant positive relationship with the degree of international collaboration in the field of AI. Also, the results demonstrate that the participation of the United States and China have promoted the international collaboration in the field of AI. This study provides a comprehensive understanding of internationalizing AI research in geographic, economic, cultural, academic, and industrial aspects.

Supplementary Information : The online version contains supplementary material available at 10.1007/s11192-021-04207-3.

Tang Xuli, Li Xin, Ma Feicheng

2022-Jan-11

AI, Academic distance, Artificial intelligence, Cultural distance, Economic distance, Geographic distance, Industrial distance, International collaboration

General General

Predicting the impact of online news articles - is information necessary?: Application to COVID-19 articles.

In Multimedia tools and applications

We exploit the Twitter platform to create a dataset of news articles derived from tweets concerning COVID-19, and use the associated tweets to define a number of popularity measures. The focus on (potentially) biomedical news articles allows the quantity of biomedically valid information (as extracted by biomedical relation extraction) to be included in the list of explored features. Aside from forming part of a systematic correlation exploration, the features - ranging from the semantic relations through readability measures to the article's digital content - are used within a number of machine learning classifier and regression algorithms. Unsurprisingly, the results support that for more complex articles (as determined by a readability measure) more sophisticated syntactic structure may be expected. A weak correlation is found with information within an article suggesting that other factors, such as numbers of videos, have a notable impact on the popularity of a news article. The best popularity prediction performance is obtained using a random forest machine learning algorithm, and the feature describing the quantity of biomedical information is in the top 3 most important features in almost a third of the experiments performed. Additionally, this feature is found to be more valuable than the widely used named entity recognition.

Preiss Judita

2022-Jan-08

Grammatical relations, Popularity prediction, SemRep relations, Twitter

General General

Explainable deep learning in healthcare: A methodological survey from an attribution view.

In WIREs mechanisms of disease

The increasing availability of large collections of electronic health record (EHR) data and unprecedented technical advances in deep learning (DL) have sparked a surge of research interest in developing DL based clinical decision support systems for diagnosis, prognosis, and treatment. Despite the recognition of the value of deep learning in healthcare, impediments to further adoption in real healthcare settings remain due to the black-box nature of DL. Therefore, there is an emerging need for interpretable DL, which allows end users to evaluate the model decision making to know whether to accept or reject predictions and recommendations before an action is taken. In this review, we focus on the interpretability of the DL models in healthcare. We start by introducing the methods for interpretability in depth and comprehensively as a methodological reference for future researchers or clinical practitioners in this field. Besides the methods' details, we also include a discussion of advantages and disadvantages of these methods and which scenarios each of them is suitable for, so that interested readers can know how to compare and choose among them for use. Moreover, we discuss how these methods, originally developed for solving general-domain problems, have been adapted and applied to healthcare problems and how they can help physicians better understand these data-driven technologies. Overall, we hope this survey can help researchers and practitioners in both artificial intelligence and clinical fields understand what methods we have for enhancing the interpretability of their DL models and choose the optimal one accordingly. This article is categorized under: Cancer > Computational Models.

Jin Di, Sergeeva Elena, Weng Wei-Hung, Chauhan Geeticka, Szolovits Peter

2022-Jan-17

deep learning in medicine, interpretable deep learning

Public Health Public Health

Microscopic retinal blood vessels detection and segmentation using support vector machine and K-nearest neighbors.

In Microscopy research and technique

The retina is the deepest layer of texture covering the rear of the eye, recorded by fundus images. Vessel detection and segmentation are useful in disease diagnosis. The retina's blood vessels could help diagnose maladies such as glaucoma, diabetic retinopathy, and blood pressure. A mix of supervised and unsupervised strategies exists for the detection and segmentation of blood vessels images. The tree structure of retinal blood vessels, their random area, and different thickness have caused vessel detection difficulties at machine learning calculations. Since the green band of retinal images conveys more information about the vessels, they are utilized for microscopic vessels detection. The current research proposes an administered calculation for segmentation of retinal vessels, where two upgrading stages depending on filtering and comparative histogram were applied after pre-processing and image quality improvement. At that point, statistical features of vessel tracking, maximum curvature and curvelet coefficient are extracted for each pixel. The extracted features are classified by support vector machine and the k-nearest neighbors. The morphological operators then enhance the classified image at the final stage to segment with higher accuracy. The dice coefficient is utilized for the evaluation of the proposed method. The proposed approach is concluded to be better than different strategies with a normal of 92%.

Rehman Amjad, Harouni Majid, Karimi Mohsen, Saba Tanzila, Bahaj Saeed Ali, Awan Mazar Javed

2022-Jan-17

blood vessels, healthcare, human and disease, microscopic retina, public health, segmentation, tracking

oncology Oncology

Site-Agnostic 3D dose distribution prediction with deep learning neural networks.

In Medical physics ; h5-index 59.0

PURPOSE : Typically, the current dose prediction models are limited to small amounts of data and require re-training for a specific site, often leading to suboptimal performance. We propose a site-agnostic, three dimensional dose distribution prediction model using deep learning that can leverage data from any treatment site, thus increasing the total data available to train the model. Applying our proposed model to a new target treatment site requires only a brief fine-tuning of the model to the new data and involves no modifications to the model input channels or its parameters. Thus, it can be efficiently adapted to a different treatment site, even with a small training dataset.

METHODS : This study uses two separate datasets/treatment sites: data from patients with prostate cancer treated with intensity-modulated radiation therapy (IMRT) (source data), and data from patients with head-and-neck cancer treated with volumetric modulated arc therapy (VMAT) (target data). We first developed a source model with 3D UNet architecture, trained from random initial weights on the source data. We evaluated the performance of this model on the source data. We then studied the generalizability of the model to the new target dataset via transfer learning. To do this, we built three more models, all with the same 3D UNet architecture: target model, adapted model, and combined model. The source and target models were trained on the source and target data from random initial weights, respectively. The adapted model fine-tuned the source model to the target domain by using the target data. Finally, the combined model was trained from random initial weights on a combined data pool consisting of both target and source datasets. We tested all four models on the target dataset and evaluated quantitative dose-volume-histogram (DVH) metrics for the planning target volume (PTV) and organs at risk (OARs).

RESULTS : When tested on the source treatment site, the source model accurately predicted the dose distributions with average (mean, max) absolute dose errors of (0.32%±0.14, 2.37%±0.93) (PTV) relative to the prescription dose, and highest mean dose error of 1.68%±0.76, and highest max dose error of 5.47%± 3.31 for femoral head right. The error in PTV dose coverage prediction is 3.21%±1.51 for D98 , 3.04%±1.69 for D95 and 1.83%±1.01 for D02 . Averaging across all OARs, the source model predicted the OAR mean dose within 1.38% and the OAR max dose within 3.64%. For the target treatment site, the target model average (mean, max) absolute dose errors relative to the prescription dose for the PTV were (1.08%±0.95, 2.90%±1.35). Left cochlea had the highest mean and max dose errors of 5.37%±5.82 and 8.33%±8.88, respectively. The errors in PTV dose coverage prediction for D98 and D95 were 2.88%±1.59 and 2.55%±1.28, respectively. The target model can predict the OAR mean dose within 2.43% and the OAR max dose within 4.33% on average across all OARs.

CONCLUSION : We developed a site-agnostic model for three dimensional dose prediction and tested its adaptability to a new target treatment site via transfer learning. Our proposed model can make accurate predictions with limited training data. This article is protected by copyright. All rights reserved.

Mashayekhi Maryam, Tapia Itzel Ramirez, Balagopal Anjali, Zhong Xinran, Barkousaraie Azar Sadeghnejad, McBeth Rafe, Lin Mu-Han, Jiang Steve, Nguyen Dan

2022-Jan-17

General General

High-intensity interval training along with spirulina algae consumption and caloric restriction ameliorated the Nrf1/Tfam/Mgmt and ATP5A1 pathway in the heart tissue of obese rats.

In Journal of food biochemistry

Nrf1/Tfam/MGMT and ATP5A1 might be a pivotal network in cardiovascular disease-inducing obesity. Therefore, we evaluated eight weeks of exercise, caloric restriction, and spirulina algae consumption on the heart in obese rats. In this study, obese rats were compared with a healthy group. First, we induced obese rats with a 60%-high-fat diet. Then, after eight weeks, obese rats were randomly divided into eight groups: obese rats without treatment (HFD), obese rats treated with spirulina algae (HFD-SA), obese rats conducted exercise (HFD-EX), obese rats treated with spirulina algae and exercise (HFD-SA+EX), obese rats treated with caloric restriction (HFD-CR), obese rats treated with caloric restriction and exercise (HFD-CR+EX), obese rats treated with spirulina algae and caloric restriction (HFD-SA+CR), and obese rats treated with SA+CR+EX (HFD-SA+CR+EX). Also, the exercise protocol was performed for eight weeks, three sessions per week at an intensity of 80%-110% of maximum running speed. The spirulina algae were consumed by gavage (100 mg/kg/day), and caloric restriction used 60% of the food consumed. We found that SA+CR+EX significantly modified the Nrf1/Tfam/MGMT and ATP5A1 network in cardiovascular disease-inducing obesity rats (p < .01). Moreover, we predicted SA could be bound to Tfam and MGMT protein targets. Hence, exercise, caloric restriction, and spirulina algae had a synergistic effect on mitochondrial biogenesis in the heart tissue of obese rats (p < .01). PRACTICAL APPLICATIONS: According to artificial intelligence and medical biology servers, we discovered that mitochondrial biogenesis and oxidative stress are dominant phenomena in the cardiovascular system. Nrf1/Tfam/MGMT and ATP5A1, as pivotal regulators of oxidative stress, could play an utmost important role in the cardiovascular disease-inducing obesity molecular pathway. Furthermore, several studies have indicated that environmental factors such as the western diet and physical inactivity disrupted the mitochondrial dynamic, which led to increased reactive oxygen species (ROS). We predicted the binding power of the Spirulina's small molecules on Tfam and Mgmt proteins based on drug-discovery technology and pharmacokinetic parameters. Considering oxidative stress and mitochondrial machinery related to the action of some molecular pathways, mitochondria-related nuclear-encoded proteins, and ROS, this study evaluated the high-intensity interval training, caloric restriction, and spirulina consumption on heart mitochondrial biogenesis in obese rats. Our data might provide a novel strategy for the prevention and treatment of cardiovascular disease-inducing obesity.

Yousefian Mahboobeh, Taghian Farzaneh, Sharifi Gholamreza, Hosseini Seyed Ali

2022-Jan-16

Nrf1, caloric restriction, exercise, high-intensity interval training, obesity, spirulina algae

General General

Machine Learning for Image Analysis: Leaf Disease Segmentation.

In Methods in molecular biology (Clifton, N.J.)

Plant phenomics field has seen a great increase in scalability in the last decade mainly due to technological advances in remote sensors and phenotyping platforms. These are capable of screening thousands of plants many times throughout the day, generating massive amounts of data, which require an automated analysis to extract meaningful information. Deep learning is a branch of machine learning that has revolutionized many fields of research. Deep learning models are able to extract autonomously the underlying features within the dataset, providing a multi-level representation of the data. Our intention is to show the feasibility and effectiveness of using deep learning and low-cost technology for automated phenotyping. In this methods chapter, we describe how to train a deep neural network to segment leaf images and extract the pixels related to the disease.

F Danilevicz Monica, Bayer Philipp Emanuel

2022

Coffee leaf, Deep learning, Disease detection, High-throughput phenotyping, Phenotyping, Segmentation, Tensorflow

General General

Finding and Characterizing Repeats in Plant Genomes.

In Methods in molecular biology (Clifton, N.J.)

Plant genomes contain a particularly high proportion of repeated structures of various types. This chapter proposes a guided tour of the available software that can help biologists to scan automatically for these repeats in sequence data or check hypothetical models intended to characterize their structures. Since transposable elements (TEs) are a major source of repeats in plants, many methods have been used or developed for this broad class of sequences. They are representative of the range of tools available for other classes of repeats and we have provided two sections on this topic (for the analysis of genomes or directly of sequenced reads), as well as a selection of the main existing software. It may be hard to keep up with the profusion of proposals in this dynamic field and the rest of the chapter is devoted to the foundations of an efficient search for repeats and more complex patterns. We first introduce the key concepts of the art of indexing and mapping or querying sequences. We end the chapter with the more prospective issue of building models of repeat families. We present the Machine Learning approach first, seeking to build predictors automatically for some families of ET, from a set of sequences known to belong to this family. A second approach, the linguistic (or syntactic) approach, allows biologists to describe themselves and check the validity of models of their favorite repeat family.

Nicolas Jacques, Tempel Sébastien, Fiston-Lavier Anna-Sophie, Cherif Emira

2022

Algorithmics on words, Homology-based, Indexing, Machine Learning, Mapping, Pattern matching, Repeats, Structure-based methods, Transposon

Surgery Surgery

Fully automatic segmentation of craniomaxillofacial CT scans for computer-assisted orthognathic surgery planning using the nnU-Net framework.

In European radiology ; h5-index 62.0

OBJECTIVES : To evaluate the performance of the nnU-Net open-source deep learning framework for automatic multi-task segmentation of craniomaxillofacial (CMF) structures in CT scans obtained for computer-assisted orthognathic surgery.

METHODS : Four hundred and fifty-three consecutive patients having undergone high-resolution CT scans before orthognathic surgery were randomly distributed among a training/validation cohort (n = 300) and a testing cohort (n = 153). The ground truth segmentations were generated by 2 operators following an industry-certified procedure for use in computer-assisted surgical planning and personalized implant manufacturing. Model performance was assessed by comparing model predictions with ground truth segmentations. Examination of 45 CT scans by an industry expert provided additional evaluation. The model's generalizability was tested on a publicly available dataset of 10 CT scans with ground truth segmentation of the mandible.

RESULTS : In the test cohort, mean volumetric Dice similarity coefficient (vDSC) and surface Dice similarity coefficient at 1 mm (sDSC) were 0.96 and 0.97 for the upper skull, 0.94 and 0.98 for the mandible, 0.95 and 0.99 for the upper teeth, 0.94 and 0.99 for the lower teeth, and 0.82 and 0.98 for the mandibular canal. Industry expert segmentation approval rates were 93% for the mandible, 89% for the mandibular canal, 82% for the upper skull, 69% for the upper teeth, and 58% for the lower teeth.

CONCLUSION : While additional efforts are required for the segmentation of dental apices, our results demonstrated the model's reliability in terms of fully automatic segmentation of preoperative orthognathic CT scans.

KEY POINTS : • The nnU-Net deep learning framework can be trained out-of-the-box to provide robust fully automatic multi-task segmentation of CT scans performed for computer-assisted orthognathic surgery planning. • The clinical viability of the trained nnU-Net model is shown on a challenging test dataset of 153 CT scans randomly selected from clinical practice, showing metallic artifacts and diverse anatomical deformities. • Commonly used biomedical segmentation evaluation metrics (volumetric and surface Dice similarity coefficient) do not always match industry expert evaluation in the case of more demanding clinical applications.

Dot Gauthier, Schouman Thomas, Dubois Guillaume, Rouch Philippe, Gajny Laurent

2022-Jan-17

Deep learning, Orthognathic surgery, Surgery, computer-assisted, Tomography, x-ray computed

General General

GPS-Uber: a hybrid-learning framework for prediction of general and E3-specific lysine ubiquitination sites.

In Briefings in bioinformatics

As an important post-translational modification, lysine ubiquitination participates in numerous biological processes and is involved in human diseases, whereas the site specificity of ubiquitination is mainly decided by ubiquitin-protein ligases (E3s). Although numerous ubiquitination predictors have been developed, computational prediction of E3-specific ubiquitination sites is still a great challenge. Here, we carefully reviewed the existing tools for the prediction of general ubiquitination sites. Also, we developed a tool named GPS-Uber for the prediction of general and E3-specific ubiquitination sites. From the literature, we manually collected 1311 experimentally identified site-specific E3-substrate relations, which were classified into different clusters based on corresponding E3s at different levels. To predict general ubiquitination sites, we integrated 10 types of sequence and structure features, as well as three types of algorithms including penalized logistic regression, deep neural network and convolutional neural network. Compared with other existing tools, the general model in GPS-Uber exhibited a highly competitive accuracy, with an area under curve values of 0.7649. Then, transfer learning was adopted for each E3 cluster to construct E3-specific models, and in total 112 individual E3-specific predictors were implemented. Using GPS-Uber, we conducted a systematic prediction of human cancer-associated ubiquitination events, which could be helpful for further experimental consideration. GPS-Uber will be regularly updated, and its online service is free for academic research at http://gpsuber.biocuckoo.cn/.

Wang Chenwei, Tan Xiaodan, Tang Dachao, Gou Yujie, Han Cheng, Ning Wanshan, Lin Shaofeng, Zhang Weizhi, Chen Miaomiao, Peng Di, Xue Yu

2022-Jan-17

Post-translational modification, deep learning, lysine ubiquitination, site-specific E3-substrate relation, ubiquitin-protein ligase

Surgery Surgery

Unsupervised Machine Learning Approaches Reveal Distinct Phenotypes of Perceived Bladder Pain: A Pilot Study.

In Frontiers in pain research (Lausanne, Switzerland)

Interstitial cystitis/bladder pain syndrome (IC/BPS) is defined as an unpleasant sensation perceived to be related to the bladder with associated urinary symptoms. Due to difficulties discriminating pelvic visceral sensation, IC/BPS likely represents multiple phenotypes with different etiologies that present with overlapping symptomatic manifestations, which complicates clinical management. We hypothesized that unique bladder pain phenotypes or "symptomatic clusters" would be identifiable using machine learning analysis (unsupervised clustering) of validated patient-reported urinary and pain measures. Patients (n = 145) with pelvic pain/discomfort perceived to originate in the bladder and lower urinary tract symptoms answered validated questionnaires [OAB Questionnaire (OAB-q), O'Leary-Sant Indices (ICSI/ICPI), female Genitourinary Pain Index (fGUPI), and Pelvic Floor Disability Index (PFDI)]. In comparison to asymptomatic controls (n = 69), machine learning revealed three bladder pain phenotypes with unique, salient features. The first group chiefly describes urinary frequency and pain with the voiding cycle, in which bladder filling causes pain relieved by bladder emptying. The second group has fluctuating pelvic discomfort and straining to void, urinary frequency and urgency without incontinence, and a sensation of incomplete emptying without urinary retention. Pain in the third group was not associated with voiding, instead being more constant and focused on the urethra and vagina. While not utilized as a feature for clustering, subjects in the second and third groups were significantly younger than subjects in the first group and controls without pain. These phenotypes defined more homogeneous patient subgroups which responded to different therapies on chart review. Current approaches to the management of heterogenous populations of bladder pain patients are often ineffective, discouraging both patients and providers. The granularity of individual phenotypes provided by unsupervised clustering approaches can be exploited to help objectively define more homogeneous patient subgroups. Better differentiation of unique phenotypes within the larger group of pelvic pain patients is needed to move toward improvements in care and a better understanding of the etiologies of these painful symptoms.

Mwesigwa Patricia J, Jackson Nicholas J, Caron Ashley T, Kanji Falisha, Ackerman James E, Webb Jessica R, Scott Victoria C S, Eilber Karyn S, Underhill David M, Anger Jennifer T, Ackerman A Lenore

2021-Nov

bladder pain syndrome, interstitial cystitis, lower urinary tract symptoms, pelvic pain/discomfort, phenotypes, unsupervised machine learning, urinary symptoms

Pathology Pathology

Optimising predictive models to prioritise viral discovery in zoonotic reservoirs.

In The Lancet. Microbe

Despite the global investment in One Health disease surveillance, it remains difficult and costly to identify and monitor the wildlife reservoirs of novel zoonotic viruses. Statistical models can guide sampling target prioritisation, but the predictions from any given model might be highly uncertain; moreover, systematic model validation is rare, and the drivers of model performance are consequently under-documented. Here, we use the bat hosts of betacoronaviruses as a case study for the data-driven process of comparing and validating predictive models of probable reservoir hosts. In early 2020, we generated an ensemble of eight statistical models that predicted host-virus associations and developed priority sampling recommendations for potential bat reservoirs of betacoronaviruses and bridge hosts for SARS-CoV-2. During a time frame of more than a year, we tracked the discovery of 47 new bat hosts of betacoronaviruses, validated the initial predictions, and dynamically updated our analytical pipeline. We found that ecological trait-based models performed well at predicting these novel hosts, whereas network methods consistently performed approximately as well or worse than expected at random. These findings illustrate the importance of ensemble modelling as a buffer against mixed-model quality and highlight the value of including host ecology in predictive models. Our revised models showed an improved performance compared with the initial ensemble, and predicted more than 400 bat species globally that could be undetected betacoronavirus hosts. We show, through systematic validation, that machine learning models can help to optimise wildlife sampling for undiscovered viruses and illustrates how such approaches are best implemented through a dynamic process of prediction, data collection, validation, and updating.

Becker Daniel J, Albery Gregory F, Sjodin Anna R, Poisot Timothée, Bergner Laura M, Chen Binqi, Cohen Lily E, Dallas Tad A, Eskew Evan A, Fagre Anna C, Farrell Maxwell J, Guth Sarah, Han Barbara A, Simmons Nancy B, Stock Michiel, Teeling Emma C, Carlson Colin J

2022-Jan-10

oncology Oncology

[Moving towards a personalized oncology: The contribution of genomic techniques and artificial intelligence in the use of circulating tumor biomarkers].

In Bulletin du cancer

Technological advances, in particular the development of high-throughput sequencing, have led to the emergence of a new generation of molecular biomarkers for tumors. These new tools have profoundly changed therapeutic management in oncology, with increasingly precise molecular characterization of tumors leading to increasingly personalized therapeutic targeting. Detection of circulating tumor cells and/or circulating tumor DNA in blood samples -so-called 'liquid biopsies'- can now provide a genetic snapshot of the patient's tumor through an alternative and less invasive procedure than biopsy of the tumor tissue itself. This procedure for characterizing and monitoring the disease in real time facilitates the search for possible relapses, the emergence of resistance, or emergence of a new therapeutic target. In the long term, it might also provide a means of early detection of cancer. These new approaches require the treatment of ever-increasing amounts of clinical data, notably, with the goal of calculating composite clinical-biological predictive scores. The use of artificial intelligence will be unavoidable in this domain, but it raises ethical questions and implications for the health-care system that will have to be addressed.

Perrier Alexandre, Hainaut Pierre, Guenoun Alexandre, Nguyen Dinh-Phong, Lamy Pierre-Jean, Guerber Fabrice, Troalen Frédéric, Denis Jérôme Alexandre, Boissan Mathieu

2022-Jan-13

Artificial intelligence, Biopsie liquide, Clinicobiological scores, Intelligence artificielle, Liquid biopsy, Marqueurs tumoraux, Scores clinico-biologiques, Theranostic value, Tumor markers, Valeur théranostique

General General

Classification of Covid-19 chest X-ray images by means of an interpretable evolutionary rule-based approach.

In Neural computing & applications

In medical practice, all decisions, as for example the diagnosis based on the classification of images, must be made reliably and effectively. The possibility of having automatic tools helping doctors in performing these important decisions is highly welcome. Artificial Intelligence techniques, and in particular Deep Learning methods, have proven very effective on these tasks, with excellent performance in terms of classification accuracy. The problem with such methods is that they represent black boxes, so they do not provide users with an explanation of the reasons for their decisions. Confidence from medical experts in clinical decisions can increase if they receive from Artificial Intelligence tools interpretable output under the form of, e.g., explanations in natural language or visualized information. This way, the system outcome can be critically assessed by them, and they can evaluate the trustworthiness of the results. In this paper, we propose a new general-purpose method that relies on interpretability ideas. The approach is based on two successive steps, the former being a filtering scheme typically used in Content-Based Image Retrieval, whereas the latter is an evolutionary algorithm able to classify and, at the same time, automatically extract explicit knowledge under the form of a set of IF-THEN rules. This approach is tested on a set of chest X-ray images aiming at assessing the presence of COVID-19.

De Falco Ivanoe, De Pietro Giuseppe, Sannino Giovanna

2022-Jan-08

Chest X-ray images, Classification, Covid-19 disease, Evolutionary algorithms, Interpretable machine learning

General General

Cybersecurity, Data Privacy and Blockchain: A Review.

In SN computer science

In this paper, we identify and review key challenges to bridge the knowledge-gap between SME's, companies, organisations, businesses, government institutions and the general public in adopting, promoting and utilising Blockchain technology. The challenges indicated are Cybersecurity and Data privacy in this instance. Additional challenges are set out supported by literature, in researching data security management systems and legal frameworks to ascertaining the types and varieties of valid encryption, data acquisition, policy and outcomes under ISO 27001 and the General Data Protection Regulations. Blockchain, a revolutionary method of storage and immutability, provides a robust storage strategy, and when coupled with a Smart Contract, gives users the ability to form partnerships, share information and consent via a legally-based system of carrying out business transactions in a secure digital domain. Globally, ethical and legal challenges significantly differ; consent and trust in the public and private sectors in deploying such defensive data management strategies, is directly related to the accountability and transparency systems in place to deliver certainty and justice. Therefore, investment and research in these areas is crucial to establishing a dialogue between nations to include health, finance and market strategies that should encompass all levels of society. A framework is proposed with elements to include Big Data, Machine Learning and Visualisation methods and techniques. Through the literature we identify a system necessary in carrying out experiments to detect, capture, process and store data. This includes isolating packet data to inform levels of Cybersecurity and privacy-related activities, and ensuring transparency demonstrated in a secure, smart and effective manner.

Wylde Vinden, Rawindaran Nisha, Lawrence John, Balasubramanian Rushil, Prakash Edmond, Jayal Ambikesh, Khan Imtiaz, Hewage Chaminda, Platts Jon

2022

Blockchain, Cybersecurity, Data privacy, GDPR, ISO 27001, IoT, Smart Contracts

General General

eHooke: A tool for automated image analysis of spherical bacteria based on cell cycle progression.

In Biological imaging

Fluorescence microscopy is a critical tool for cell biology studies on bacterial cell division and morphogenesis. Because the analysis of fluorescence microscopy images evolved beyond initial qualitative studies, numerous images analysis tools were developed to extract quantitative parameters on cell morphology and organization. To understand cellular processes required for bacterial growth and division, it is particularly important to perform such analysis in the context of cell cycle progression. However, manual assignment of cell cycle stages is laborious and prone to user bias. Although cell elongation can be used as a proxy for cell cycle progression in rod-shaped or ovoid bacteria, that is not the case for cocci, such as Staphylococcus aureus. Here, we describe eHooke, an image analysis framework developed specifically for automated analysis of microscopy images of spherical bacterial cells. eHooke contains a trained artificial neural network to automatically classify the cell cycle phase of individual S. aureus cells. Users can then apply various functions to obtain biologically relevant information on morphological features of individual cells and cellular localization of proteins, in the context of the cell cycle.

Saraiva Bruno M, Krippahl Ludwig, Filipe Sérgio R, Henriques Ricardo, Pinho Mariana G

2021

Automated image analysis, Staphylococcus aureus, bacterial cell cycle, deep learning, fluorescence microscopy

General General

Automated detection and staging of malaria parasites from cytological smears using convolutional neural networks.

In Biological imaging

Microscopic examination of blood smears remains the gold standard for laboratory inspection and diagnosis of malaria. Smear inspection is, however, time-consuming and dependent on trained microscopists with results varying in accuracy. We sought to develop an automated image analysis method to improve accuracy and standardization of smear inspection that retains capacity for expert confirmation and image archiving. Here, we present a machine learning method that achieves red blood cell (RBC) detection, differentiation between infected/uninfected cells, and parasite life stage categorization from unprocessed, heterogeneous smear images. Based on a pretrained Faster Region-Based Convolutional Neural Networks (R-CNN) model for RBC detection, our model performs accurately, with an average precision of 0.99 at an intersection-over-union threshold of 0.5. Application of a residual neural network-50 model to infected cells also performs accurately, with an area under the receiver operating characteristic curve of 0.98. Finally, combining our method with a regression model successfully recapitulates intraerythrocytic developmental cycle with accurate lifecycle stage categorization. Combined with a mobile-friendly web-based interface, called PlasmoCount, our method permits rapid navigation through and review of results for quality assurance. By standardizing assessment of Giemsa smears, our method markedly improves inspection reproducibility and presents a realistic route to both routine lab and future field-based automated malaria diagnosis.

Davidson Mira S, Andradi-Brown Clare, Yahiya Sabrina, Chmielewski Jill, O’Donnell Aidan J, Gurung Pratima, Jeninga Myriam D, Prommana Parichat, Andrew Dean W, Petter Michaela, Uthaipibull Chairat, Boyle Michelle J, Ashdown George W, Dvorin Jeffrey D, Reece Sarah E, Wilson Danny W, Cunningham Kane A, Ando D Michael, Dimon Michelle, Baum Jake

2021

Artificial intelligence, Giemsa stain, Plasmodium falciparum, gametocytes, residual neural networks (ResNets)

Surgery Surgery

'All In One' SARS-CoV-2 variant recognition platform: Machine learning-enabled point of care diagnostics.

In Biosensors & bioelectronics: X

Point of care (PoC) devices are highly demanding to control current pandemic, originated from severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). Though nucleic acid-based methods such as RT-PCR are widely available, they require sample preparation and long processing time. PoC diagnostic devices provide relatively faster and stable results. However they require further investigation to provide high accuracy and be adaptable for the new variants. In this study, laser-scribed graphene (LSG) sensors are coupled with gold nanoparticles (AuNPs) as stable promising biosensing platforms. Angiotensin Converting Enzyme 2 (ACE2), an enzymatic receptor, is chosen to be the biorecognition unit due to its high binding affinity towards spike proteins as a key-lock model. The sensor was integrated to a homemade and portable potentistat device, wirelessly connected to a smartphone having a customized application for easy operation. LODs of 5.14 and 2.09 ng/mL was achieved for S1 and S2 protein in the linear range of 1.0-200 ng/mL, respectively. Clinical study has been conducted with nasopharyngeal swabs from 63 patients having alpha (B.1.1.7), beta (B.1.351), delta (B.1.617.2) variants, patients without mutation and negative patients. A machine learning model was developed with accuracy of 99.37% for the identification of the SARS-Cov-2 variants under 1 min. With the increasing need for rapid and improved disease diagnosis and monitoring, the PoC platform proved its potential for real time monitoring by providing accurate and fast variant identification without any expertise and pre sample preparation, which is exactly what societies need in this time of pandemic.

Beduk Duygu, Ilton de Oliveira Filho José, Beduk Tutku, Harmanci Duygu, Zihnioglu Figen, Cicek Candan, Sertoz Ruchan, Arda Bilgin, Goksel Tuncay, Turhan Kutsal, Salama Khaled Nabil, Timur Suna

2022-May

COVID-19, Laser-scribed graphene, Machine learning, Point-of-care, SARS-CoV-2, Sensor

Surgery Surgery

Cardiovascular mechanisms underlying vocal behavior in freely moving macaque monkeys.

In iScience

Communication is a keystone of animal behavior. However, the physiological states underlying natural vocal signaling are still largely unknown. In this study, we investigated the correlation of affective vocal utterances with concomitant cardiorespiratory mechanisms. We telemetrically recorded electrocardiography, blood pressure, and physical activity in six freely moving and interacting cynomolgus monkeys (Macaca fascicularis). Our results demonstrate that vocal onsets are strengthened during states of sympathetic activation, and are phase locked to a slower Mayer wave and a faster heart rate signal at ∼2.5 Hz. Vocalizations are coupled with a distinct peri-vocal physiological signature based on which we were able to predict the onset of vocal output using three machine learning classification models. These findings emphasize the role of cardiorespiratory mechanisms correlated with vocal onsets to optimize arousal levels and minimize energy expenditure during natural vocal production.

Risueno-Segovia Cristina, Koç Okan, Champéroux Pascal, Hage Steffen R

2022-Jan-21

Behavioral neuroscience, Biological sciences, Cardiovascular medicine, Ethology

General General

Deep learning model of somatic hypermutation reveals importance of sequence context beyond hotspot targeting.

In iScience

B cells undergo somatic hypermutation (SHM) of the Immunoglobulin (Ig) variable region to generate high-affinity antibodies. SHM relies on the activity of activation-induced deaminase (AID), which mutates C>U preferentially targeting WRC (W=A/T, R=A/G) hotspots. Downstream mutations at WA Polymerase η hotspots contribute further mutations. Computational models of SHM can describe the probability of mutations essential for vaccine responses. Previous studies using short subsequences (k-mers) failed to explain divergent mutability for the same k-mer. We developed the DeepSHM (Deep learning on SHM) model using k-mers of size 5-21, improving accuracy over previous models. Interpretation of DeepSHM identified an extended WWRCT motif with particularly high mutability. Increased mutability was further associated with lower surrounding G content. Our model also discovered a conserved AGYCTGGGGG (Y=C/T) motif within FW1 of IGHV3 family genes with unusually high T>G substitution rates. Thus, a wider sequence context increases predictive power and identifies features that drive mutational targeting.

Tang Catherine, Krantsevich Artem, MacCarthy Thomas

2022-Jan-21

Biological sciences, Computational bioinformatics, Immunology

General General

CX-ToM: Counterfactual explanations with theory-of-mind for enhancing human trust in image recognition models.

In iScience

We propose CX-ToM, short for counterfactual explanations with theory-of-mind, a new explainable AI (XAI) framework for explaining decisions made by a deep convolutional neural network (CNN). In contrast to the current methods in XAI that generate explanations as a single shot response, we pose explanation as an iterative communication process, i.e., dialogue between the machine and human user. More concretely, our CX-ToM framework generates a sequence of explanations in a dialogue by mediating the differences between the minds of the machine and human user. To do this, we use Theory of Mind (ToM) which helps us in explicitly modeling the human's intention, the machine's mind as inferred by the human, as well as human's mind as inferred by the machine. Moreover, most state-of-the-art XAI frameworks provide attention (or heat map) based explanations. In our work, we show that these attention-based explanations are not sufficient for increasing human trust in the underlying CNN model. In CX-ToM, we instead use counterfactual explanations called fault-lines which we define as follows: given an input image I for which a CNN classification model M predicts class c pred , a fault-line identifies the minimal semantic-level features (e.g., stripes on zebra), referred to as explainable concepts, that need to be added to or deleted from I to alter the classification category of I by M to another specified class c alt . Extensive experiments verify our hypotheses, demonstrating that our CX-ToM significantly outperforms the state-of-the-art XAI models.

Akula Arjun R, Wang Keze, Liu Changsong, Saba-Sadiya Sari, Lu Hongjing, Todorovic Sinisa, Chai Joyce, Zhu Song-Chun

2022-Jan-21

Artificial intelligence, Computer science, Human-computer interaction

General General

Hybrid PSO-SVM algorithm for Covid-19 screening and quantification.

In International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management

Corona Virus Disease (COVID) 19 has shaken the earth at its root and the devastation has increased the diagnostic burden of radiologists by large. At this crucial juncture, Artificial Intelligence (AI) will go a long way in decreasing the workload of physicians working in the outbreak zone, aiding them to accurately diagnose the new disease. In this work, a hybrid Particle Swarm Optimization-Support Vector Machine based AI algorithm is deployed to analyze the Computed Tomography images automatically providing a high probability in determining the presence of pneumonia due to COVID19. This paper presents a model for training the sy