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Category articles

General General

Story Arcs in Serious Illness: Natural Language Processing features of Palliative Care Conversations.

In Patient education and counseling ; h5-index 0.0

OBJECTIVE : Serious illness conversations are complex clinical narratives that remain poorly understood. Natural Language Processing (NLP) offers new approaches for identifying hidden patterns within the lexicon of stories that may reveal insights about the taxonomy of serious illness conversations.

METHODS : We analyzed verbatim transcripts from 354 consultations involving 231 patients and 45 palliative care clinicians from the Palliative Care Communication Research Initiative. We stratified each conversation into deciles of "narrative time" based on word counts. We used standard NLP analyses to examine the frequency and distribution of words and phrases indicating temporal reference, illness terminology, sentiment and modal verbs (indicating possibility/desirability).

RESULTS : Temporal references shifted steadily from talking about the past to talking about the future over deciles of narrative time. Conversations progressed incrementally from "sadder" to "happier" lexicon; reduction in illness terminology accounted substantially for this pattern. We observed the following sequence in peak frequency over narrative time: symptom terms, treatment terms, prognosis terms and modal verbs indicating possibility.

CONCLUSIONS : NLP methods can identify narrative arcs in serious illness conversations.

PRACTICE IMPLICATIONS : Fully automating NLP methods will allow for efficient, large scale and real time measurement of serious illness conversations for research, education and system re-design.

Ross Lindsay, Danforth Christopher M, Eppstein Margaret J, Clarfeld Laurence A, Durieux Brigitte N, Gramling Cailin J, Hirsch Laura, Rizzo Donna M, Gramling Robert

2019-Dec-07

Artificial Intelligence, Communication, Conversation, Machine Learning, Narrative Analysis, Natural Language Processing, Palliative Care, Stories

General General

Computational modeling of the monoaminergic neurotransmitter and male neuroendocrine systems in an analysis of therapeutic neuroadaptation to chronic antidepressant.

In European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology ; h5-index 0.0

Second-line depression treatment involves augmentation with one (rarely two) additional drugs, of chronic administration of a selective serotonin reuptake inhibitor (SSRI), which is the first-line depression treatment. Unfortunately, many depressed patients still fail to respond even after months to years of searching to find an effective combination. To aid in the identification of potentially effective antidepressant combinations, we created a computational model of the monoaminergic neurotransmitter (serotonin, norepinephrine, and dopamine), stress-hormone (cortisol), and male sex hormone (testosterone) systems. The model was trained via machine learning to represent a broad range of empirical observations. Neuroadaptation to chronic drug administration was simulated through incremental adjustments in model parameters that corresponded to key regulatory components of the neurotransmitter and neurohormone systems. Analysis revealed that neuroadaptation in the model depended on all of the regulatory components in complicated ways, and did not reveal any one or a few specific components that could be targeted in the design of antidepressant treatments. We used large sets of neuroadapted states of the model to screen 74 different drug and hormone combinations and identified several combinations that could potentially be therapeutic for a higher proportion of male patients than SSRIs by themselves.

Camacho Mariam Bonyadi, Vijitbenjaronk Warut D, Anastasio Thomas J

2019-Dec-09

Depression, Drug discovery, Gonadal hormones, Machine learning, Neuropharmacology, Systems biology

General General

Towards the automation of early-stage human embryo development detection.

In Biomedical engineering online ; h5-index 0.0

BACKGROUND : Infertility and subfertility affect a significant proportion of humanity. Assisted reproductive technology has been proven capable of alleviating infertility issues. In vitro fertilisation is one such option whose success is highly dependent on the selection of a high-quality embryo for transfer. This is typically done manually by analysing embryos under a microscope. However, evidence has shown that the success rate of manual selection remains low. The use of new incubators with integrated time-lapse imaging system is providing new possibilities for embryo assessment. As such, we address this problem by proposing an approach based on deep learning for automated embryo quality evaluation through the analysis of time-lapse images. Automatic embryo detection is complicated by the topological changes of a tracked object. Moreover, the algorithm should process a large number of image files of different qualities in a reasonable amount of time.

METHODS : We propose an automated approach to detect human embryo development stages during incubation and to highlight embryos with abnormal behaviour by focusing on five different stages. This method encompasses two major steps. First, the location of an embryo in the image is detected by employing a Haar feature-based cascade classifier and leveraging the radiating lines. Then, a multi-class prediction model is developed to identify a total cell number in the embryo using the technique of deep learning.

RESULTS : The experimental results demonstrate that the proposed method achieves an accuracy of at least 90% in the detection of embryo location. The implemented deep learning approach to identify the early stages of embryo development resulted in an overall accuracy of over 92% using the selected architectures of convolutional neural networks. The most problematic stage was the 3-cell stage, presumably due to its short duration during development.

CONCLUSION : This research contributes to the field by proposing a model to automate the monitoring of early-stage human embryo development. Unlike in other imaging fields, only a few published attempts have involved leveraging deep learning in this field. Therefore, the approach presented in this study could be used in the creation of novel algorithms integrated into the assisted reproductive technology used by embryologists.

Raudonis Vidas, Paulauskaite-Taraseviciene Agne, Sutiene Kristina, Jonaitis Domas

2019-Dec-12

Deep learning, Embryo development, Image recognition, Location detection, Multi-class prediction

General General

A low-cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson's Disease.

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

BACKGROUND : Assessment and rating of Parkinson's Disease (PD) are commonly based on the medical observation of several clinical manifestations, including the analysis of motor activities. In particular, medical specialists refer to the MDS-UPDRS (Movement Disorder Society - sponsored revision of Unified Parkinson's Disease Rating Scale) that is the most widely used clinical scale for PD rating. However, clinical scales rely on the observation of some subtle motor phenomena that are either difficult to capture with human eyes or could be misclassified. This limitation motivated several researchers to develop intelligent systems based on machine learning algorithms able to automatically recognize the PD. Nevertheless, most of the previous studies investigated the classification between healthy subjects and PD patients without considering the automatic rating of different levels of severity.

METHODS : In this context, we implemented a simple and low-cost clinical tool that can extract postural and kinematic features with the Microsoft Kinect v2 sensor in order to classify and rate PD. Thirty participants were enrolled for the purpose of the present study: sixteen PD patients rated according to MDS-UPDRS and fourteen healthy paired subjects. In order to investigate the motor abilities of the upper and lower body, we acquired and analyzed three main motor tasks: (1) gait, (2) finger tapping, and (3) foot tapping. After preliminary feature selection, different classifiers based on Support Vector Machine (SVM) and Artificial Neural Networks (ANN) were trained and evaluated for the best solution.

RESULTS : Concerning the gait analysis, results showed that the ANN classifier performed the best by reaching 89.4% of accuracy with only nine features in diagnosis PD and 95.0% of accuracy with only six features in rating PD severity. Regarding the finger and foot tapping analysis, results showed that an SVM using the extracted features was able to classify healthy subjects versus PD patients with great performances by reaching 87.1% of accuracy. The results of the classification between mild and moderate PD patients indicated that the foot tapping features were the most representative ones to discriminate (81.0% of accuracy).

CONCLUSIONS : The results of this study have shown how a low-cost vision-based system can automatically detect subtle phenomena featuring the PD. Our findings suggest that the proposed tool can support medical specialists in the assessment and rating of PD patients in a real clinical scenario.

Buongiorno Domenico, Bortone Ilaria, Cascarano Giacomo Donato, Trotta Gianpaolo Francesco, Brunetti Antonio, Bevilacqua Vitoantonio

2019-Dec-12

Artificial neural network, Classification, Feature selection, Finger tapping, Foot tapping, Gait analysis, MDS-UPDRS, Microsoft kinect v2, Parkinson’s disease, Support vector machine

General General

Implementation of machine learning algorithms to create diabetic patient re-admission profiles.

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

BACKGROUND : Machine learning is a branch of Artificial Intelligence that is concerned with the design and development of algorithms, and it enables today's computers to have the property of learning. Machine learning is gradually growing and becoming a critical approach in many domains such as health, education, and business.

METHODS : In this paper, we applied machine learning to the diabetes dataset with the aim of recognizing patterns and combinations of factors that characterizes or explain re-admission among diabetes patients. The classifiers used include Linear Discriminant Analysis, Random Forest, k-Nearest Neighbor, Naïve Bayes, J48 and Support vector machine.

RESULTS : Of the 100,000 cases, 78,363 were diabetic and over 47% were readmitted.Based on the classes that models produced, diabetic patients who are more likely to be readmitted are either women, or Caucasians, or outpatients, or those who undergo less rigorous lab procedures, treatment procedures, or those who receive less medication, and are thus discharged without proper improvements or administration of insulin despite having been tested positive for HbA1c.

CONCLUSION : Diabetic patients who do not undergo vigorous lab assessments, diagnosis, medications are more likely to be readmitted when discharged without improvements and without receiving insulin administration, especially if they are women, Caucasians, or both.

Alloghani Mohamed, Aljaaf Ahmed, Hussain Abir, Baker Thar, Mustafina Jamila, Al-Jumeily Dhiya, Khalaf Mohammed

2019-Dec-12

Algorithms, Diabetes re-admission, HbA1c, Linear discriminant, Machine learning, Support vector machine

General General

A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images.

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

BACKGROUND : The automatic segmentation of kidneys in medical images is not a trivial task when the subjects undergoing the medical examination are affected by Autosomal Dominant Polycystic Kidney Disease (ADPKD). Several works dealing with the segmentation of Computed Tomography images from pathological subjects were proposed, showing high invasiveness of the examination or requiring interaction by the user for performing the segmentation of the images. In this work, we propose a fully-automated approach for the segmentation of Magnetic Resonance images, both reducing the invasiveness of the acquisition device and not requiring any interaction by the users for the segmentation of the images.

METHODS : Two different approaches are proposed based on Deep Learning architectures using Convolutional Neural Networks (CNN) for the semantic segmentation of images, without needing to extract any hand-crafted features. In details, the first approach performs the automatic segmentation of images without any procedure for pre-processing the input. Conversely, the second approach performs a two-steps classification strategy: a first CNN automatically detects Regions Of Interest (ROIs); a subsequent classifier performs the semantic segmentation on the ROIs previously extracted.

RESULTS : Results show that even though the detection of ROIs shows an overall high number of false positives, the subsequent semantic segmentation on the extracted ROIs allows achieving high performance in terms of mean Accuracy. However, the segmentation of the entire images input to the network remains the most accurate and reliable approach showing better performance than the previous approach.

CONCLUSION : The obtained results show that both the investigated approaches are reliable for the semantic segmentation of polycystic kidneys since both the strategies reach an Accuracy higher than 85%. Also, both the investigated methodologies show performances comparable and consistent with other approaches found in literature working on images from different sources, reducing both the invasiveness of the analyses and the interaction needed by the users for performing the segmentation task.

Bevilacqua Vitoantonio, Brunetti Antonio, Cascarano Giacomo Donato, Guerriero Andrea, Pesce Francesco, Moschetta Marco, Gesualdo Loreto

2019-Dec-12

ADPKD, Convolutional neural network, Deep learning, Magnetic resonance, R-CNN, Semantic segmentation

General General

Learning physical properties in complex visual scenes: An intelligent machine for perceiving blood flow dynamics from static CT angiography imaging.

In Neural networks : the official journal of the International Neural Network Society ; h5-index 0.0

Humans perceive physical properties such as motion and elastic force by observing objects in visual scenes. Recent research has proven that computers are capable of inferring physical properties from camera images like humans. However, few studies perceive the physical properties in more complex environment, i.e. humans have difficulty estimating physical quantities directly from the visual observation, or encounter difficulty visualizing the physical process in mind according to their daily experiences. As an appropriate example, fractional flow reserve (FFR), which measures the blood pressure difference across the vessel stenosis, becomes an important physical quantitative value determining the likelihood of myocardial ischemia in clinical coronary intervention procedure. In this study, we propose a novel deep neural network solution (TreeVes-Net) that allows machines to perceive FFR values directly from static coronary CT angiography images. Our framework fully utilizes a tree-structured recurrent neural network (RNN) with a coronary representation encoder. The encoder captures coronary geometric information providing the blood fluid-related representation. The tree-structured RNN builds a long-distance spatial dependency of blood flow information inside the coronary tree. The experiments performed on 13000 synthetic coronary trees and 180 real coronary trees from clinical patients show that the values of the area under ROC curve (AUC) are 0.92 and 0.93 under two clinical criterions. These results can demonstrate the effectiveness of our framework and its superiority to seven FFR computation methods based on machine learning.

Gao Zhifan, Wang Xin, Sun Shanhui, Wu Dan, Bai Junjie, Yin Youbing, Liu Xin, Zhang Heye, de Albuquerque Victor Hugo C

2019-Nov-30

CT angiography, Fractional flow reserve, LSTM, Learning physical properties, Tree-structured RNN

General General

Deep Learning Reveals Cancer Metastasis and Therapeutic Antibody Targeting in the Entire Body.

In Cell ; h5-index 250.0

Reliable detection of disseminated tumor cells and of the biodistribution of tumor-targeting therapeutic antibodies within the entire body has long been needed to better understand and treat cancer metastasis. Here, we developed an integrated pipeline for automated quantification of cancer metastases and therapeutic antibody targeting, named DeepMACT. First, we enhanced the fluorescent signal of cancer cells more than 100-fold by applying the vDISCO method to image metastasis in transparent mice. Second, we developed deep learning algorithms for automated quantification of metastases with an accuracy matching human expert manual annotation. Deep learning-based quantification in 5 different metastatic cancer models including breast, lung, and pancreatic cancer with distinct organotropisms allowed us to systematically analyze features such as size, shape, spatial distribution, and the degree to which metastases are targeted by a therapeutic monoclonal antibody in entire mice. DeepMACT can thus considerably improve the discovery of effective antibody-based therapeutics at the pre-clinical stage. VIDEO ABSTRACT.

Pan Chenchen, Schoppe Oliver, Parra-Damas Arnaldo, Cai Ruiyao, Todorov Mihail Ivilinov, Gondi Gabor, von Neubeck Bettina, Böğürcü-Seidel Nuray, Seidel Sascha, Sleiman Katia, Veltkamp Christian, Förstera Benjamin, Mai Hongcheng, Rong Zhouyi, Trompak Omelyan, Ghasemigharagoz Alireza, Reimer Madita Alice, Cuesta Angel M, Coronel Javier, Jeremias Irmela, Saur Dieter, Acker-Palmer Amparo, Acker Till, Garvalov Boyan K, Menze Bjoern, Zeidler Reinhard, Ertürk Ali

2019-Dec-12

antibody, cancer, deep learning, drug targeting, imaging, light-sheet, metastasis, microscopy, tissue clearing, vDISCO

General General

Improving Generalization via Attribute Selection on Out-of-the-Box Data.

In Neural computation ; h5-index 0.0

Zero-shot learning (ZSL) aims to recognize unseen objects (test classes) given some other seen objects (training classes) by sharing information of attributes between different objects. Attributes are artificially annotated for objects and treated equally in recent ZSL tasks. However, some inferior attributes with poor predictability or poor discriminability may have negative impacts on the ZSL system performance. This letter first derives a generalization error bound for ZSL tasks. Our theoretical analysis verifies that selecting the subset of key attributes can improve the generalization performance of the original ZSL model, which uses all the attributes. Unfortunately, previous attribute selection methods have been conducted based on the seen data, and their selected attributes have poor generalization capability to the unseen data, which is unavailable in the training stage of ZSL tasks. Inspired by learning from pseudo-relevance feedback, this letter introduces out-of-the-box data-pseudo-data generated by an attribute-guided generative model-to mimic the unseen data. We then present an iterative attribute selection (IAS) strategy that iteratively selects key attributes based on the out-of-the-box data. Since the distribution of the generated out-of-the-box data is similar to that of the test data, the key attributes selected by IAS can be effectively generalized to test data. Extensive experiments demonstrate that IAS can significantly improve existing attribute-based ZSL methods and achieve state-of-the-art performance.

Xu Xiaofeng, Tsang Ivor W, Liu Chuancai

2019-Dec-13

General General

Transition Scale-Spaces: A Computational Theory for the Discretized Entorhinal Cortex.

In Neural computation ; h5-index 0.0

Although hippocampal grid cells are thought to be crucial for spatial navigation, their computational purpose remains disputed. Recently, they were proposed to represent spatial transitions and convey this knowledge downstream to place cells. However, a single scale of transitions is insufficient to plan long goal-directed sequences in behaviorally acceptable time. Here, a scale-space data structure is suggested to optimally accelerate retrievals from transition systems, called transition scale-space (TSS). Remaining exclusively on an algorithmic level, the scale increment is proved to be ideally 2 for biologically plausible receptive fields. It is then argued that temporal buffering is necessary to learn the scale-space online. Next, two modes for retrieval of sequences from the TSS are presented: top down and bottom up. The two modes are evaluated in symbolic simulations (i.e., without biologically plausible spiking neurons). Additionally, a TSS is used for short-cut discovery in a simulated Morris water maze. Finally, the results are discussed in depth with respect to biological plausibility, and several testable predictions are derived. Moreover, relations to other grid cell models, multiresolution path planning, and scale-space theory are highlighted. Summarized, reward-free transition encoding is shown here, in a theoretical model, to be compatible with the observed discretization along the dorso-ventral axis of the medial entorhinal cortex. Because the theoretical model generalizes beyond navigation, the TSS is suggested to be a general-purpose cortical data structure for fast retrieval of sequences and relational knowledge. Source code for all simulations presented in this paper can be found at https://github.com/rochus/transitionscalespace.

Waniek Nicolai

2019-Dec-13

General General

EEG spectral power, but not theta/beta ratio, is a neuromarker for adult ADHD.

In The European journal of neuroscience ; h5-index 0.0

Adults with attention-deficit/hyperactivity disorder (ADHD) have been described as having altered resting-state electroencephalographic (EEG) spectral power and theta/beta ratio (TBR). However, a recent review (Pulini et al. 2018) identified methodological errors in neuroimaging, including EEG, ADHD classification studies. Therefore, the specific EEG neuromarkers of adult ADHD remain to be identified, as do the EEG characteristics that mediate between genes and behavior (mediational endophenotypes). Resting-state eyes-open and eyes-closed EEG were measured from 38 adults with ADHD, 45 first-degree relatives of people with ADHD and 51 unrelated controls. A machine learning classification analysis using penalized logistic regression (Elastic Net) examined if EEG spectral power (1-45 Hz) and TBR could classify participants into ADHD, first-degree relatives and/or control groups. Random-label permutation was used to quantify any bias in the analysis. Eyes-open absolute and relative EEG power distinguished ADHD from control participants (area under receiver operating characteristic = .71-.77). The best predictors of ADHD status were increased power in delta, theta and low-alpha over centro-parietal regions, and in frontal low-beta and parietal mid-beta. TBR did not successfully classify ADHD status. Elevated eyes-open power in delta, theta, low-alpha and low-beta distinguished first-degree relatives from controls (area under receiver operating characteristic = .68-.72), suggesting that these features may be a mediational endophenotype for adult ADHD. Resting-state EEG spectral power may be a neuromarker and mediational endophenotype of adult ADHD. These results did not support TBR as a diagnostic neuromarker for ADHD. It is possible that TBR is a characteristic of childhood ADHD.

Kiiski Hanni, Bennett Marc, Rueda-Delgado Laura M, Farina Francesca, Knight Rachel, Boyle Rory, Roddy Darren, Grogan Katie, Bramham Jessica, Kelly Clare, Whelan Robert

2019-Dec-13

Adults, Attention-Deficit/Hyperactivity Disorder, Endophenotype, Machine learning, Resting-state EEG

General General

A validation of machine learning-based risk scores in the prehospital setting.

In PloS one ; h5-index 176.0

BACKGROUND : The triage of patients in prehospital care is a difficult task, and improved risk assessment tools are needed both at the dispatch center and on the ambulance to differentiate between low- and high-risk patients. This study validates a machine learning-based approach to generating risk scores based on hospital outcomes using routinely collected prehospital data.

METHODS : Dispatch, ambulance, and hospital data were collected in one Swedish region from 2016-2017. Dispatch center and ambulance records were used to develop gradient boosting models predicting hospital admission, critical care (defined as admission to an intensive care unit or in-hospital mortality), and two-day mortality. Composite risk scores were generated based on the models and compared to National Early Warning Scores (NEWS) and actual dispatched priorities in a prospectively gathered dataset from 2018.

RESULTS : A total of 38203 patients were included from 2016-2018. Concordance indexes (or areas under the receiver operating characteristics curve) for dispatched priorities ranged from 0.51-0.66, while those for NEWS ranged from 0.66-0.85. Concordance ranged from 0.70-0.79 for risk scores based only on dispatch data, and 0.79-0.89 for risk scores including ambulance data. Dispatch data-based risk scores consistently outperformed dispatched priorities in predicting hospital outcomes, while models including ambulance data also consistently outperformed NEWS. Model performance in the prospective test dataset was similar to that found using cross-validation, and calibration was comparable to that of NEWS.

CONCLUSIONS : Machine learning-based risk scores outperformed a widely-used rule-based triage algorithm and human prioritization decisions in predicting hospital outcomes. Performance was robust in a prospectively gathered dataset, and scores demonstrated adequate calibration. Future research should explore the robustness of these methods when applied to other settings, establish appropriate outcome measures for use in determining the need for prehospital care, and investigate the clinical impact of interventions based on these methods.

Spangler Douglas, Hermansson Thomas, Smekal David, Blomberg Hans

2019

General General

An evaluation of different classification algorithms for protein sequence-based reverse vaccinology prediction.

In PloS one ; h5-index 176.0

Previous work has shown that proteins that have the potential to be vaccine candidates can be predicted from features derived from their amino acid sequences. In this work, we make an empirical comparison across various machine learning classifiers on this sequence-based inference problem. Using systematic cross validation on a dataset of 200 known vaccine candidates and 200 negative examples, with a set of 525 features derived from the AA sequences and feature selection applied through a greedy backward elimination approach, we show that simple classification algorithms often perform as well as more complex support vector kernel machines. The work also includes a novel cross validation applied across bacterial species, i.e. the validation proteins all come from a specific species of bacterium not represented in the training set. We termed this type of validation Leave One Bacteria Out Validation (LOBOV).

Heinson Ashley I, Ewing Rob M, Holloway John W, Woelk Christopher H, Niranjan Mahesan

2019

General General

DeepSV: accurate calling of genomic deletions from high-throughput sequencing data using deep convolutional neural network.

In BMC bioinformatics ; h5-index 0.0

BACKGROUND : Calling genetic variations from sequence reads is an important problem in genomics. There are many existing methods for calling various types of variations. Recently, Google developed a method for calling single nucleotide polymorphisms (SNPs) based on deep learning. Their method visualizes sequence reads in the forms of images. These images are then used to train a deep neural network model, which is used to call SNPs. This raises a research question: can deep learning be used to call more complex genetic variations such as structural variations (SVs) from sequence data?

RESULTS : In this paper, we extend this high-level approach to the problem of calling structural variations. We present DeepSV, an approach based on deep learning for calling long deletions from sequence reads. DeepSV is based on a novel method of visualizing sequence reads. The visualization is designed to capture multiple sources of information in the sequence data that are relevant to long deletions. DeepSV also implements techniques for working with noisy training data. DeepSV trains a model from the visualized sequence reads and calls deletions based on this model. We demonstrate that DeepSV outperforms existing methods in terms of accuracy and efficiency of deletion calling on the data from the 1000 Genomes Project.

CONCLUSIONS : Our work shows that deep learning can potentially lead to effective calling of different types of genetic variations that are complex than SNPs.

Cai Lei, Wu Yufeng, Gao Jingyang

2019-Dec-12

Deep learning, Feature extraction, Genetic variations, High-throughput sequencing, Structural variations, Visualization

Surgery Surgery

Predicting the occurrence of surgical site infections using text mining and machine learning.

In PloS one ; h5-index 176.0

In this study we propose the use of text mining and machine learning methods to predict and detect Surgical Site Infections (SSIs) using textual descriptions of surgeries and post-operative patients' records, mined from the database of a high complexity University hospital. SSIs are among the most common adverse events experienced by hospitalized patients; preventing such events is fundamental to ensure patients' safety. Knowledge on SSI occurrence rates may also be useful in preventing future episodes. We analyzed 15,479 surgery descriptions and post-operative records testing different preprocessing strategies and the following machine learning algorithms: Linear SVC, Logistic Regression, Multinomial Naive Bayes, Nearest Centroid, Random Forest, Stochastic Gradient Descent, and Support Vector Classification (SVC). For prediction purposes, the best result was obtained using the Stochastic Gradient Descent method (79.7% ROC-AUC); for detection, Logistic Regression yielded the best performance (80.6% ROC-AUC).

da Silva Daniel A, Ten Caten Carla S, Dos Santos Rodrigo P, Fogliatto Flavio S, Hsuan Juliana

2019

General General

Robust, automated sleep scoring by a compact neural network with distributional shift correction.

In PloS one ; h5-index 176.0

Studying the biology of sleep requires the accurate assessment of the state of experimental subjects, and manual analysis of relevant data is a major bottleneck. Recently, deep learning applied to electroencephalogram and electromyogram data has shown great promise as a sleep scoring method, approaching the limits of inter-rater reliability. As with any machine learning algorithm, the inputs to a sleep scoring classifier are typically standardized in order to remove distributional shift caused by variability in the signal collection process. However, in scientific data, experimental manipulations introduce variability that should not be removed. For example, in sleep scoring, the fraction of time spent in each arousal state can vary between control and experimental subjects. We introduce a standardization method, mixture z-scoring, that preserves this crucial form of distributional shift. Using both a simulated experiment and mouse in vivo data, we demonstrate that a common standardization method used by state-of-the-art sleep scoring algorithms introduces systematic bias, but that mixture z-scoring does not. We present a free, open-source user interface that uses a compact neural network and mixture z-scoring to allow for rapid sleep scoring with accuracy that compares well to contemporary methods. This work provides a set of computational tools for the robust automation of sleep scoring.

Barger Zeke, Frye Charles G, Liu Danqian, Dan Yang, Bouchard Kristofer E

2019

Pathology Pathology

A computer-aided diagnosis system for differentiation and delineation of malignant regions on whole-slide prostate histopathology image using spatial statistics and multidimensional DenseNet.

In Medical physics ; h5-index 59.0

PURPOSE : Prostate cancer (PCa) is a major health concern in aging males, and proper management of the disease depends on accurately interpreting pathology specimens. However, reading prostatectomy histopathology slides, which is basically for staging, is usually time-consuming and differs from reading small biopsy specimens, which is mainly used for diagnosis. Generally, each prostatectomy specimen generates tens of large tissue sections and for each section, the malignant region needs to be delineated to assess the amount of tumor and its burden. With the aim of reducing the workload of pathologists, in this study, we focus on developing a computer-aided diagnosis (CAD) system based on a densely connected convolutional neural network (DenseNet) for whole-slide histopathology images to outline the malignant regions.

METHODS : We use an efficient color normalization process based on ranklet transformation to automatically correct the intensity of the images. Additionally, we use spatial probability to segment the tissue structure regions for different tissue recognition patterns. Based on the segmentation, we incorporate a multidimensional structure into DenseNet to determine if a particular prostatic region is benign or malignant.

RESULTS : As demonstrated by the experimental results with a test set of 2,663 images from 32 whole-slide prostate histopathology images, our proposed system achieved 0.726, 0.6306, and 0.5209 in the average of the Dice coefficient, Jaccard similarity coefficient, and Boundary F1 score measures, respectively. Then, the accuracy, sensitivity, specificity, and the area under the ROC curve (AUC) of the proposed classification method were observed to be 95.0% (2544/2663), 96.7% (1210/1251), 93.9% (1334/1412), and 0.9831, respectively.

DISCUSSIONS : We provide a detailed discussion on how our proposed system demonstrates considerable improvement compared with similar methods considered in previous researches as well as how it can be used for delineating malignant regions.

Chen Chiao-Min, Huang Yao-Sian, Fang Pei-Wei, Liang Cher-Wei, Chang Ruey-Feng

2019-Dec-13

computer-aided diagnosis, deep learning, densely connected network, prostate cancer, whole-slide histopathology image

General General

MLDSP-GUI: An alignment-free standalone tool with an interactive graphical user interface for DNA sequence comparison and analysis.

In Bioinformatics (Oxford, England) ; h5-index 0.0

SUMMARY : MLDSP-GUI (Machine Learning with Digital Signal Processing) is an open-source, alignment-free, ultrafast, computationally lightweight, standalone software tool with an interactive Graphical User Interface (GUI) for comparison and analysis of DNA sequences. MLDSP-GUI is a general-purpose tool that can be used for a variety of applications such as taxonomic classification, disease classification, virus subtype classification, evolutionary analyses, among others.

AVAILABILITY : MLDSP-GUI is open-source, cross-platform compatible, and is available under the terms of the Creative Commons Attribution 4.0 International license (http://creativecommons.org/licenses/by/4.0/). The executable and dataset files are available at https://sourceforge.net/projects/mldsp-gui/.

SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.

Randhawa Gurjit S, Hill Kathleen A, Kari Lila

2019-Dec-13

Surgery Surgery

External Validation of PATHFx Version 3.0 in Patients Treated Surgically and Non-surgically for Symptomatic Skeletal Metastases.

In Clinical orthopaedics and related research ; h5-index 71.0

BACKGROUND : PATHFx is a clinical decision-support tool based on machine learning capable of estimating the likelihood of survival after surgery for patients with skeletal metastases. The applicability of any machine-learning tool depends not only on successful external validation in unique patient populations but also on remaining relevant as more effective systemic treatments are introduced. With advancements in the treatment of metastatic disease, it is our responsibility to patients to ensure clinical support tools remain contemporary and accurate.

QUESTION/PURPOSES : Therefore, we sought to (1) generate updated PATHFx models using recent data from patients treated at one large, urban tertiary referral center and (2) externally validate the models using two contemporary patient populations treated either surgically or nonsurgically with external-beam radiotherapy alone for symptomatic skeletal metastases for symptomatic lesions.

METHODS : After obtaining institutional review board approval, we collected data on 208 patients undergoing surgical treatment for pathologic fractures at Memorial Sloan Kettering Cancer Center between 2015 and 2018. These data were combined with the original PATHFx training set (n = 189) to create the final training set (n = 397). We then created six Bayesian belief networks designed to estimate the likelihood of 1-month, 3-month, 6-month, 12-month, 18-month, and 24-month survival after treatment. Bayesian belief analysis is a statistical method that allows data-driven learning to arise from conditional probabilities by exploring relationships between variables to estimate the likelihood of an outcome using observed data. For external validation, we extracted the records of patients treated between 2016 and 2018 from the International Bone Metastasis Registry and records of patients treated nonoperatively with external-beam radiation therapy for symptomatic skeletal metastases from 2012 to 2016 using the Military Health System Data Repository (radiotherapy-only group). From each record, we collected the date of treatment, laboratory values at the time of treatment initiation, demographic data, details of diagnosis, and the date of death. All records reported sufficient follow-up to establish survival (yes/no) at 24-months after treatment. For external validation, we applied the data from each record to the new PATHFx models. We assessed calibration (calibration plots), accuracy (Brier score), discriminatory ability (area under the receiver operating characteristic curve [AUC]).

RESULTS : The updated PATHFx version 3.0 models successfully classified survival at each time interval in both external validation sets and demonstrated appropriate discriminatory ability and model calibration. The Bayesian models were reasonably calibrated to the Memorial Sloan Kettering Cancer Center training set. External validation with 197 records from the International Bone Metastasis Registry and 192 records from the Military Health System Data Repository for analysis found Brier scores that were all less than 0.20, with upper bounds of the 95% CIs all less than 0.25, both for the radiotherapy-only and International Bone Metastasis Registry groups. Additionally, AUC estimates were all greater than 0.70, with lower bounds of the 95% CI all greater than 0.68, except for the 1-month radiotherapy-only group. To complete external validation, decision curve analysis demonstrated clinical utility. This means it was better to use the PATHFx models when compared to the default assumption that all or no patients would survive at all time periods except for the 1-month models. We believe the favorable Brier scores (< 0.20) as well as DCA indicate these models are suitable for clinical use.

CONCLUSIONS : We successfully updated PATHFx using contemporary data from patients undergoing either surgical or nonsurgical treatment for symptomatic skeletal metastases. These models have been incorporated for clinical use on PATHFx version 3.0 (https://www.pathfx.org). Clinically, external validation suggests it is better to use PATHFx version 3.0 for all time periods except when deciding whether to give radiotherapy to patients with the life expectancy of less than 1 month. This is partly because most patients survived 1-month after treatment. With the advancement of medical technology in treatment and diagnosis for patients with metastatic bone disease, part of our fiduciary responsibility is to the main current clinical support tools.

LEVEL OF EVIDENCE : Level III, therapeutic study.

Anderson Ashley B, Wedin Rikard, Fabbri Nicola, Boland Patrick, Healey John, Forsberg Jonathan A

2019-Dec-06

Public Health Public Health

Polypharmacy in HIV: recent insights and future directions.

In Current opinion in HIV and AIDS ; h5-index 41.0

PURPOSE OF REVIEW : Update findings regarding polypharmacy among people with HIV (PWH) and consider what research is most needed.

RECENT FINDINGS : Among PWH, polypharmacy is common, occurs in middle age, and is predominantly driven by nonantiretroviral (ARV) medications. Many studies have demonstrated strong associations between polypharmacy and receipt of potentially inappropriate medications (PIMS), but few have considered actual adverse events. Falls, delirium, pneumonia, hospitalization, and mortality are associated with polypharmacy among PWH and risks remain after adjustment for severity of illness.

SUMMARY : Polypharmacy is a growing problem and mechanisms of injury likely include potentially inappropriate medications, total drug burden, known pairwise drug interactions, higher level drug interactions, drug--gene interactions, and drug--substance use interactions (alcohol, extra-medical prescription medication, and drug use). Before we can effectively design interventions, we need to use observational data to gain a better understanding of the modifiable mechanisms of injury. As sicker individuals take more medications, analyses must account for severity of illness. As self-report of substance use may be inaccurate, direct biomarkers, such as phosphatidylethanol (PEth) for alcohol are needed. Large samples including electronic health records, genetics, accurate measures of substance use, and state of the art statistical and artificial intelligence techniques are needed to advance our understanding and inform clinical management of polypharmacy in PWH.

Edelman E Jennifer, Rentsch Christopher T, Justice Amy C

2019-Dec-10

General General

High Accuracy of Convolutional Neural Network for Evaluation of Helicobacter pylori Infection Based on Endoscopic Images: Preliminary Experience.

In Clinical and translational gastroenterology ; h5-index 0.0

OBJECTIVES : Application of artificial intelligence in gastrointestinal endoscopy is increasing. The aim of the study was to examine the accuracy of convolutional neural network (CNN) using endoscopic images for evaluating Helicobacter pylori (H. pylori) infection.

METHODS : Patients who received upper endoscopy and gastric biopsies at Sir Run Run Shaw Hospital (January 2015-June 2015) were retrospectively searched. A novel Computer-Aided Decision Support System that incorporates CNN model (ResNet-50) based on endoscopic gastric images was developed to evaluate for H. pylori infection. Diagnostic accuracy was evaluated in an independent validation cohort. H. pylori infection was defined by the presence of H. pylori on immunohistochemistry testing on gastric biopsies and/or a positive 13C-urea breath test.

RESULTS : Of 1,959 patients, 1,507 (77%) including 847 (56%) with H. pylori infection (11,729 gastric images) were assigned to the derivation cohort, and 452 (23%) including 310 (69%) with H. pylori infection (3,755 images) were assigned to the validation cohort. The area under the curve for a single gastric image was 0.93 (95% confidence interval [CI] 0.92-0.94) with sensitivity, specificity, and accuracy of 81.4% (95% CI 79.8%-82.9%), 90.1% (95% CI 88.4%-91.7%), and 84.5% (95% CI 83.3%-85.7%), respectively, using an optimal cutoff value of 0.3. Area under the curve for multiple gastric images (8.3 ± 3.3) per patient was 0.97 (95% CI 0.96-0.99) with sensitivity, specificity, and accuracy of 91.6% (95% CI 88.0%-94.4%), 98.6% (95% CI 95.0%-99.8%), and 93.8% (95% CI 91.2%-95.8%), respectively, using an optimal cutoff value of 0.4.

DISCUSSION : In this pilot study, CNN using multiple archived gastric images achieved high diagnostic accuracy for the evaluation of H. pylori infection.

Zheng Wenfang, Zhang Xu, Kim John J, Zhu Xinjian, Ye Guoliang, Ye Bin, Wang Jianping, Luo Songlin, Li Jingjing, Yu Tao, Liu Jiquan, Hu Weiling, Si Jianmin

2019-Dec-11

General General

Artificial intelligence and neural networks in urology: current clinical applications.

In Minerva urologica e nefrologica = The Italian journal of urology and nephrology ; h5-index 0.0

INTRODUCTION : As we enter the era of "big data", an increasing amount of complex health- care data will become available. These data are often redundant, "noisy", and characterized by wide variability. In order to offer a precise and transversal view of a clinical scenario the Artificial Intelligence (AI) with Machine learning (ML) algorithms and Artificial neuron networks (ANNs) process were adopted, with a promising wide diffusion in the near future. The present work aims to provide a comprehensive and critical overview of the current and potential applications of AI and ANNs in Urology.

EVIDENCE ACQUISITION : A non-systematic review of the literature was performed by screening Medline, PubMed, the Cochrane Database, and Embase to detect pertinent studies regarding the application of AI and ANN in Urology.

EVIDENCE SYNTHESIS : The main application of AI in urology is the field of genitourinary cancers. Focusing on prostate cancer, AI was applied for the prediction of prostate biopsy results. For bladder cancer, the prediction of recurrence-free probability and diagnostic evaluation were analysed with ML algorithms. For kidney and testis cancer, anecdotal experiences were reported for staging and prediction of diseases recurrence. More recently, AI has been applied in non- oncological diseases like stones and functional urology.

CONCLUSIONS : AI technologies are growing their role in health care; but, up to now, their "real-life" implementation remains limited. However, in the near future, the potential of AI-driven era could change the clinical practice in Urology, improving overall patient outcomes.

Checcucci Enrico, Autorino Riccardo, Cacciamani Giovanni E, Amparore Daniele, De Cillis Sabrina, Piana Alberto, Piazzolla Pietro, Vezzetti Enrico, Fiori Cristian, Veneziano Domenico, Tewari Ash, Dasgupta Prokar, Hung Andrew, Gill Inderbir, Porpiglia Francesco

2019-Dec-12

General General

Density Functional Theory as a Data Science.

In Chemical record (New York, N.Y.) ; h5-index 0.0

The development of density functional theory (DFT) functionals and physical corrections are reviewed focusing on the physical meanings and the semiempirical parameters from the viewpoint of data science. This review shows that DFT exchange-correlation functionals have been developed under many strict physical conditions with minimizing the number of the semiempirical parameters, except for some recent functionals. Major physical corrections for exchange-correlation function- als are also shown to have clear physical meanings independent of the functionals, though they inevitably require minimum semiempirical parameters dependent on the functionals combined. We, therefore, interpret that DFT functionals with physical corrections are the most sophisticated target functions that are physically legitimated, even from the viewpoint of data science.

Tsuneda Takao

2019-Dec-13

Density functional theory, Exchange-correlation functionals, Machine learning, Physical corrections

General General

Learning-based biomarker-assisted rules for optimized clinical benefit under a risk-constraint.

In Biometrics ; h5-index 0.0

Novel biomarkers, in combination with currently available clinical information, have been sought to improve clinical decision making in many branches of medicine, including screening, surveillance, and prognosis. Statistical methods are needed to integrate such diverse information to develop targeted interventions that balance benefit and harm. In the specific setting of disease detection, we propose novel approaches to construct a multiple-marker-based decision rule by directly optimizing a benefit function, while controlling harm at a maximally tolerable level. These new approaches include plug-in and direct-optimization-based algorithms, and they allow for the construction of both nonparametric and parametric rules. A study of asymptotic properties of the proposed estimators is provided. Simulation results demonstrate good clinical utilities for the resulting decision rules under various scenarios. The methods are applied to a biomarker study in prostate cancer surveillance. This article is protected by copyright. All rights reserved.

Wang Yanqing, Zhao Ying-Qi, Zheng Yingye

2019-Dec-13

biomarker, clinical decision rules, false positive fraction, machine learning, true positive fraction

Surgery Surgery

Usefulness of regional right ventricular and right atrial strain for prediction of early and late right ventricular failure following a left ventricular assist device implant: A machine learning approach.

In The International journal of artificial organs ; h5-index 0.0

BACKGROUND : Identifying candidates for left ventricular assist device surgery at risk of right ventricular failure remains difficult. The aim was to identify the most accurate predictors of right ventricular failure among clinical, biological, and imaging markers, assessed by agreement of different supervised machine learning algorithms.

METHODS : Seventy-four patients, referred to HeartWare left ventricular assist device since 2010 in two Italian centers, were recruited. Biomarkers, right ventricular standard, and strain echocardiography, as well as cath-lab measures, were compared among patients who did not develop right ventricular failure (N = 56), those with acute-right ventricular failure (N = 8, 11%) or chronic-right ventricular failure (N = 10, 14%). Logistic regression, penalized logistic regression, linear support vector machines, and naïve Bayes algorithms with leave-one-out validation were used to evaluate the efficiency of any combination of three collected variables in an "all-subsets" approach.

RESULTS : Michigan risk score combined with central venous pressure assessed invasively and apical longitudinal systolic strain of the right ventricular-free wall were the most significant predictors of acute-right ventricular failure (maximum receiver operating characteristic-area under the curve = 0.95, 95% confidence interval = 0.91-1.00, by the naïve Bayes), while the right ventricular-free wall systolic strain of the middle segment, right atrial strain (QRS-synced), and tricuspid annular plane systolic excursion were the most significant predictors of Chronic-RVF (receiver operating characteristic-area under the curve = 0.97, 95% confidence interval = 0.91-1.00, according to naïve Bayes).

CONCLUSION : Apical right ventricular strain as well as right atrial strain provides complementary information, both critical to predict acute-right ventricular failure and chronic-right ventricular failure, respectively.

Bellavia Diego, Iacovoni Attilio, Agnese Valentina, Falletta Calogero, Coronnello Claudia, Pasta Salvatore, Novo Giuseppina, di Gesaro Gabriele, Senni Michele, Maalouf Joseph, Sciacca Sergio, Pilato Michele, Simon Marc, Clemenza Francesco, Gorcsan Sir John

2019-Dec-12

Right ventricle, echocardiography, heart failure, machine learning, strain imaging

General General

The Lancaster Sensorimotor Norms: multidimensional measures of perceptual and action strength for 40,000 English words.

In Behavior research methods ; h5-index 0.0

Sensorimotor information plays a fundamental role in cognition. However, the existing materials that measure the sensorimotor basis of word meanings and concepts have been restricted in terms of their sample size and breadth of sensorimotor experience. Here we present norms of sensorimotor strength for 39,707 concepts across six perceptual modalities (touch, hearing, smell, taste, vision, and interoception) and five action effectors (mouth/throat, hand/arm, foot/leg, head excluding mouth/throat, and torso), gathered from a total of 3,500 individual participants using Amazon's Mechanical Turk platform. The Lancaster Sensorimotor Norms are unique and innovative in a number of respects: They represent the largest-ever set of semantic norms for English, at 40,000 words × 11 dimensions (plus several informative cross-dimensional variables), they extend perceptual strength norming to the new modality of interoception, and they include the first norming of action strength across separate bodily effectors. In the first study, we describe the data collection procedures, provide summary descriptives of the dataset, and interpret the relations observed between sensorimotor dimensions. We then report two further studies, in which we (1) extracted an optimal single-variable composite of the 11-dimension sensorimotor profile (Minkowski 3 strength) and (2) demonstrated the utility of both perceptual and action strength in facilitating lexical decision times and accuracy in two separate datasets. These norms provide a valuable resource to researchers in diverse areas, including psycholinguistics, grounded cognition, cognitive semantics, knowledge representation, machine learning, and big-data approaches to the analysis of language and conceptual representations. The data are accessible via the Open Science Framework (http://osf.io/7emr6/) and an interactive web application (https://www.lancaster.ac.uk/psychology/lsnorms/).

Lynott Dermot, Connell Louise, Brysbaert Marc, Brand James, Carney James

2019-Dec-12

Surgery Surgery

Machine learning methods are comparable to logistic regression techniques in predicting severe walking limitation following total knee arthroplasty.

In Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA ; h5-index 0.0

PURPOSE : Machine-learning methods are flexible prediction algorithms with potential advantages over conventional regression. This study aimed to use machine learning methods to predict post-total knee arthroplasty (TKA) walking limitation, and to compare their performance with that of logistic regression.

METHODS : From the department's clinical registry, a cohort of 4026 patients who underwent elective, primary TKA between July 2013 and July 2017 was identified. Candidate predictors included demographics and preoperative clinical, psychosocial, and outcome measures. The primary outcome was severe walking limitation at 6 months post-TKA, defined as a maximum walk time ≤ 15 min. Eight common regression (logistic, penalized logistic, and ordinal logistic with natural splines) and ensemble machine learning (random forest, extreme gradient boosting, and SuperLearner) methods were implemented to predict the probability of severe walking limitation. Models were compared on discrimination and calibration metrics.

RESULTS : At 6 months post-TKA, 13% of patients had severe walking limitation. Machine learning and logistic regression models performed moderately [mean area under the ROC curves (AUC) 0.73-0.75]. Overall, the ordinal logistic regression model performed best while the SuperLearner performed best among machine learning methods, with negligible differences between them (Brier score difference, < 0.001; 95% CI [- 0.0025, 0.002]).

CONCLUSIONS : When predicting post-TKA physical function, several machine learning methods did not outperform logistic regression-in particular, ordinal logistic regression that does not assume linearity in its predictors.

LEVEL OF EVIDENCE : Prognostic level II.

Pua Yong-Hao, Kang Hakmook, Thumboo Julian, Clark Ross Allan, Chew Eleanor Shu-Xian, Poon Cheryl Lian-Li, Chong Hwei-Chi, Yeo Seng-Jin

2019-Dec-12

Algorithms, Arthroplasty, Artificial intelligence, Knee, Machine learning, Prediction, Replacement

Surgery Surgery

Artificial Neural Networks to Assess Virtual Reality Anterior Cervical Discectomy Performance.

In Operative neurosurgery (Hagerstown, Md.) ; h5-index 0.0

BACKGROUND : Virtual reality surgical simulators provide a safe environment for trainees to practice specific surgical scenarios and allow for self-guided learning. Artificial intelligence technology, including artificial neural networks, offers the potential to manipulate large datasets from simulators to gain insight into the importance of specific performance metrics during simulated operative tasks.

OBJECTIVE : To distinguish performance in a virtual reality-simulated anterior cervical discectomy scenario, uncover novel performance metrics, and gain insight into the relative importance of each metric using artificial neural networks.

METHODS : Twenty-one participants performed a simulated anterior cervical discectomy on the novel virtual reality Sim-Ortho simulator. Participants were divided into 3 groups, including 9 post-resident, 5 senior, and 7 junior participants. This study focused on the discectomy portion of the task. Data were recorded and manipulated to calculate metrics of performance for each participant. Neural networks were trained and tested and the relative importance of each metric was calculated.

RESULTS : A total of 369 metrics spanning 4 categories (safety, efficiency, motion, and cognition) were generated. An artificial neural network was trained on 16 selected metrics and tested, achieving a training accuracy of 100% and a testing accuracy of 83.3%. Network analysis identified safety metrics, including the number of contacts on spinal dura, as highly important.

CONCLUSION : Artificial neural networks classified 3 groups of participants based on expertise allowing insight into the relative importance of specific metrics of performance. This novel methodology aids in the understanding of which components of surgical performance predominantly contribute to expertise.

Mirchi Nykan, Bissonnette Vincent, Ledwos Nicole, Winkler-Schwartz Alexander, Yilmaz Recai, Karlik Bekir, Del Maestro Rolando F

2019-Dec-13

Anterior cervical discectomy, Artificial intelligence, Artificial neural networks, Machine learning, Simulation, Surgical training, Virtual reality

General General

Windows Into Human Health Through Wearables Data Analytics.

In Current opinion in biomedical engineering ; h5-index 0.0

Background : Wearable sensors (wearables) have been commonly integrated into a wide variety of commercial products and are increasingly being used to collect and process raw physiological parameters into salient digital health information. The data collected by wearables are currently being investigated across a broad set of clinical domains and patient populations. There is significant research occurring in the domain of algorithm development, with the aim of translating raw sensor data into fitness- or health-related outcomes of interest for users, patients, and health care providers.

Objectives : The aim of this review is to highlight a selected group of fitness- and health-related indicators from wearables data and to describe several algorithmic approaches used to generate these higher order indicators.

Methods : A systematic search of the Pubmed database was performed with the following search terms (number of records in parentheses): Fitbit algorithm (18), Apple Watch algorithm (3), Garmin algorithm (5), Microsoft Band algorithm (8), Samsung Gear algorithm (2), Xiaomi MiBand algorithm (1), Huawei Band (Watch) algorithm (2), photoplethysmography algorithm (465), accelerometry algorithm (966), ECG algorithm (8287), continuous glucose monitor algorithm (343). The search terms chosen for this review are focused on algorithms for wearable devices that dominated the commercial wearables market between 2014-2017 and that were highly represented in the biomedical literature. A second set of search terms included categories of algorithms for fitness-related and health-related indicators that are commonly used in wearable devices (e.g. accelerometry, PPG, ECG). These papers covered the following domain areas: fitness; exercise; movement; physical activity; step count; walking; running; swimming; energy expenditure; atrial fibrillation; arrhythmia; cardiovascular; autonomic nervous system; neuropathy; heart rate variability; fall detection; trauma; behavior change; diet; eating; stress detection; serum glucose monitoring; continuous glucose monitoring; diabetes mellitus type 1; diabetes mellitus type 2. All studies uncovered through this search on commercially available device algorithms and pivotal studies on sensor algorithm development were summarized, and a summary table was constructed using references generated by the literature review as described (Table 1).

Conclusions : Wearable health technologies aim to collect and process raw physiological or environmental parameters into salient digital health information. Much of the current and future utility of wearables lies in the signal processing steps and algorithms used to analyze large volumes of data. Continued algorithmic development and advances in machine learning techniques will further increase analytic capabilities. In the context of these advances, our review aims to highlight a range of advances in fitness- and other health-related indicators provided by current wearable technologies.

Witt Daniel, Kellogg Ryan, Snyder Michael, Dunn Jessilyn

2019-Mar

algorithms, digital health, physiologic monitoring, wearables

General General

A noninvasive, machine learning-based method for monitoring anthocyanin accumulation in plants using digital color imaging.

In Applications in plant sciences ; h5-index 0.0

Premise : When plants are exposed to stress conditions, irreversible damage can occur, negatively impacting yields. It is therefore important to detect stress symptoms in plants, such as the accumulation of anthocyanin, as early as possible.

Methods and Results : Twenty-two regression models in five color spaces were trained to develop a prediction model for plant anthocyanin levels from digital color imaging data. Of these, a quantile random forest regression model trained with standard red, green, blue (sRGB) color space data most accurately predicted the actual anthocyanin levels. This model was then used to noninvasively monitor the spatial and temporal accumulation of anthocyanin in Arabidopsis thaliana leaves.

Conclusions : The digital imaging-based nature of this protocol makes it a low-cost and noninvasive method for the detection of plant stress. Applying a similar protocol to more economically viable crops could lead to the development of large-scale, cost-effective systems for monitoring plant health.

Askey Bryce C, Dai Ru, Lee Won Suk, Kim Jeongim

2019-Nov

anthocyanin, digital color imaging, early stress detection, machine learning

Cardiology Cardiology

Artificial intelligence supported patient self-care in chronic heart failure: a paradigm shift from reactive to predictive, preventive and personalised care.

In The EPMA journal ; h5-index 0.0

Heart failure (HF) is one of the most complex chronic disorders with high prevalence, mainly due to the ageing population and better treatment of underlying diseases. Prevalence will continue to rise and is estimated to reach 3% of the population in Western countries by 2025. It is the most important cause of hospitalisation in subjects aged 65 years or more, resulting in high costs and major social impact. The current "one-size-fits-all" approach in the treatment of HF does not result in best outcome for all patients. These facts are an imminent threat to good quality management of patients with HF. An unorthodox approach from a new vision on care is required. We propose a novel predictive, preventive and personalised medicine approach where patients are truly leading their management, supported by an easily accessible online application that takes advantage of artificial intelligence. This strategy paper describes the needs in HF care, the needed paradigm shift and the elements that are required to achieve this shift. Through the inspiring collaboration of clinical and high-tech partners from North-West Europe combining state of the art HF care, artificial intelligence, serious gaming and patient coaching, a virtual doctor is being created. The results are expected to advance and personalise self-care, where standard care tasks are performed by the patients themselves, in principle without involvement of healthcare professionals, the latter being able to focus on complex conditions. This new vision on care will significantly reduce costs per patient while improving outcomes to enable long-term sustainability of top-level HF care.

Barrett Matthew, Boyne Josiane, Brandts Julia, Brunner-La Rocca Hans-Peter, De Maesschalck Lieven, De Wit Kurt, Dixon Lana, Eurlings Casper, Fitzsimons Donna, Golubnitschaja Olga, Hageman Arjan, Heemskerk Frank, Hintzen André, Helms Thomas M, Hill Loreena, Hoedemakers Thom, Marx Nikolaus, McDonald Kenneth, Mertens Marc, Müller-Wieland Dirk, Palant Alexander, Piesk Jens, Pomazanskyi Andrew, Ramaekers Jan, Ruff Peter, Schütt Katharina, Shekhawat Yash, Ski Chantal F, Thompson David R, Tsirkin Andrew, van der Mierden Kay, Watson Chris, Zippel-Schultz Bettina

2019-Dec

Artificial Intelligence, Comorbidities, Diabetes, Disease modelling, Healthcare digitalisation, Healthcare economy, Heart failure, Individualised patient profile, Information and communications technology, Integrated care, Medical ethics, Multi-level diagnostics, Patient engagement, Patient stratification, Predictive preventive personalised participatory medicine, Professional interactome, Societal impact, Therapy monitoring

oncology Oncology

Chronic inflammation: key player and biomarker-set to predict and prevent cancer development and progression based on individualized patient profiles.

In The EPMA journal ; h5-index 0.0

A strong relationship exists between tumor and inflammation, which is the hot point in cancer research. Inflammation can promote the occurrence and development of cancer by promoting blood vessel growth, cancer cell proliferation, and tumor invasiveness, negatively regulating immune response, and changing the efficacy of certain anti-tumor drugs. It has been demonstrated that there are a large number of inflammatory factors and inflammatory cells in the tumor microenvironment, and tumor-promoting immunity and anti-tumor immunity exist simultaneously in the tumor microenvironment. The typical relationship between chronic inflammation and tumor has been presented by the relationships between Helicobacter pylori, chronic gastritis, and gastric cancer; between smoking, development of chronic pneumonia, and lung cancer; and between hepatitis virus (mainly hepatitis virus B and C), development of chronic hepatitis, and liver cancer. The prevention of chronic inflammation is a factor that can prevent cancer, so it effectively inhibits or blocks the occurrence, development, and progression of the chronic inflammation process playing important roles in the prevention of cancer. Monitoring of the causes and inflammatory factors in chronic inflammation processes is a useful way to predict cancer and assess the efficiency of cancer prevention. Chronic inflammation-based biomarkers are useful tools to predict and prevent cancer.

Qian Shehua, Golubnitschaja Olga, Zhan Xianquan

2019-Dec

Big data analysis, Biomarkers, Cancer, Chronic inflammation, Collateral pathologies, Epigenetics, Genetics, Global statistics, Individualized patient profile, Inflammatory factors, Machine learning, Modifiable and preventable, Multiomics, Patient stratification, Phenotyping, Predictive preventive personalized medicine, Risk factors

General General

Machine learning driven non-invasive approach of water content estimation in living plant leaves using terahertz waves.

In Plant methods ; h5-index 0.0

Background : The demand for effective use of water resources has increased because of ongoing global climate transformations in the agriculture science sector. Cost-effective and timely distributions of the appropriate amount of water are vital not only to maintain a healthy status of plants leaves but to drive the productivity of the crops and achieve economic benefits. In this regard, employing a terahertz (THz) technology can be more reliable and progressive technique due to its distinctive features. This paper presents a novel, and non-invasive machine learning (ML) driven approach using terahertz waves with a swissto12 material characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz in real-life digital agriculture interventions, aiming to develop a feasible and viable technique for the precise estimation of water content (WC) in plants leaves for 4 days. For this purpose, using measurements observations data, multi-domain features are extracted from frequency, time, time-frequency domains to incorporate three different machine learning algorithms such as support vector machine (SVM), K-nearest neighbour (KNN) and decision-tree (D-Tree).

Results : The results demonstrated SVM outperformed other classifiers using tenfold and leave-one-observations-out cross-validation for different days classification with an overall accuracy of 98.8%, 97.15%, and 96.82% for Coffee, pea shoot, and baby spinach leaves respectively. In addition, using SFS technique, coffee leaf showed a significant improvement of 15%, 11.9%, 6.5% in computational time for SVM, KNN and D-tree. For pea-shoot, 21.28%, 10.01%, and 8.53% of improvement was noticed in operating time for SVM, KNN and D-Tree classifiers, respectively. Lastly, baby spinach leaf exhibited a further improvement of 21.28% in SVM, 10.01% in KNN, and 8.53% in D-tree in overall operating time for classifiers. These improvements in classifiers produced significant advancements in classification accuracy, indicating a more precise quantification of WC in leaves.

Conclusion : Thus, the proposed method incorporating ML using terahertz waves can be beneficial for precise estimation of WC in leaves and can provide prolific recommendations and insights for growers to take proactive actions in relations to plants health monitoring.

Zahid Adnan, Abbas Hasan T, Ren Aifeng, Zoha Ahmed, Heidari Hadi, Shah Syed A, Imran Muhammad A, Alomainy Akram, Abbasi Qammer H

2019

Agriculture, Classification, Machine learning, Plant leaves, Sensing, Terahertz (THz), Water content

General General

Drug repurposing to improve treatment of rheumatic autoimmune inflammatory diseases.

In Nature reviews. Rheumatology ; h5-index 74.0

The past century has been characterized by intensive efforts, within both academia and the pharmaceutical industry, to introduce new treatments to individuals with rheumatic autoimmune inflammatory diseases (RAIDs), often by 'borrowing' treatments already employed in one RAID or previously used in an entirely different disease, a concept known as drug repurposing. However, despite sharing some clinical manifestations and immune dysregulation, disease pathogenesis and phenotype vary greatly among RAIDs, and limited understanding of their aetiology has made repurposing drugs for RAIDs challenging. Nevertheless, the past century has been characterized by different 'waves' of repurposing. Early drug repurposing occurred in academia and was based on serendipitous observations or perceived disease similarity, often driven by the availability and popularity of drug classes. Since the 1990s, most biologic therapies have been developed for one or several RAIDs and then tested among the others, with varying levels of success. The past two decades have seen data-driven repurposing characterized by signature-based approaches that rely on molecular biology and genomics. Additionally, many data-driven strategies employ computational modelling and machine learning to integrate multiple sources of data. Together, these repurposing periods have led to advances in the treatment for many RAIDs.

Kingsmore Kathryn M, Grammer Amrie C, Lipsky Peter E

2019-Dec-12

General General

Coupled whole-body rhythmic entrainment between two chimpanzees.

In Scientific reports ; h5-index 158.0

Dance is an icon of human expression. Despite astounding diversity around the world's cultures and dazzling abundance of reminiscent animal systems, the evolution of dance in the human clade remains obscure. Dance requires individuals to interactively synchronize their whole-body tempo to their partner's, with near-perfect precision. This capacity is motorically-heavy, engaging multiple neural circuitries, but also dependent on an acute socio-emotional bond between partners. Hitherto, these factors helped explain why no dance forms were present amongst nonhuman primates. Critically, evidence for conjoined full-body rhythmic entrainment in great apes that could help reconstruct possible proto-stages of human dance is still lacking. Here, we report an endogenously-effected case of ritualized dance-like behaviour between two captive chimpanzees - synchronized bipedalism. We submitted video recordings to rigorous time-series analysis and circular statistics. We found that individual step tempo was within the genus' range of "solo" bipedalism. Between-individual analyses, however, revealed that synchronisation between individuals was non-random, predictable, phase concordant, maintained with instantaneous centi-second precision and jointly regulated, with individuals also taking turns as "pace-makers". No function was apparent besides the behaviour's putative positive social affiliation. Our analyses show a first case of spontaneous whole-body entrainment between two ape peers, thus providing tentative empirical evidence for phylogenies of human dance. Human proto-dance, we argue, may have been rooted in mechanisms of social cohesion among small groups that might have granted stress-releasing benefits via gait-synchrony and mutual-touch. An external sound/musical beat may have been initially uninvolved. We discuss dance evolution as driven by ecologically-, socially- and/or culturally-imposed "captivity".

Lameira Adriano R, Eerola Tuomas, Ravignani Andrea

2019-Dec-12

General General

Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review.

In F1000Research ; h5-index 0.0

Background: Clinical decision support (CDS) systems have emerged as tools providing intelligent decision making to address challenges of critical care. CDS systems can be based on existing guidelines or best practices; and can also utilize machine learning to provide a diagnosis, recommendation, or therapy course. Methods: This research aimed to identify evidence-based study designs and outcome measures to determine the clinical effectiveness of clinical decision support systems in the detection and prediction of hemodynamic instability, respiratory distress, and infection within critical care settings. PubMed, ClinicalTrials.gov and Cochrane Database of Systematic Reviews were systematically searched to identify primary research published in English between 2013 and 2018. Studies conducted in the USA, Canada, UK, Germany and France with more than 10 participants per arm were included. Results: In studies on hemodynamic instability, the prediction and management of septic shock were the most researched topics followed by the early prediction of heart failure. For respiratory distress, the most popular topics were pneumonia detection and prediction followed by pulmonary embolisms. Given the importance of imaging and clinical notes, this area combined Machine Learning with image analysis and natural language processing. In studies on infection, the most researched areas were the detection, prediction, and management of sepsis, surgical site infections, as well as acute kidney injury. Overall, a variety of Machine Learning algorithms were utilized frequently, particularly support vector machines, boosting techniques, random forest classifiers and neural networks. Sensitivity, specificity, and ROC AUC were the most frequently reported performance measures. Conclusion: This review showed an increasing use of Machine Learning for CDS in all three areas. Large datasets are required for training these algorithms; making it imperative to appropriately address, challenges such as class imbalance, correct labelling of data and missing data. Recommendations are formulated for the development and successful adoption of CDS systems.

Medic Goran, Kosaner Kließ Melodi, Atallah Louis, Weichert Jochen, Panda Saswat, Postma Maarten, El-Kerdi Amer

2019

clinical trials, critical care., hemodynamic instability, infection, machine learning, respiratory distress, sepsis

General General

Deep learning and taphonomy: high accuracy in the classification of cut marks made on fleshed and defleshed bones using convolutional neural networks.

In Scientific reports ; h5-index 158.0

Accurate identification of bone surface modifications (BSM) is crucial for the taphonomic understanding of archaeological and paleontological sites. Critical interpretations of when humans started eating meat and animal fat or when they started using stone tools, or when they occupied new continents or interacted with predatory guilds impinge on accurate identifications of BSM. Until now, interpretations of Plio-Pleistocene BSM have been contentious because of the high uncertainty in discriminating among taphonomic agents. Recently, the use of machine learning algorithms has yielded high accuracy in the identification of BSM. A branch of machine learning methods based on imaging, computer vision (CV), has opened the door to a more objective and accurate method of BSM identification. The present work has selected two extremely similar types of BSM (cut marks made on fleshed an defleshed bones) to test the immense potential of artificial intelligence methods. This CV approach not only produced the highest accuracy in the classification of these types of BSM until present (95% on complete images of BSM and 88.89% of images of only internal mark features), but it also has enabled a method for determining which inconspicuous microscopic features determine successful BSM discrimination. The potential of this method in other areas of taphonomy and paleobiology is enormous.

Cifuentes-Alcobendas Gabriel, Domínguez-Rodrigo Manuel

2019-Dec-12

Public Health Public Health

Current state of science in machine learning methods for automatic infant pain evaluation using facial expression information: study protocol of a systematic review and meta-analysis.

In BMJ open ; h5-index 0.0

INTRODUCTION : Infants can experience pain similar to adults, and improperly controlled pain stimuli could have a long-term adverse impact on their cognitive and neurological function development. The biggest challenge of achieving good infant pain control is obtaining objective pain assessment when direct communication is lacking. For years, computer scientists have developed many different facial expression-centred machine learning (ML) methods for automatic infant pain assessment. Many of these ML algorithms showed rather satisfactory performance and have demonstrated good potential to be further enhanced for implementation in real-world clinical settings. To date, there is no prior research that has systematically summarised and compared the performance of these ML algorithms. Our proposed meta-analysis will provide the first comprehensive evidence on this topic to guide further ML algorithm development and clinical implementation.

METHODS AND ANALYSIS : We will search four major public electronic medical and computer science databases including Web of Science, PubMed, Embase and IEEE Xplore Digital Library from January 2008 to present. All the articles will be imported into the Covidence platform for study eligibility screening and inclusion. Study-level extracted data will be stored in the Systematic Review Data Repository online platform. The primary outcome will be the prediction accuracy of the ML model. The secondary outcomes will be model utility measures including generalisability, interpretability and computational efficiency. All extracted outcome data will be imported into RevMan V.5.2.1 software and R V3.3.2 for analysis. Risk of bias will be summarised using the latest Prediction Model Study Risk of Bias Assessment Tool.

ETHICS AND DISSEMINATION : This systematic review and meta-analysis will only use study-level data from public databases, thus formal ethical approval is not required. The results will be disseminated in the form of an official publication in a peer-reviewed journal and/or presentation at relevant conferences.

PROSPERO REGISTRATION NUMBER : CRD42019118784.

Cheng Dan, Liu Dianbo, Philpotts Lisa Liang, Turner Dana P, Houle Timothy T, Chen Lucy, Zhang Miaomiao, Yang Jianjun, Zhang Wei, Deng Hao

2019-Dec-11

artificial intelligence, facial expression, infant, machine learning, pain

General General

Mitral Annulus Segmentation using Deep Learning in 3D Transesophageal Echocardiography.

In IEEE journal of biomedical and health informatics ; h5-index 0.0

3D Transesophageal Echocardiography is an excellent tool for evaluating the mitral valve and is also well suited for guiding cardiac interventions. We introduce a fully automatic method for mitral annulus segmentation in 3D Transesophageal Echocardiography, which requires no manual input. One hundred eleven multi-frame 3D transesophageal echocardiography recordings were split into training, validation, and test sets. Each 3D recording was decomposed into a set of 2D planes, exploiting the symmetry around the centerline of the left ventricle. A deep 2D convolutional neural network was trained to predict the mitral annulus coordinates, and the predictions from neighboring planes were regularized by enforcing continuity around the annulus. Applying the final model and postprocessing to the test set data gave a mean error of 2.0 mm - with a standard deviation of 1.9 mm. Fully automatic segmentation of the mitral annulus can alleviate the need for manual interaction in the quantification of an array of mitral annular parameters and has the potential to eliminate inter-observer variability.

Andreassen Borge Solli, Veronesi Federico, Gerard Olivier, Solberg Anne H Schistad, Samset Eigil

2019-Dec-12

General General

Deep Learning for Fall Risk Assessment with Inertial Sensors: Utilizing Domain Knowledge in Spatio-Temporal Gait Parameters.

In IEEE journal of biomedical and health informatics ; h5-index 0.0

Fall risk assessment is essential to predict and prevent falls in geriatric populations, especially patients with life-long conditions like neurological disorders. Inertial sensor-based pervasive gait analysis systems have become viable means to facilitate continuous fall risk assessment in non-hospital settings. However, a gait analysis system is not sufficient to detect the characteristics leading to increased fall risk, and powerful inference models are required to detect the underlying factors specific to fall risk. Machine learning models and especially the recently proposed deep learning methods offer the needed predictive power. Deep neural networks have the potential to produce models that can operate directly on the raw data, thus alleviating the need for feature engineering. However, the domain knowledge inherent in the well-established spatio-temporal gait parameters are still valuable to help a model achieve high inference accuracies. In this study, we explore deep learning methods, specifically long short-term memory (LSTM) neural networks, for the problem of fall risk assessment. We utilize sequences of spatio-temporal gait parameters extracted by an inertial sensor-based gait analysis system as input features. To quantify the performance of the proposed approach, we compare it with more traditional machine learning methods. The proposed LSTM model, trained with a gait dataset collected from 60 neurological disorder patients, achieves a superior classification accuracy of 92.1% on a separate test dataset collected from 16 patients. This study serves as one of the first attempts to employ deep learning approaches in this domain and the results demonstrate their potential.

Tunca Can, Salur Gulustu, Ersoy Cem

2019-Dec-11

General General

Accurate Ambulatory Gait Analysis in Walking and Running Using Machine Learning Models§.

In IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society ; h5-index 0.0

Wearable sensors have been proposed as alternatives to traditional laboratory equipment for low-cost and portable real-time gait analysis in unconstrained environments. However, the moderate accuracy of these systems currently limits their widespread use. In this paper, we show that support vector regression (SVR) models can be used to extract accurate estimates of fundamental gait parameters (i.e., stride length, velocity and foot clearance), from custom-engineered instrumented insoles (SportSole) during walking and running tasks. Additionally, these learning-based models are robust to inter-subject variability, thereby making it unnecessary to collect subject-specific training data. Gait analysis was performed in N=14 healthy subjects during two separate sessions, each including 6-minute bouts of treadmill walking and running at different speeds (i.e., 85% and 11% of each subject's preferred speed). Gait metrics were simultaneously measured with the instrumented insoles and with reference laboratory equipment. SVR models yielded excellent intraclass correlation coefficients (ICC) in all the gait parameters analyzed. Percentage mean absolute errors (MAE%) in stride length, velocity, and foot clearance obtained with SVR models were 1.37%±0.49%, 1.23%±0.27%, and 2.08%±0.72% for walking, 2.59%±0.64%, 2.91%±0.85%, and 5.13%±1.52% for running, respectively. These findings provide evidence that machine learning regression is a promising new approach to improve the accuracy of wearable sensors for gait analysis.

Zhang Huanghe, Guo Yi, Zanotto Damiano

2019-Dec-10

General General

Deep Image-to-Video Adaptation and Fusion Networks for Action Recognition.

In IEEE transactions on image processing : a publication of the IEEE Signal Processing Society ; h5-index 0.0

Existing deep learning methods for action recognition in videos require a large number of labeled videos for training, which is labor-intensive and time-consuming. For the same action, the knowledge learned from different media types, e.g., videos and images, may be related and complementary. However, due to the domain shifts and heterogeneous feature representations between videos and images, the performance of classifiers trained on images may be dramatically degraded when directly deployed to videos. In this paper, we propose a novel method, named Deep Image-to-Video Adaptation and Fusion Networks (DIVAFN), to enhance action recognition in videos by transferring knowledge from images using video keyframes as a bridge. The DIVAFN is a unified deep learning model, which integrates domain-invariant representations learning and cross-modal feature fusion into a unified optimization framework. Specifically, we design an efficient cross-modal similarities metric to reduce the modality shift among images, keyframes and videos. Then, we adopt an autoencoder architecture, whose hidden layer is constrained to be the semantic representations of the action class names. In this way, when the autoencoder is adopted to project the learned features from different domains to the same space, more compact, informative and discriminative representations can be obtained. Finally, the concatenation of the learned semantic feature representations from these three autoencoders are used to train the classifier for action recognition in videos. Comprehensive experiments on four real-world datasets show that our method outperforms some state-of-the-art domain adaptation and action recognition methods.

Liu Yang, Lu Zhaoyang, Li Jing, Yang Tao, Yao Chao

2019-Dec-11

General General

Multi-Scale Deep Residual Learning-Based Single Image Haze Removal via Image Decomposition.

In IEEE transactions on image processing : a publication of the IEEE Signal Processing Society ; h5-index 0.0

Images/videos captured from outdoor visual devices are usually degraded by turbid media, such as haze, smoke, fog, rain, and snow. Haze is the most common one in outdoor scenes due to the atmosphere conditions. In this paper, a novel deep learning-based architecture (denoted by MSRL-DehazeNet) for single image haze removal relying on multi-scale residual learning (MSRL) and image decomposition is proposed. Instead of learning an end-to-end mapping between each pair of hazy image and its corresponding haze-free one adopted by most existing learningbased approaches, we reformulate the problem as restoration of the image base component. Based on the decomposition of a hazy image into the base and the detail components, haze removal (or dehazing) can be achieved by both of our multi-scale deep residual learning and our simplified U-Net learning only for mapping between hazy and haze-free base components, while the detail component is further enhanced via the other learned convolutional neural network (CNN). Moreover, benefited by the basic building block of our deep residual CNN architecture and our simplified UNet structure, the feature maps (produced by extracting structural and statistical features), and each previous layer can be fully preserved and fed into the next layer. Therefore, possible color distortion in the recovered image would be avoided. As a result, the final haze-removed (or dehazed) image is obtained by integrating the haze-removed base and the enhanced detail image components. Experimental results have demonstrated good effectiveness of the proposed framework, compared with state-ofthe-art approaches.

Yeh Chia-Hung, Huang Chih-Hsiang, Kang Li-Wei

2019-Dec-11

General General

Low-Rank Matrix Recovery via Modified Schatten-p Norm Minimization with Convergence Guarantees.

In IEEE transactions on image processing : a publication of the IEEE Signal Processing Society ; h5-index 0.0

In recent years, low-rank matrix recovery problems have attracted much attention in computer vision and machine learning. The corresponding rank minimization problems are both combinational and NP-hard in general, which are mainly solved by both nuclear norm and Schatten-p (0<p<1) norm based optimization algorithms. However, inspired by weighted nuclear norm and Schatten-p norm as the relaxations of rank function, the main merits of this work firstly provide a modified Schatten-p norm in the affine matrix rank minimization problem, denoted as the modified Schatten-p norm minimization (MSpNM). Secondly, its surrogate function is constructed and the equivalence relationship with the MSpNM is further achieved. Thirdly, the iterative singular value thresholding algorithm (ISVTA) is devised to optimize it, and its accelerated version, i.e., AISVTA, is also obtained to reduce the number of iterations through the well-known Nesterov's acceleration strategy. Most importantly, the convergence guarantees and their relationship with objective function, stationary point and variable sequence generated by the proposed algorithms are established under some specific assumptions, e.g., Kurdyka-Łojasiewicz (KŁ) property. Finally, numerical experiments demonstrate the effectiveness of the proposed algorithms in the matrix completion problem for image inpainting and recommender systems. It should be noted that the accelerated algorithm has a much faster convergence speed and a very close recovery precision when comparing with the proposed non-accelerated one.

Zhang Hengmin, Qian Jianjun, Zhang Bob, Yang Jian, Gong Chen, Wei Yang

2019-Dec-11

General General

EMS-Net: A Deep Learning Method for Autodetecting Epileptic Magnetoencephalography Spikes.

In IEEE transactions on medical imaging ; h5-index 74.0

Epilepsy is a neurological disorder characterized by sudden and unpredictable epileptic seizures, which incurs significant negative impacts on patients' physical, psychological and social health. A practical approach to assist with the clinical assessment and treatment planning for patients is to process magnetoencephalography (MEG) data to identify epileptogenic zones. As a widely accepted biomarker of epileptic foci, epileptic MEG spikes need to be precisely detected. Given that the visual inspection of spikes is time consuming, an automatic and efficient system with adequate accuracy for spike detection is valuable in clinical practice. However, current approaches for MEG spike autodetection are dependent on hand-engineered features. Here, we propose a novel multiview Epileptic MEG Spikes detection algorithm based on a deep learning Network (EMS-Net) to accurately and efficiently recognize the spike events from MEG raw data. The results of the leave-k-subject-out validation tests for multiple datasets (i.e., balanced and realistic datasets) showed that EMS-Net achieved state-of-the-art classification performance (i.e., accuracy: 91.82% -99.89%; precision: 91.90% -99.45%; sensitivity: 91.61% -99.53%; specificity: 91.60% -99.96%; f1 score: 91.70% -99.48%; and area under the curve: 0.9688 -0.9998).

Zheng Li, Liao Pan, Shen Luo, Sheng Jingwei, Teng Pengfei, Luan Guoming, Gao Jia-Hong

2019-Dec-10

General General

Recurrent Temporal Aggregation Framework for Deep Video Inpainting.

In IEEE transactions on pattern analysis and machine intelligence ; h5-index 127.0

Video inpainting aims to fill in spatio-temporal holes in videos with plausible content. Despite tremendous progress on deep learning-based inpainting of a single image, it is still challenging to extend these methods to video domain due to the additional time dimension. In this paper, we propose a recurrent temporal aggregation framework for fast deep video inpainting. In particular, we construct an encoder-decoder model, where the encoder takes multiple source frames which can provide visible pixels revealed from the scene dynamics. These hints are aggregated and fed into the decoder. We apply a recurrent feedback in an auto-regressive manner to enforce temporal consistency in the video results. We propose two architectural designs based on this framework. Our first model is a blind video decaptioning network (BVDNet) that is designed to automatically remove and inpaint text overlays in videos without any mask information. Our BVDNet wins the first place in the ECCV Chalearn 2018 LAP Inpainting Competition Track 2: Video Decaptioning. Second, we propose a network for more general video inpainting (VINet) to deal with more arbitrary and larger holes. Video results demonstrate the advantage of our framework compared to state-of-the-art methods both qualitatively and quantitatively.

Kim Dahun, Woo Sanghyun, Lee Joon-Young, Kweon In So

2019-Dec-11

General General

Deep learning for image-based large-flowered chrysanthemum cultivar recognition.

In Plant methods ; h5-index 0.0

Background : Cultivar recognition is a basic work in flower production, research, and commercial application. Chinese large-flowered chrysanthemum (Chrysanthemum × morifolium Ramat.) is miraculous because of its high ornamental value and rich cultural deposits. However, the complicated capitulum structure, various floret types and numerous cultivars hinder chrysanthemum cultivar recognition. Here, we explore how deep learning method can be applied to chrysanthemum cultivar recognition.

Results : We propose deep learning models with two networks VGG16 and ResNet50 to recognize large-flowered chrysanthemum. Dataset A comprising 14,000 images for 103 cultivars, and dataset B comprising 197 images from different years were collected. Dataset A was used to train the networks and determine the calibration accuracy (Top-5 rate of above 98%), and dataset B was used to evaluate the model generalization performance (Top-5 rate of above 78%). Moreover, gradient-weighted class activation mapping (Grad-CAM) visualization and feature clustering analysis were used to explore how the deep learning model recognizes chrysanthemum cultivars.

Conclusion : Deep learning method applied to cultivar recognition is a breakthrough in horticultural science with the advantages of strong recognition performance and high recognition speed. Inflorescence edge areas, disc floret areas, inflorescence colour and inflorescence shape may well be the key factors in model decision-making process, which are also critical in human decision-making.

Liu Zhilan, Wang Jue, Tian Ye, Dai Silan

2019

Chrysanthemum × morifolium Ramat., Deep learning, Grad-CAM, Image recognition

General General

Channel-Unet: A Spatial Channel-Wise Convolutional Neural Network for Liver and Tumors Segmentation.

In Frontiers in genetics ; h5-index 62.0

It is a challenge to automatically and accurately segment the liver and tumors in computed tomography (CT) images, as the problem of over-segmentation or under-segmentation often appears when the Hounsfield unit (Hu) of liver and tumors is close to the Hu of other tissues or background. In this paper, we propose the spatial channel-wise convolution, a convolutional operation along the direction of the channel of feature maps, to extract mapping relationship of spatial information between pixels, which facilitates learning the mapping relationship between pixels in the feature maps and distinguishing the tumors from the liver tissue. In addition, we put forward an iterative extending learning strategy, which optimizes the mapping relationship of spatial information between pixels at different scales and enables spatial channel-wise convolution to map the spatial information between pixels in high-level feature maps. Finally, we propose an end-to-end convolutional neural network called Channel-UNet, which takes UNet as the main structure of the network and adds spatial channel-wise convolution in each up-sampling and down-sampling module. The network can converge the optimized mapping relationship of spatial information between pixels extracted by spatial channel-wise convolution and information extracted by feature maps and realizes multi-scale information fusion. The proposed ChannelUNet is validated by the segmentation task on the 3Dircadb dataset. The Dice values of liver and tumors segmentation were 0.984 and 0.940, which is slightly superior to current best performance. Besides, compared with the current best method, the number of parameters of our method reduces by 25.7%, and the training time of our method reduces by 33.3%. The experimental results demonstrate the efficiency and high accuracy of Channel-UNet in liver and tumors segmentation in CT images.

Chen Yilong, Wang Kai, Liao Xiangyun, Qian Yinling, Wang Qiong, Yuan Zhiyong, Heng Pheng-Ann

2019

Channel-UNet, computed tomography, deep learning, liver and tumors segmentation, spatial channel-wise convolution

General General

Partial Least Squares Regression Performs Well in MRI-Based Individualized Estimations.

In Frontiers in neuroscience ; h5-index 72.0

Estimation of individuals' cognitive, behavioral and demographic (CBD) variables based on MRI has attracted much research interest in the past decade, and effective machine learning techniques are of great importance for these estimations. Partial least squares regression (PLSR) is an attractive machine learning technique that can accommodate both single- and multi-label learning in a simple framework, while its potential for MRI-based estimations of CBD variables remains to be explored. In this study, we systemically investigated the performance of PLSR in MRI-based estimations of individuals' CBD variables, especially its performance in simultaneous estimation of multiple CBD variables (multi-label learning). We performed the study on the dataset included in the HCP S1200 release. Resting state functional connections (RSFCs) were used as features, and a total of 10 CBD variables (e.g., age, gender, grip strength, and picture vocabulary) were estimated. The results showed that PLSR performed well in both single- and multi-label learning. In fact, the present estimations were better than those reported in literatures, as indicated by stronger correlations between the estimated and actual CBD variables, as well as high gender classification accuracy (97.8% in this study). Moreover, the RSFCs that contributed to the estimations exhibited strong correlations with the CBD variable estimated, that is, PLSR algorithm automatically selected the RSFCs closely related to one CBD variable to establish predictive models for the variable. Besides, the estimation accuracies based on RSFCs among 100, 200, and 300 regions of interest (ROIs) were higher than those based on RSFCs among 15, 25, and 50 ROIs; the estimation accuracies based on RSFCs evaluated using partial correlation were higher than those based on RSFCs evaluated using full correlation. In addition to the aforementioned virtues, PLSR is efficient in model training and testing, and it is simple and easy to use. Therefore, PLSR can be a favorable choice for future MRI-based estimations of CBD variables.

Chen Chen, Cao Xuyu, Tian Lixia

2019

Human Connectome Project, classification, machine learning, multi-label learning, partial correlation, regression, resting state fMRI, resting state functional connection

General General

Quantitative TLC-SERS detection of histamine in seafood with support vector machine analysis.

In Food control ; h5-index 0.0

Scombroid fish poisoning caused by histamine intoxication is one of the most prevalent allergies associated with seafood consumption in the United States. Typical symptoms range from mild itching up to fatal cardiovascular collapse seen in anaphylaxis. In this paper, we demonstrate rapid, sensitive, and quantitative detection of histamine in both artificially spoiled tuna solution and real spoiled tuna samples using thin layer chromatography in tandem with surface-enhanced Raman scattering (TLC-SERS) sensing methods, enabled by machine learning analysis based on support vector regression (SVR) after feature extraction with principal component analysis (PCA). The TLC plates used herein, which were made from commercial food-grade diatomaceous earth, served simultaneously as the stationary phase to separate histamine from the blended tuna meat and as ultra-sensitive SERS substrates to enhance the detection limit. Using a simple drop cast method to dispense gold colloidal nanoparticles onto the diatomaceous earth plate, we were able to directly detect histamine concentration in artificially spoiled tuna solution down to 10 ppm. Based on the TLC-SERS spectral data of real tuna samples spoiled at room temperature for 0 to 48 hours, we used the PCA-SVR quantitative model to achieve superior predictive performance exceling traditional partial least squares regression (PLSR) method. This work proves that diatomaceous earth based TLC-SERS technique combined with machine-learning analysis is a cost-effective, reliable, and accurate approach for on-site detection and quantification of seafood allergen to enhance food safety.

Tan Ailing, Zhao Yong, Sivashanmugan Kundan, Squire Kenneth, Wang Alan X

2019-Sep

Histamine, Machine Learning, Seafood allergen, Support vector regression, Surface-enhanced Raman scattering, Thin layer chromatography

General General

Machine-learning based patient classification using Hepatitis B virus full-length genome quasispecies from Asian and European cohorts.

In Scientific reports ; h5-index 158.0

Chronic infection with Hepatitis B virus (HBV) is a major risk factor for the development of advanced liver disease including fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). The relative contribution of virological factors to disease progression has not been fully defined and tools aiding the deconvolution of complex patient virus profiles is an unmet clinical need. Variable viral mutant signatures develop within individual patients due to the low-fidelity replication of the viral polymerase creating 'quasispecies' populations. Here we present the first comprehensive survey of the diversity of HBV quasispecies through ultra-deep sequencing of the complete HBV genome across two distinct European and Asian patient populations. Seroconversion to the HBV e antigen (HBeAg) represents a critical clinical waymark in infected individuals. Using a machine learning approach, a model was developed to determine the viral variants that accurately classify HBeAg status. Serial surveys of patient quasispecies populations and advanced analytics will facilitate clinical decision support for chronic HBV infection and direct therapeutic strategies through improved patient stratification.

Mueller-Breckenridge Alan J, Garcia-Alcalde Fernando, Wildum Steffen, Smits Saskia L, de Man Robert A, van Campenhout Margo J H, Brouwer Willem P, Niu Jianjun, Young John A T, Najera Isabel, Zhu Lina, Wu Daitze, Racek Tomas, Hundie Gadissa Bedada, Lin Yong, Boucher Charles A, van de Vijver David, Haagmans Bart L

2019-Dec-11

oncology Oncology

Genetic interactions and tissue specificity modulate the association of mutations with drug response.

In Molecular cancer therapeutics ; h5-index 0.0

In oncology, biomarkers are widely used to predict subgroups of patients that respond to a given drug. While clinical decisions often rely on single gene biomarkers, machine learning approaches tend to generate complex multi-gene biomarkers that are hard to interpret. Models predicting drug response based on multiple altered genes often assume that the effects of single alterations are independent. We asked whether the association of cancer driver mutations with drug response is modulated by other driver mutations or the tissue of origin. We developed an analytical framework based on linear regression to study interactions in pharmacogenomic data from two large cancer cell line panels. Starting from a model with only covariates, we included additional variables only if they significantly improved simpler models. This allows to systematically assess interactions in small, easily interpretable models. Our results show that including mutation-mutation interactions in drug response prediction models tends to improve model performance and robustness. For example, we found that TP53 mutations decrease sensitivity to BRAF inhibitors in BRAF mutated cell lines and patient tumors, suggesting a therapeutic benefit of combining inhibition of oncogenic BRAF with reactivation of the tumor suppressor TP53. Moreover, we identified tissue-specific mutation-drug associations and synthetic lethal triplets where the simultaneous mutation of two genes sensitizes cells to a drug. In summary, our interaction-based approach contributes to a holistic view on the determining factors of drug response.

Cramer Dina, Mazur Johanna, Espinosa Octavio, Schlesner Matthias, Hübschmann Daniel, Eils Roland, Staub Eike

2019-Dec-11

General General

The use of machine learning techniques in trauma-related disorders: a systematic review.

In Journal of psychiatric research ; h5-index 59.0

Establishing the diagnosis of trauma-related disorders such as Acute Stress Disorder (ASD) and Posttraumatic Stress Disorder (PTSD) have always been a challenge in clinical practice and in academic research, due to clinical and biological heterogeneity. Machine learning (ML) techniques can be applied to improve classification of disorders, to predict outcomes or to determine person-specific treatment selection. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with ASD or PTSD. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to May 2019. We found 806 abstracts and included 49 studies in our review. Most of the included studies used multiple levels of biological data to predict risk factors or to identify early symptoms related to PTSD. Other studies used ML classification techniques to distinguish individuals with ASD or PTSD from other psychiatric disorder or from trauma-exposed and healthy controls. We also found studies that attempted to define outcome profiles using clustering techniques and studies that assessed the relationship among symptoms using network analysis. Finally, we proposed a quality assessment in this review, evaluating methodological and technical features on machine learning studies. We concluded that etiologic and clinical heterogeneity of ASD/PTSD patients is suitable to machine learning techniques and a major challenge for the future is to use it in clinical practice for the benefit of patients in an individual level.

Ramos-Lima Luis Francisco, Waikamp Vitoria, Antonelli-Salgado Thyago, Passos Ives Calvalcante, Freitas Lucia Helena Machado

2019-Dec-06

Forecasting, Machine learning, Posttraumatic stress disorders, Psychological trauma

General General

Automatic digital ECG signal extraction and normal QRS recognition from real scene ECG images.

In Computer methods and programs in biomedicine ; h5-index 0.0

BACKGROUND AND OBJECTIVE : Electrocardiogram (ECG) is one of the most important tools for assessing cardiac function and detecting potential heart problems. However, most of the current ECG report records remain on the paper, which makes it difficult to preserve and analyze the data. Moreover, paper records could result in the loss significant data, which brings inconvenience to the subsequent clinical diagnosis or artificial intelligence-assisted heart health diagnosis. Taking digital pictures is an intuitive way of preserving these files and can be done simply using smartphones or any other devices with cameras. However, these real scene ECG images often have some image noise that hinders signal extraction. How to eliminate image noise and extract ECG binary image automatically from the noisy and low-quality real scene images of ECG reports is the first problem to be solved in this paper. Next, QRS recognition is implemented on the extracted binary images to determine key points of ECG signals. 1D digital ECG signal is also extracted for accessing the exact values of the extracted points. In light of these tasks, an automatic digital ECG signal extraction and normal QRS recognition from real scene ECG images is proposed in this paper.

METHODS : The normal QRS recognition approach for real scene ECG images in this paper consists of two steps: ECG binary image extraction from ECG images using a new two-layer hierarchical method, and the subsequent QRS recognition based on a novel feature-fusing method. ECG binary image extraction is implemented using sub-channel filters followed by an adaptive filtering algorithm. According to the ratio between pixel and real value of ECG binary image, 1D digital ECG signal is obtained. The normal QRS recognition includes three main steps: establishment of candidate point sets, feature fusion extraction, and QRS recognition. Two datasets are introduced for evaluation including a real scene ECG images dataset and the public Non-Invasive Fetal Electrocardiogram Database (FECG).

RESULTS : Through the experiment on real scene ECG image, the F1 score for Q, R, S detection is 0.841, 0.992, and 0.891, respectively. The evaluation on the public FECG dataset also proves the robustness of our algorithm, where F1 score for R is 0.992 (0.996 for thoracic lead) and 0.988 for thoracic S wave.

CONCLUSIONS : The proposed method in this article is a promising tool for automatically extracting digital ECG signals and detecting QRS complex in real scene ECG images with normal QRS.

Wang Shuang, Zhang Shugang, Li Zhen, Huang Lei, Wei Zhiqiang

2019-Nov-30

Adaptive Filter Algorithm (AFA), ECG image, ECG signal extraction, QRS recognition

General General

Prediction of anaerobic digestion performance and identification of critical operational parameters using machine learning algorithms.

In Bioresource technology ; h5-index 0.0

Machine learning has emerges as a novel method for model development and has potential to be used to predict and control the performance of anaerobic digesters. In this study, several machine learning algorithms were applied in regression and classification models on digestion performance to identify determinant operational parameters and predict methane production. In the regression models, k-nearest neighbors (KNN) algorithm demonstrates optimal prediction accuracy (root mean square error = 26.6, with the dataset range of 259.0-573.8), after narrowing prediction coverage by excluding extreme outliers from the validation set. In the classification models, logistic regression multiclass algorithm yields the best prediction accuracy of 0.73. Feature importance reveals that total carbon was the determinant operational parameter. These results demonstrate the great potential of using machine learning algorithms to predict anaerobic digestion performance.

Wang Luguang, Long Fei, Liao Wei, Liu Hong

2019-Dec-02

Anaerobic digestion, Machine learning, Methane production, Operational parameters, Prediction

General General

Soft detection of 5-day BOD with sparse matrix in city harbor water using deep learning techniques.

In Water research ; h5-index 0.0

To better control and manage harbor water quality is an important mission for coastal cities such as New York City (NYC). To achieve this, managers and governors need keep track of key quality indicators, such as temperature, pH, and dissolved oxygen. Among these, the Biochemical Oxygen Demand (BOD) over five days is a critical indicator that requires much time and effort to detect, causing great inconvenience in both academia and industry. Existing experimental and statistical methods cannot effectively solve the detection time problem or provide limited accuracy. Also, due to various human-made mistakes or facility issues, the data used for BOD detection and prediction contain many missing values, resulting in a sparse matrix. Few studies have addressed the sparse matrix problem while developing statistical detection methods. To address these gaps, we propose a deep learning based model that combines Deep Matrix Factorization (DMF) and Deep Neural Network (DNN). The model was able to solve the sparse matrix problem more intelligently and predict the BOD value more accurately. To test its effectiveness, we conducted a case study on the NYC harbor water, based on 32,323 water samples. The results showed that the proposed method achieved 11.54%-17.23% lower RMSE than conventional matrix completion methods, and 19.20%-25.16% lower RMSE than traditional machine learning algorithms.

Ma Jun, Ding Yuexiong, Cheng Jack C P, Jiang Feifeng, Xu Zherui

2019-Dec-04

Biochemical oxygen demand, Deep matrix factorization, Deep neural network, Harbor water, Sparse matrix

General General

Global exponential synchronization of delayed memristive neural networks with reaction-diffusion terms.

In Neural networks : the official journal of the International Neural Network Society ; h5-index 0.0

This paper investigates the global exponential synchronization problem of delayed memristive neural networks (MNNs) with reaction-diffusion terms. First, by utilizing the pinning control technique, two novel kinds of control methods are introduced to achieve synchronization of delayed MNNs with reaction-diffusion terms. Then, with the help of inequality techniques, pinning control technique, the drive-response concept and Lyapunov functional method, two sufficient conditions are obtained in the form of algebraic inequalities, which can be used for ensuring the exponential synchronization of the proposed delayed MNNs with reaction-diffusion terms. Moreover, the obtained results based on algebraic inequality complement and improve the previously known results. Finally, two illustrative examples are given to support the effectiveness and validity of the obtained theoretical results.

Cao Yanyi, Cao Yuting, Guo Zhenyuan, Huang Tingwen, Wen Shiping

2019-Nov-29

Delayed memristive neural networks, Global exponential synchronization, Pinning control technique, Reaction–diffusion terms

oncology Oncology

Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges.

In Cancer letters ; h5-index 85.0

Cancer is an aggressive disease with a low median survival rate. Ironically, the treatment process is long and very costly due to its high recurrence and mortality rates. Accurate early diagnosis and prognosis prediction of cancer are essential to enhance the patient's survival rate. Developments in statistics and computer engineering over the years have encouraged many scientists to apply computational methods such as multivariate statistical analysis to analyze the prognosis of the disease, and the accuracy of such analyses is significantly higher than that of empirical predictions. Furthermore, as artificial intelligence (AI), especially machine learning and deep learning, has found popular applications in clinical cancer research in recent years, cancer prediction performance has reached new heights. This article reviews the literature on the application of AI to cancer diagnosis and prognosis, and summarizes its advantages. We explore how AI assists cancer diagnosis and prognosis, specifically with regard to its unprecedented accuracy, which is even higher than that of general statistical applications in oncology. We also demonstrate ways in which these methods are advancing the field. Finally, opportunities and challenges in the clinical implementation of AI are discussed. Hence, this article provides a new perspective on how AI technology can help improve cancer diagnosis and prognosis, and continue improving human health in the future.

Huang Shigao, Yang Jie, Fong Simon, Zhao Qi

2019-Dec-09

Cancer diagnosis, Deep learning, Deep neural network, Machine learning, Prognosis prediction

General General

PreDSLpmo: a neural network-based prediction tool for functional annotation of lytic polysaccharide monooxygenases.

In Journal of biotechnology ; h5-index 0.0

Lytic polysaccharide monooxygenases (LPMOs), a family of copper-dependent oxidative enzymes, boost the degradation of polysaccharides such as cellulose, chitin, and others. While experimental methods are used to validate LPMO function, a computational method that can aid experimental methods and provide fast and accurate classification of sequences into LPMOs and its families would be an important step towards understanding the breadth of contributions these enzymes make in deconstruction of recalcitrant polysaccharides. In this study, we developed a machine learning-based tool called PreDSLpmo that employs two different approaches to functionally classify protein sequences into the major LPMO families (AA9 and AA10). The first approach uses a traditional neural network or multilayer percerptron-based approach, while the second employs bi-directional long short-term memory for sequence classification. Our method shows improvement in predictive power when compared with dbCAN2, an existing HMM-profile-based CAZyme predicting tool, on both validation and independent benchmark set.

Srivastava Pulkit Anupam, Hegg Eric L, Fox Brian G, Yennamalli Ragothaman M

2019-Dec-09

Lytic polysaccharide monooxygenases, long short-term memory, machine learning technique, neural network, proteome

Surgery Surgery

Expert-level Diagnosis of Nasal Polyps Using Deep Learning on Whole-slide Imaging.

In The Journal of allergy and clinical immunology ; h5-index 0.0

AICEP is the first use of deep learning in combination with WSI in nasal polyp diagnosis and treatment. It can improve the diagnosis and management of nasal polyps more quickly and accurately.

Wu Qingwu, Chen Jianning, Deng Huiyi, Ren Yong, Sun Yueqi, Wang Weihao, Yuan Lianxiong, Hong Haiyu, Zheng Rui, Kong Weifeng, Huang Xuekun, Huang Guifang, Wang Lunji, Zhang Yana, Han Lanqing, Yang Qintai

2019-Dec-09

CRSwNP, WSI, deep learning, eosinophils, pathological classification

General General

Novel anti-flavivirus drugs targeting the nucleolar distribution of core protein.

In Virology ; h5-index 53.0

The risk of infectious diseases caused by Flavivirus is increasing globally. Here, we developed a novel high-throughput screening (HTS) system to evaluate the inhibitory effects of compounds targeting the nuclear localization of the flavivirus core protein. We screened 4000 compounds based on their ability to inhibit the nuclear localization of the core protein, and identified over 20 compounds including inhibitors for cyclin dependent kinase and glycogen synthase kinase. The efficacy of the identified compounds to suppress viral growth was validated in a cell-based infection system. Remarkably, the nucleolus morphology was affected by the treatment with the compounds, suggesting that the nucleolus function is critical for viral propagation. The present HTS system provides a useful strategy for the identification of antivirals against flavivirus by targeting the nucleolar localization of the core protein.

Tokunaga Makoto, Miyamoto Yoichi, Suzuki Tatsuya, Otani Mayumi, Inuki Shinsuke, Esaki Tsuyoshi, Nagao Chioko, Mizuguchi Kenji, Ohno Hiroaki, Yoneda Yoshihiro, Okamoto Toru, Oka Masahiro, Matsuura Yoshiharu

2019-Nov-29

Core protein, Flavivirus, High-throughput screening, Nuclear transport, Nucleolus

General General

Human perception and biosignal-based identification of posed and spontaneous smiles.

In PloS one ; h5-index 176.0

Facial expressions are behavioural cues that represent an affective state. Because of this, they are an unobtrusive alternative to affective self-report. The perceptual identification of facial expressions can be performed automatically with technological assistance. Once the facial expressions have been identified, the interpretation is usually left to a field expert. However, facial expressions do not always represent the felt affect; they can also be a communication tool. Therefore, facial expression measurements are prone to the same biases as self-report. Hence, the automatic measurement of human affect should also make inferences on the nature of the facial expressions instead of describing facial movements only. We present two experiments designed to assess whether such automated inferential judgment could be advantageous. In particular, we investigated the differences between posed and spontaneous smiles. The aim of the first experiment was to elicit both types of expressions. In contrast to other studies, the temporal dynamics of the elicited posed expression were not constrained by the eliciting instruction. Electromyography (EMG) was used to automatically discriminate between them. Spontaneous smiles were found to differ from posed smiles in magnitude, onset time, and onset and offset speed independently of the producer's ethnicity. Agreement between the expression type and EMG-based automatic detection reached 94% accuracy. Finally, measurements of the agreement between human video coders showed that although agreement on perceptual labels is fairly good, the agreement worsens with inferential labels. A second experiment confirmed that a layperson's accuracy as regards distinguishing posed from spontaneous smiles is poor. Therefore, the automatic identification of inferential labels would be beneficial in terms of affective assessments and further research on this topic.

Perusquía-Hernández Monica, Ayabe-Kanamura Saho, Suzuki Kenji

2019

General General

The well-being profile (WB-Pro): Creating a theoretically based multidimensional measure of well-being to advance theory, research, policy, and practice.

In Psychological assessment ; h5-index 0.0

There is no universally agreed definition of well-being as a subjective experience, but Huppert and So (2013) adopted and systematically applied the definition of well-being as positive mental health-the opposite of the common mental disorders described in standard mental health classifications (e.g., Diagnostic and Statistical Manual of Mental Disorders). We extended their theoretical approach to include multi-item scales, using 2 waves of nationally representative U.S. adult samples to develop, test, and validate our multidimensional measure of well-being (WB-Pro). This resulted in a good-fitting a priori (48-item, 15-factor) model that was invariant over time, education, gender, and age; showed good reliability (coefficient αs .81-.93), test-retest correlation (.73-.85; M = .80), and convergent/discriminant validity based on a multitrait-multimethod analysis, and relations with demographic variables, selected psychological measures, and other multidimensional and purportedly unidimensional well-being measures. Further, we found that items from 2 widely used, purportedly unidimensional well-being measures loaded on different WB-Pro factors consistent with a priori predictions based on the WB-Pro factor structure, thereby calling into question their claimed unidimensionality and theoretical rationale. Because some applications require a short global measure, we used a machine-learning algorithm to construct 2 global well-being short versions (five- and 15-item forms) and tested these formative measures in relation to the full-form and validity criteria (to download short and long versions see https://ippe.acu.edu.au/research/research-instruments/wb-pro). The WB-Pro appears to be one of the most comprehensive measures of subjective well-being, based on a sound conceptual model and empirical support, with broad applicability for research and practice, as well as providing a framework for evaluating the breadth of other well-being measures. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Marsh Herbert W, Huppert Felicia A, Donald James N, Horwood Marcus S, Sahdra Baljinder K

2019-Dec-12

General General

Conditional Prediction of Ribonucleic Acid Secondary Structure Using Chemical Shifts.

In The journal of physical chemistry. B ; h5-index 0.0

Inspired by methods that utilize chemical-mapping data to guide secondary structure prediction, we sought to develop a framework for using assigned chemical shift data to guide RNA secondary structure prediction. We first used machine learning to develop classifiers which predict the base-pairing status of individual residues in an RNA based on their assigned chemical shifts. Then, we used these base-pairing status predictions as restraints to guide RNA folding algorithms. Our results showed that we could recover the correct secondary fold of most of the 108 RNAs in our data set with remarkable accuracy. Finally, we tested whether we could use the base-pairing status predictions that we obtained from assigned chemical shift data to conditionally predict the secondary structure of RNA. To achieve this, we attempted to model two distinct conformational states of the microRNA-20b (miR-20b) and the fluoride riboswitch using assigned chemical shifts that were available for both conformational states of each of these test RNAs. For both test cases, we found that by using the base-pairing status predictions that we obtained from assigned chemical shift data as folding restraints, we could generate structures that closely resembled the known structure of the two distinct states. A command-line tool for Chemical Shifts to Base-Pairing Status (CS2BPS) predictions in RNA has been incorporated into our CS2Structure Git repository and can be accessed via: https://github.com/atfrank/CS2Structure.

Zhang Kexin, Frank Aaron Terrence

2019-Dec-12

General General

Development of a prognostic composite cytokine signature based on the correlation with nivolumab clearance: translational PK/PD analysis in patients with renal cell carcinoma.

In Journal for immunotherapy of cancer ; h5-index 0.0

BACKGROUND : Although several therapeutic options for patients with renal cell carcinoma (RCC) have been approved over recent years, including immune checkpoint inhibitors, considerable need remains for molecular biomarkers to assess disease prognosis. The higher pharmacokinetic (PK) clearance of checkpoint inhibitors, such as the anti-programmed death-1 (PD-1) therapies nivolumab and pembrolizumab, has been shown to be associated with poor overall survival (OS) across several tumor types. However, determination of PK clearance requires the collection and analysis of post-treatment serum samples, limiting its utility as a prognostic biomarker. This report outlines a translational PK-pharmacodynamic (PD) methodology used to derive a baseline composite cytokine signature correlated with nivolumab clearance using data from three clinical trials in which nivolumab or everolimus was administered.

METHODS : Peripheral serum cytokine (PD) and nivolumab clearance (PK) data from patients with RCC were analyzed using a PK-PD machine-learning model. Nivolumab studies CheckMate 009 (NCT01358721) and CheckMate 025 (NCT01668784) (n = 480) were used for PK-PD analysis model development and cytokine feature selection (training dataset). Validation of the model and assessment of the prognostic value of the cytokine signature was performed using data from CheckMate 010 (NCT01354431) and the everolimus comparator arm of CheckMate 025 (test dataset; n = 453).

RESULTS : The PK-PD analysis found a robust association between the eight top-ranking model-selected baseline inflammatory cytokines and nivolumab clearance (area under the receiver operating characteristic curve = 0.7). The predicted clearance (high vs low) based on the cytokine signature was significantly associated with long-term OS (p < 0.01) across all three studies (training and test datasets). Furthermore, cytokines selected from the model development trials also correlated with OS of the everolimus comparator arm (p < 0.01), suggesting the prognostic nature of the composite cytokine signature for RCC.

CONCLUSIONS : Here, we report a PK-PD translational approach to identify a molecular prognostic biomarker signature based on the correlation with nivolumab clearance in patients with RCC. This composite biomarker signature may provide improved prognostic accuracy of long-term clinical outcome compared with individual cytokine features and could be used to ensure the balance of patient randomization in RCC clinical trials.

Wang Rui, Zheng Junying, Shao Xiao, Ishii Yuko, Roy Amit, Bello Akintunde, Lee Richard, Zhang Joshua, Wind-Rotolo Megan, Feng Yan

2019-Dec-11

Clearance, Composite signature, Cytokine, Nivolumab, Renal cell carcinoma, Translational PK/PD analysis

oncology Oncology

scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data.

In Genome biology ; h5-index 114.0

Single-cell RNA sequencing has enabled the characterization of highly specific cell types in many tissues, as well as both primary and stem cell-derived cell lines. An important facet of these studies is the ability to identify the transcriptional signatures that define a cell type or state. In theory, this information can be used to classify an individual cell based on its transcriptional profile. Here, we present scPred, a new generalizable method that is able to provide highly accurate classification of single cells, using a combination of unbiased feature selection from a reduced-dimension space, and machine-learning probability-based prediction method. We apply scPred to scRNA-seq data from pancreatic tissue, mononuclear cells, colorectal tumor biopsies, and circulating dendritic cells and show that scPred is able to classify individual cells with high accuracy. The generalized method is available at https://github.com/powellgenomicslab/scPred/.

Alquicira-Hernandez Jose, Sathe Anuja, Ji Hanlee P, Nguyen Quan, Powell Joseph E

2019-Dec-12

Public Health Public Health

Prediction of health care expenditure increase: how does pharmacotherapy contribute?

In BMC health services research ; h5-index 0.0

BACKGROUND : Rising health care costs are a major public health issue. Thus, accurately predicting future costs and understanding which factors contribute to increases in health care expenditures are important. The objective of this project was to predict patients healthcare costs development in the subsequent year and to identify factors contributing to this prediction, with a particular focus on the role of pharmacotherapy.

METHODS : We used 2014-2015 Swiss health insurance claims data on 373'264 adult patients to classify individuals' changes in health care costs. We performed extensive feature generation and developed predictive models using logistic regression, boosted decision trees and neural networks. Based on the decision tree model, we performed a detailed feature importance analysis and subgroup analysis, with an emphasis on drug classes.

RESULTS : The boosted decision tree model achieved an overall accuracy of 67.6% and an area under the curve-score of 0.74; the neural network and logistic regression models performed 0.4 and 1.9% worse, respectively. Feature engineering played a key role in capturing temporal patterns in the data. The number of features was reduced from 747 to 36 with only a 0.5% loss in the accuracy. In addition to hospitalisation and outpatient physician visits, 6 drug classes and the mode of drug administration were among the most important features. Patient subgroups with a high probability of increase (up to 88%) and decrease (up to 92%) were identified.

CONCLUSIONS : Pharmacotherapy provides important information for predicting cost increases in the total population. Moreover, its relative importance increases in combination with other features, including health care utilisation.

Jödicke Annika M, Zellweger Urs, Tomka Ivan T, Neuer Thomas, Curkovic Ivanka, Roos Malgorzata, Kullak-Ublick Gerd A, Sargsyan Hayk, Egbring Marco

2019-Dec-11

Boosted decision tree, Claims data, Health care costs, Health care utilisation, Machine learning, Neural network, Pharmacology

General General

Comparative analysis of computer-vision and BLE technology based indoor navigation systems for people with visual impairments.

In International journal of health geographics ; h5-index 0.0

BACKGROUND : Considerable number of indoor navigation systems has been proposed to augment people with visual impairments (VI) about their surroundings. These systems leverage several technologies, such as computer-vision, Bluetooth low energy (BLE), and other techniques to estimate the position of a user in indoor areas. Computer-vision based systems use several techniques including matching pictures, classifying captured images, recognizing visual objects or visual markers. BLE based system utilizes BLE beacons attached in the indoor areas as the source of the radio frequency signal to localize the position of the user.

METHODS : In this paper, we examine the performance and usability of two computer-vision based systems and BLE-based system. The first system is computer-vision based system, called CamNav that uses a trained deep learning model to recognize locations, and the second system, called QRNav, that utilizes visual markers (QR codes) to determine locations. A field test with 10 blindfolded users has been conducted while using the three navigation systems.

RESULTS : The obtained results from navigation experiment and feedback from blindfolded users show that QRNav and CamNav system is more efficient than BLE based system in terms of accuracy and usability. The error occurred in BLE based application is more than 30% compared to computer vision based systems including CamNav and QRNav.

CONCLUSIONS : The developed navigation systems are able to provide reliable assistance for the participants during real time experiments. Some of the participants took minimal external assistance while moving through the junctions in the corridor areas. Computer vision technology demonstrated its superiority over BLE technology in assistive systems for people with visual impairments.

Kunhoth Jayakanth, Karkar AbdelGhani, Al-Maadeed Somaya, Al-Attiyah Asma

2019-Dec-11

Computer vision, Indoor navigation, Mobile technology, People with visual impairments

General General

Medical education trends for future physicians in the era of advanced technology and artificial intelligence: an integrative review.

In BMC medical education ; h5-index 0.0

BACKGROUND : Medical education must adapt to different health care contexts, including digitalized health care systems and a digital generation of students in a hyper-connected world. The aims of this study are to identify and synthesize the values that medical educators need to implement in the curricula and to introduce representative educational programs.

METHODS : An integrative review was conducted to combine data from various research designs. We searched for articles on PubMed, Scopus, Web of Science, and EBSCO ERIC between 2011 and 2017. Key search terms were "undergraduate medical education," "future," "twenty-first century," "millennium," "curriculum," "teaching," "learning," and "assessment." We screened and extracted them according to inclusion and exclusion criteria from titles and abstracts. All authors read the full texts and discussed them to reach a consensus about the themes and subthemes. Data appraisal was performed using a modified Hawker 's evaluation form.

RESULTS : Among the 7616 abstracts initially identified, 28 full-text articles were selected to reflect medical education trends and suggest suitable educational programs. The integrative themes and subthemes of future medical education are as follows: 1) a humanistic approach to patient safety that involves encouraging humanistic doctors and facilitating collaboration; 2) early experience and longitudinal integration by early exposure to patient-oriented integration and longitudinal integrated clerkships; 3) going beyond hospitals toward society by responding to changing community needs and showing respect for diversity; and 4) student-driven learning with advanced technology through active learning with individualization, social interaction, and resource accessibility.

CONCLUSIONS : This review integrated the trends in undergraduate medical education in readiness for the anticipated changes in medical environments. The detailed programs introduced in this study could be useful for medical educators in the development of curricula. Further research is required to integrate the educational trends into graduate and continuing medical education, and to investigate the status or effects of innovative educational programs in each medical school or environment.

Han Eui-Ryoung, Yeo Sanghee, Kim Min-Jeong, Lee Young-Hee, Park Kwi-Hwa, Roh Hyerin

2019-Dec-11

Humanities, Integration, Self-directed learning, Societies, Technology, Undergraduate medical education

General General

Analysis of disease comorbidity patterns in a large-scale China population.

In BMC medical genomics ; h5-index 0.0

BACKGROUND : Disease comorbidity is popular and has significant indications for disease progress and management. We aim to detect the general disease comorbidity patterns in Chinese populations using a large-scale clinical data set.

METHODS : We extracted the diseases from a large-scale anonymized data set derived from 8,572,137 inpatients in 453 hospitals across China. We built a Disease Comorbidity Network (DCN) using correlation analysis and detected the topological patterns of disease comorbidity using both complex network and data mining methods. The comorbidity patterns were further validated by shared molecular mechanisms using disease-gene associations and pathways. To predict the disease occurrence during the whole disease progressions, we applied four machine learning methods to model the disease trajectories of patients.

RESULTS : We obtained the DCN with 5702 nodes and 258,535 edges, which shows a power law distribution of the degree and weight. It further indicated that there exists high heterogeneity of comorbidities for different diseases and we found that the DCN is a hierarchical modular network with community structures, which have both homogeneous and heterogeneous disease categories. Furthermore, adhering to the previous work from US and Europe populations, we found that the disease comorbidities have their shared underlying molecular mechanisms. Furthermore, take hypertension and psychiatric disease as instance, we used four classification methods to predicte the disease occurrence using the comorbid disease trajectories and obtained acceptable performance, in which in particular, random forest obtained an overall best performance (with F1-score 0.6689 for hypertension and 0.6802 for psychiatric disease).

CONCLUSIONS : Our study indicates that disease comorbidity is significant and valuable to understand the disease incidences and their interactions in real-world populations, which will provide important insights for detection of the patterns of disease classification, diagnosis and prognosis.

Guo Mengfei, Yu Yanan, Wen Tiancai, Zhang Xiaoping, Liu Baoyan, Zhang Jin, Zhang Runshun, Zhang Yanning, Zhou Xuezhong

2019-Dec-12

Complex network, Disease comorbidity, Network medicine

General General

Time-resolved evaluation of compound repositioning predictions on a text-mined knowledge network.

In BMC bioinformatics ; h5-index 0.0

BACKGROUND : Computational compound repositioning has the potential for identifying new uses for existing drugs, and new algorithms and data source aggregation strategies provide ever-improving results via in silico metrics. However, even with these advances, the number of compounds successfully repositioned via computational screening remains low. New strategies for algorithm evaluation that more accurately reflect the repositioning potential of a compound could provide a better target for future optimizations.

RESULTS : Using a text-mined database, we applied a previously described network-based computational repositioning algorithm, yielding strong results via cross-validation, averaging 0.95 AUROC on test-set indications. However, to better approximate a real-world scenario, we built a time-resolved evaluation framework. At various time points, we built networks corresponding to prior knowledge for use as a training set, and then predicted on a test set comprised of indications that were subsequently described. This framework showed a marked reduction in performance, peaking in performance metrics with the 1985 network at an AUROC of .797. Examining performance reductions due to removal of specific types of relationships highlighted the importance of drug-drug and disease-disease similarity metrics. Using data from future timepoints, we demonstrate that further acquisition of these kinds of data may help improve computational results.

CONCLUSIONS : Evaluating a repositioning algorithm using indications unknown to input network better tunes its ability to find emerging drug indications, rather than finding those which have been randomly withheld. Focusing efforts on improving algorithmic performance in a time-resolved paradigm may further improve computational repositioning predictions.

Mayers Michael, Li Tong Shu, Queralt-Rosinach Núria, Su Andrew I

2019-Dec-11

Compound repositioning, Drug central, Heterogeneous network, Machine learning, Semantic Medline database, Semantic network, Unified medical language system

General General

Updating and reasoning: Different processes, different models, different functions.

In The Behavioral and brain sciences ; h5-index 0.0

Two issues should be addressed to refine and extend the distinction between temporal updating and reasoning advocated by Hoerl & McCormack. First, do the mental representations constructed during updating differ from those used for reasoning? Second, are updating and reasoning the only two processes relevant to temporal thinking? If not, is a dual-systems framework sensible? We address both issues below.

Kelly Laura, Prabhakar Janani, Khemlani Sangeet

2019-12-12

General General

A pan-cancer somatic mutation embedding using autoencoders.

In BMC bioinformatics ; h5-index 0.0

BACKGROUND : Next generation sequencing instruments are providing new opportunities for comprehensive analyses of cancer genomes. The increasing availability of tumor data allows to research the complexity of cancer disease with machine learning methods. The large available repositories of high dimensional tumor samples characterised with germline and somatic mutation data requires advance computational modelling for data interpretation. In this work, we propose to analyze this complex data with neural network learning, a methodology that made impressive advances in image and natural language processing.

RESULTS : Here we present a tumor mutation profile analysis pipeline based on an autoencoder model, which is used to discover better representations of lower dimensionality from large somatic mutation data of 40 different tumor types and subtypes. Kernel learning with hierarchical cluster analysis are used to assess the quality of the learned somatic mutation embedding, on which support vector machine models are used to accurately classify tumor subtypes.

CONCLUSIONS : The learned latent space maps the original samples in a much lower dimension while keeping the biological signals from the original tumor samples. This pipeline and the resulting embedding allows an easier exploration of the heterogeneity within and across tumor types and to perform an accurate classification of tumor samples in the pan-cancer somatic mutation landscape.

Palazzo Martin, Beauseroy Pierre, Yankilevich Patricio

2019-Dec-11

Autoencoder, Cancer genomics, Kernel learning

General General

Effective machine-learning assembly for next-generation amplicon sequencing with very low coverage.

In BMC bioinformatics ; h5-index 0.0

BACKGROUND : In short-read DNA sequencing experiments, the read coverage is a key parameter to successfully assemble the reads and reconstruct the sequence of the input DNA. When coverage is very low, the original sequence reconstruction from the reads can be difficult because of the occurrence of uncovered gaps. Reference guided assembly can then improve these assemblies. However, when the available reference is phylogenetically distant from the sequencing reads, the mapping rate of the reads can be extremely low. Some recent improvements in read mapping approaches aim at modifying the reference according to the reads dynamically. Such approaches can significantly improve the alignment rate of the reads onto distant references but the processing of insertions and deletions remains challenging.

RESULTS : Here, we introduce a new algorithm to update the reference sequence according to previously aligned reads. Substitutions, insertions and deletions are performed in the reference sequence dynamically. We evaluate this approach to assemble a western-grey kangaroo mitochondrial amplicon. Our results show that more reads can be aligned and that this method produces assemblies of length comparable to the truth while limiting error rate when classic approaches fail to recover the correct length. Finally, we discuss how the core algorithm of this method could be improved and combined with other approaches to analyse larger genomic sequences.

CONCLUSIONS : We introduced an algorithm to perform dynamic alignment of reads on a distant reference. We showed that such approach can improve the reconstruction of an amplicon compared to classically used bioinformatic pipelines. Although not portable to genomic scale in the current form, we suggested several improvements to be investigated to make this method more flexible and allow dynamic alignment to be used for large genome assemblies.

Ranjard Louis, Wong Thomas K F, Rodrigo Allen G

2019-Dec-11

Amplicon, Assembly, Machine learning, Mitochondrion, Western-grey kangaroo

Internal Medicine Internal Medicine

Mortality prediction models in the adult critically ill: A scoping review.

In Acta anaesthesiologica Scandinavica ; h5-index 37.0

BACKGROUND : Mortality prediction models are applied in the Intensive Care Unit (ICU) to stratify patients into different risk categories and to facilitate benchmarking. To ensure that the correct prediction models are applied for these purposes, the best performing models must be identified. As a first step, we aimed to establish a systematic review of mortality prediction models in critically ill patients.

METHODS : Mortality prediction models were searched in four databases using the following criteria: developed for use in adult ICU patients in high-income countries, with mortality as primary or secondary outcome. Characteristics and performance measures of the models were summarized. Performance was presented in terms of discrimination, calibration and overall performance measures presented in the original publication.

RESULTS : In total, 43 mortality prediction models were included in the final analysis. Fifteen models were only internally validated (35%), 13 externally (30%) and 10 (23%) were both internally and externally validated by the original researchers. Discrimination was assessed in 42 models (98%). Commonly used calibration measures were the Hosmer-Lemeshow test (60%) and the calibration plot (28%). Calibration was not assessed in 11 models (26%). Overall performance was assessed in the Brier score (19%) and the Nagelkerke's R2 (4.7%).

CONCLUSIONS : Mortality prediction models have varying methodology, and validation and performance of individual models differ. External validation by the original researchers is often lacking and head-to-head comparisons are urgently needed to identify the best performing mortality prediction models for guiding clinical care and research in different settings and populations.

Keuning Britt E, Kaufmann Thomas, Wiersema Renske, Granholm Anders, Pettilä Ville, Møller Morten Hylander, Christiansen Christian Fynbo, Castela Forte José, Snieder Harold, Keus Frederik, Pleijhuis Rick G, van der Horst Iwan Cc

2019-Dec-12

Intensive Care Unit, critical care, mortality prediction model, performance, risk prediction, scoping review

General General

From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices.

In Science and engineering ethics ; h5-index 0.0

The debate about the ethical implications of Artificial Intelligence dates from the 1960s (Samuel in Science, 132(3429):741-742, 1960. https://doi.org/10.1126/science.132.3429.741; Wiener in Cybernetics: or control and communication in the animal and the machine, MIT Press, New York, 1961). However, in recent years symbolic AI has been complemented and sometimes replaced by (Deep) Neural Networks and Machine Learning (ML) techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such a debate has primarily focused on principles-the 'what' of AI ethics (beneficence, non-maleficence, autonomy, justice and explicability)-rather than on practices, the 'how.' Awareness of the potential issues is increasing at a fast rate, but the AI community's ability to take action to mitigate the associated risks is still at its infancy. Our intention in presenting this research is to contribute to closing the gap between principles and practices by constructing a typology that may help practically-minded developers apply ethics at each stage of the Machine Learning development pipeline, and to signal to researchers where further work is needed. The focus is exclusively on Machine Learning, but it is hoped that the results of this research may be easily applicable to other branches of AI. The article outlines the research method for creating this typology, the initial findings, and provides a summary of future research needs.

Morley Jessica, Floridi Luciano, Kinsey Libby, Elhalal Anat

2019-Dec-11

Applied ethics, Artificial intelligence, Data governance, Digital ethics, Ethics of AI, Governance, Machine learning

General General

A Mobile-Based Screening System for Data Analyses of Early Dementia Traits Detection.

In Journal of medical systems ; h5-index 48.0

Existing early detection methods that deal with the pre-diagnosis of dementia have been criticised as not being comprehensive as they do not measure certain cognitive functioning domains besides being inaccessible. A more realistic approach is to develop a comprehensive outcome that includes cognitive functioning of dementia, as this will offer a robust and unbiased outcome for an individual. In this research, a mobile screening application for dementia traits called DementiaTest is proposed, which adopts the gold standard assessment criteria of Diagnostic and Statistical Manual of Mental Disorders (DSM-V). DementiaTest is implemented and tested on the Android and IOS stores. More importantly, it collects data from cases and controls using an easy, interactive, and accessible platform. It provides patients and their family with quick pre-diagnostic reports using certain cognitive functioning indicators; these can be utilized by general practitioners (GPs) for referrals for further assessment in cases of positive outcomes. The data gathered using the new application can be analysed using Artificial Intelligence methods to evaluate the performance of the screening to pinpoint early signs of the dementia.

Thabtah Fadi, Mampusti Ella, Peebles David, Herradura Raymund, Varghese Jithin

2019-Dec-11

Accessibility, Cognitive impairment, Data analyses, Dementia, Health informatics, Mobile application, Mobile health

oncology Oncology

Kidney edge detection in laparoscopic image data for computer-assisted surgery : Kidney edge detection.

In International journal of computer assisted radiology and surgery ; h5-index 0.0

PURPOSE : In robotic-assisted kidney surgery, computational methods make it possible to augment the surgical scene and potentially improve patient outcome. Most often, soft-tissue registration is a prerequisite for the visualization of tumors and vascular structures hidden beneath the surface. State-of-the-art volume-to-surface registration methods, however, are computationally demanding and require a sufficiently large target surface. To overcome this limitation, the first step toward registration is the extraction of the outer edge of the kidney.

METHODS : To tackle this task, we propose a deep learning-based solution. Rather than working only on the raw laparoscopic images, the network is given depth information and distance fields to predict whether a pixel of the image belongs to an edge. We evaluate our method on expert-labeled in vivo data from the EndoVis sub-challenge 2017 Kidney Boundary Detection and define the current state of the art.

RESULTS : By using a leave-one-out cross-validation, we report results for the most suitable network with a median precision-like, recall-like, and intersection over union (IOU) of 39.5 px, 143.3 px, and 0.3, respectively.

CONCLUSION : We conclude that our approach succeeds in predicting the edges of the kidney, except in instances where high occlusion occurs, which explains the average decrease in the IOU score. All source code, reference data, models, and evaluation results are openly available for download: https://github.com/ghattab/kidney-edge-detection/.

Hattab Georges, Arnold Marvin, Strenger Leon, Allan Max, Arsentjeva Darja, Gold Oliver, Simpfendörfer Tobias, Maier-Hein Lena, Speidel Stefanie

2019-Dec-11

Boundary, Deep learning, Edge, Kidney, Segmentation

Surgery Surgery

Preoperative prediction of tumour deposits in rectal cancer by an artificial neural network-based US radiomics model.

In European radiology ; h5-index 62.0

OBJECTIVE : To develop a machine learning-based ultrasound (US) radiomics model for predicting tumour deposits (TDs) preoperatively.

METHODS : From December 2015 to December 2017, 127 patients with rectal cancer were prospectively enrolled and divided into training and validation sets. Endorectal ultrasound (ERUS) and shear-wave elastography (SWE) examinations were conducted for each patient. A total of 4176 US radiomics features were extracted for each patient. After the reduction and selection of US radiomics features , a predictive model using an artificial neural network (ANN) was constructed in the training set. Furthermore, two models (one incorporating clinical information and one based on MRI radiomics) were developed. These models were validated by assessing their diagnostic performance and comparing the areas under the curve (AUCs) in the validation set.

RESULTS : The training and validation sets included 29 (33.3%) and 11 (27.5%) patients with TDs, respectively. A US radiomics ANN model was constructed. The model for predicting TDs showed an accuracy of 75.0% in the validation cohort. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and AUC were 72.7%, 75.9%, 53.3%, 88.0% and 0.743, respectively. For the model incorporating clinical information, the AUC improved to 0.795. Although the AUC of the US radiomics model was improved compared with that of the MRI radiomics model (0.916 vs. 0.872) in the 90 patients with both ultrasound and MRI data (which included both the training and validation sets), the difference was nonsignificant (p = 0.384).

CONCLUSIONS : US radiomics may be a potential model to accurately predict TDs before therapy.

KEY POINTS : • We prospectively developed an artificial neural network model for predicting tumour deposits based on US radiomics that had an accuracy of 75.0%. • The area under the curve of the US radiomics model was improved than that of the MRI radiomics model (0.916 vs. 0.872), but the difference was not significant (p = 0.384). • The US radiomics-based model may potentially predict TDs accurately before therapy, but this model needs further validation with larger samples.

Chen Li-Da, Li Wei, Xian Meng-Fei, Zheng Xin, Lin Yuan, Liu Bao-Xian, Lin Man-Xia, Li Xin, Zheng Yan-Ling, Xie Xiao-Yan, Lu Ming-De, Kuang Ming, Xu Jian-Bo, Wang Wei

2019-Dec-11

Machine learning, Rectal neoplasms, Ultrasonography

Radiology Radiology

Diffusion- and perfusion-weighted MRI radiomics model may predict isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade glioma.

In European radiology ; h5-index 62.0

OBJECTIVES : To determine whether diffusion- and perfusion-weighted MRI-based radiomics features can improve prediction of isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in lower grade gliomas (LGGs) METHODS: Radiomics features (n = 6472) were extracted from multiparametric MRI including conventional MRI, apparent diffusion coefficient (ADC), and normalized cerebral blood volume, acquired on 127 LGG patients with determined IDH mutation status and grade (WHO II or III). Radiomics models were constructed using machine learning-based feature selection and generalized linear model classifiers. Segmentation stability was calculated between two readers using concordance correlation coefficients (CCCs). Diagnostic performance to predict IDH mutation and tumor grade was compared between the multiparametric and conventional MRI radiomics models using the area under the receiver operating characteristics curve (AUC). The models were tested using a temporally independent validation set (n = 28).

RESULTS : The multiparametric MRI radiomics model was optimized with a random forest feature selector, with segmentation stability of a CCC threshold of 0.8. For IDH mutation, multiparametric MR radiomics showed similar performance (AUC 0.795) to the conventional radiomics model (AUC 0.729). In tumor grading, multiparametric model with ADC features showed higher performance (AUC 0.932) than the conventional model (AUC 0.555). The independent validation set showed the same trend with AUCs of 0.747 for IDH prediction and 0.819 for tumor grading with multiparametric MRI radiomics model.

CONCLUSION : Multiparametric MRI radiomics model showed improved diagnostic performance in tumor grading and comparable diagnostic performance in IDH mutation status, with ADC features playing a significant role.

KEY POINTS : • The multiparametric MRI radiomics model was comparable with conventional MRI radiomics model in predicting IDH mutation. • The multiparametric MRI radiomics model outperformed conventional MRI in glioma grading. • Apparent diffusion coefficient played an important role in glioma grading and predicting IDH mutation status using radiomics.

Kim Minjae, Jung So Yeong, Park Ji Eun, Jo Yeongheun, Park Seo Young, Nam Soo Jung, Kim Jeong Hoon, Kim Ho Sung

2019-Dec-11

Glioma, Isocitrate dehydrogenase, Machine learning, Magnetic resonance imaging, Neoplasm grading

Radiology Radiology

Radiomics allows for detection of benign and malignant histopathology in patients with metastatic testicular germ cell tumors prior to post-chemotherapy retroperitoneal lymph node dissection.

In European radiology ; h5-index 62.0

OBJECTIVES : To evaluate whether a computed tomography (CT) radiomics-based machine learning classifier can predict histopathology of lymph nodes (LNs) after post-chemotherapy LN dissection (pcRPLND) in patients with metastatic non-seminomatous testicular germ cell tumors (NSTGCTs).

METHODS : Eighty patients with retroperitoneal LN metastases and contrast-enhanced CT were included into this retrospective study. Resected LNs were histopathologically classified into "benign" (necrosis/fibrosis) or "malignant" (viable tumor/teratoma). On CT imaging, 204 corresponding LNs were segmented and 97 radiomic features per LN were extracted after standardized image processing. The dataset was split into training, test, and validation sets. After stepwise feature reduction based on reproducibility, variable importance, and correlation analyses, a gradient-boosted tree was trained and tuned on the selected most important features using the training and test datasets. Model validation was performed on the independent validation dataset.

RESULTS : The trained machine learning classifier achieved a classification accuracy of 0.81 in the validation dataset with a misclassification of 8 of 36 benign LNs as malignant and 4 of 25 malignant LNs as benign (sensitivity 88%, specificity 72%, negative predictive value 88%). In contrast, a model containing only the LN volume resulted in a classification accuracy of 0.68 with 64% sensitivity and 68% specificity.

CONCLUSIONS : CT radiomics represents an exciting new tool for improved prediction of the presence of malignant histopathology in retroperitoneal LN metastases from NSTGCTs, aiming at reducing overtreatment in this group of young patients. Thus, the presented approach should be combined with established clinical biomarkers and further validated in larger, prospective clinical trials.

KEY POINTS : • Patients with metastatic non-seminomatous testicular germ cell tumors undergoing post-chemotherapy retroperitoneal lymph node dissection of residual lesions show overtreatment in up to 50%. • We assessed whether a CT radiomics-based machine learning classifier can predict histopathology of lymph nodes after post-chemotherapy lymph node dissection. • The trained machine learning classifier achieved a classification accuracy of 0.81 in the validation dataset with a sensitivity of 88% and a specificity of 78%, thus allowing for prediction of the presence of viable tumor or teratoma in retroperitoneal lymph node metastases.

Baessler Bettina, Nestler Tim, Pinto Dos Santos Daniel, Paffenholz Pia, Zeuch Vikram, Pfister David, Maintz David, Heidenreich Axel

2019-Dec-11

Lymph nodes, Testicular neoplasms, Tomography

Radiology Radiology

[Artificial Intelligence in radiology : What can be expected in the next few years?]

In Der Radiologe ; h5-index 0.0

CLINICAL/METHODOLOGICAL ISSUE : Artificial intelligence (AI) is being increasingly used in the field of radiology. The aim of this review is to illustrate the developments expected in the next 5 to 10 years as well as possible advantages and risks.

STANDARD RADIOLOGICAL METHODS : Currently, all computed tomography (CT) images are reconstructed using programmed algorithms. Pathologies are detected by the radiologist with a high expenditure of time and evaluated using standardized procedures.

METHODOLOGICAL INNOVATIONS : AI can potentially provide a significant improvement to all these standard procedures in the future. CT reconstructions can be significantly enhanced using generative adversarial networks (GAN). Histology can be evaluated using radiomics or deep learning (DL)-based image analysis and the prognosis of the patient can be predicted highly individualized.

PERFORMANCE : The performance of the networks is strongly influenced by data quality and requires extensive validation. The ability and willingness of the manufacturers to integrate these into the existing RIS/PACS systems is also decisive.

EVALUATION : AI will have a large impact on the daily clinical work of radiologists. However, publications on the risks of the technology and on adequate validation are still lacking. In addition to opening new fields of application, further research regarding possible risks is warranted.

PRACTICAL RECOMMENDATIONS : In the next 5 to 10 years, AI will improve and facilitate work in clinical practice. The integration of the applications into the existing RIS/PACS systems is expected to take place via app stores and/or existing teleradiology networks.

Haubold Johannes

2019-Dec-11

Deep learning, Image analysis, Radiomics, Risks, Validation

General General

Computational prediction of implantation outcome after embryo transfer.

In Health informatics journal ; h5-index 25.0

The aim of this study is to develop a computational prediction model for implantation outcome after an embryo transfer cycle. In this study, information of 500 patients and 1360 transferred embryos, including cleavage and blastocyst stages and fresh or frozen embryos, from April 2016 to February 2018, were collected. The dataset containing 82 attributes and a target label (indicating positive and negative implantation outcomes) was constructed. Six dominant machine learning approaches were examined based on their performance to predict embryo transfer outcomes. Also, feature selection procedures were used to identify effective predictive factors and recruited to determine the optimum number of features based on classifiers performance. The results revealed that random forest was the best classifier (accuracy = 90.40% and area under the curve = 93.74%) with optimum features based on a 10-fold cross-validation test. According to the Support Vector Machine-Feature Selection algorithm, the ideal numbers of features are 78. Follicle stimulating hormone/human menopausal gonadotropin dosage for ovarian stimulation was the most important predictive factor across all examined embryo transfer features. The proposed machine learning-based prediction model could predict embryo transfer outcome and implantation of embryos with high accuracy, before the start of an embryo transfer cycle.

Raef Behnaz, Maleki Masoud, Ferdousi Reza

2019-Dec-12

assisted reproductive technology, embryo transfer, machine learning, prediction model, ranking algorithms

Radiology Radiology

Analysis of head CT scans flagged by deep learning software for acute intracranial hemorrhage.

In Neuroradiology ; h5-index 0.0

PURPOSE : To analyze the implementation of deep learning software for the detection and worklist prioritization of acute intracranial hemorrhage on non-contrast head CT (NCCT) in various clinical settings at an academic medical center.

METHODS : Urgent NCCT scans were reviewed by the Aidoc (Tel Aviv, Israel) neural network software. All cases flagged by the software as positive for acute intracranial hemorrhage on the neuroradiology worklist were prospectively included in this assessment. The scans were classified regarding presence and type of hemorrhage, whether these were initial or follow-up scans, and patient visit location, including trauma/emergency, inpatient, and outpatient departments.

RESULTS : During the 2 months of enrollment, 373 NCCT scans were flagged by the Aidoc software for possible intracranial hemorrhage out of 2011 scans analyzed (18.5%). Among the flagged cases, 275 (72.4%) were positive; 290 (77.7%) were inpatient cases, 75 (20.1%) were trauma/emergency cases, and eight (2.1%) were outpatient cases, and 229 of 373 (62.5%) were follow-up cases, of which 219 (95.6%) inpatient cases. Among the 144 new cases flagged for hemorrhage, 66 (44.4%) were positive, of which 39 (58.2%) were trauma/emergency cases. The overall sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 88.7%, 94.2% and 73.7%, 97.7%, and 93.4%, respectively. The accuracy of the intracranial hemorrhage detection was significantly higher for emergency cases than for inpatient cases (96.5% versus 89.4%).

CONCLUSION : This study reveals that the performance of the deep learning software for acute intracranial hemorrhage detection varies depending upon the patient visit location. Furthermore, a substantial portion of flagged cases were follow-up exams, the majority of which were inpatient exams. These findings can help optimize the artificial intelligence-driven clincical workflow.

Ginat Daniel T

2019-Dec-11

CT, Deep learning, Intracranial hemorrhage, Neural networks

General General

Time-series spectral dataset for croplands in France (2006-2017).

In Data in brief ; h5-index 0.0

Decadal time-series derived from satellite observations are useful for discriminating crops and identifying crop succession at national and regional scales. However, use of these data for crop modeling is challenged by the presence of mixed pixels due to the coarse spatial resolution of these data, which influences model accuracy, and the scarcity of field data over the decadal period necessary to calibrate and validate the model. For this data article, cloud-free satellite "Vegetation Indices 16-Day Global 250 m" Terra (MOD13Q1) and Aqua (MYD13Q1) products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), as well as the Land Parcel Information System (LPIS) vector field data, were collected throughout France for the 12-year period from 2006 to the end of 2017. A GIS workflow was developed using R software to combine the MOD13Q1 and MYD13Q1 products, and then to select "pure" MODIS pixels located within single-crop parcels over the entire period. As a result, a dataset for 21,129 reference plots (corresponding to "pure" pixels) was generated that contained a spectral time-series (red band, near-infrared band, Normalized Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI)) and the associated annual crop type with an 8-day time step over the period. This dataset can be used to develop new classification methods based on time-series analysis using deep learning, and to monitor and predict crop succession.

Hubert-Moy Laurence, Thibault Jeanne, Fabre Elodie, Rozo Clémence, Arvor Damien, Corpetti Thomas, Rapinel Sébastien

2019-Dec

Big data, Crop modeling, LPIS, MODIS, Time-series analysis, Vegetation index

General General

A Wrapper Feature Subset Selection Method Based on Randomized Search and Multilayer Structure.

In BioMed research international ; h5-index 102.0

The identification of discriminative features from information-rich data with the goal of clinical diagnosis is crucial in the field of biomedical science. In this context, many machine-learning techniques have been widely applied and achieved remarkable results. However, disease, especially cancer, is often caused by a group of features with complex interactions. Unlike traditional feature selection methods, which only focused on finding single discriminative features, a multilayer feature subset selection method (MLFSSM), which employs randomized search and multilayer structure to select a discriminative subset, is proposed herein. In each level of this method, many feature subsets are generated to assure the diversity of the combinations, and the weights of features are evaluated on the performances of the subsets. The weight of a feature would increase if the feature is selected into more subsets with better performances compared with other features on the current layer. In this manner, the values of feature weights are revised layer-by-layer; the precision of feature weights is constantly improved; and better subsets are repeatedly constructed by the features with higher weights. Finally, the topmost feature subset of the last layer is returned. The experimental results based on five public gene datasets showed that the subsets selected by MLFSSM were more discriminative than the results by traditional feature methods including LVW (a feature subset method used the Las Vegas method for randomized search strategy), GAANN (a feature subset selection method based genetic algorithm (GA)), and support vector machine recursive feature elimination (SVM-RFE). Furthermore, MLFSSM showed higher classification performance than some state-of-the-art methods which selected feature pairs or groups, including top scoring pair (TSP), k-top scoring pairs (K-TSP), and relative simplicity-based direct classifier (RS-DC).

Mao Yifei, Yang Yuansheng

2019

General General

Comparison of Bagging and Boosting Ensemble Machine Learning Methods for Automated EMG Signal Classification.

In BioMed research international ; h5-index 102.0

The neuromuscular disorders are diagnosed using electromyographic (EMG) signals. Machine learning algorithms are employed as a decision support system to diagnose neuromuscular disorders. This paper compares bagging and boosting ensemble learning methods to classify EMG signals automatically. Even though ensemble classifiers' efficacy in relation to real-life issues has been presented in numerous studies, there are almost no studies which focus on the feasibility of bagging and boosting ensemble classifiers to diagnose the neuromuscular disorders. Therefore, the purpose of this paper is to assess the feasibility of bagging and boosting ensemble classifiers to diagnose neuromuscular disorders through the use of EMG signals. It should be understood that there are three steps to this method, where the step number one is to calculate the wavelet packed coefficients (WPC) for every type of EMG signal. After this, it is necessary to calculate statistical values of WPC so that the distribution of wavelet coefficients could be demonstrated. In the last step, an ensemble classifier used the extracted features as an input of the classifier to diagnose the neuromuscular disorders. Experimental results showed the ensemble classifiers achieved better performance for diagnosis of neuromuscular disorders. Results are promising and showed that the AdaBoost with random forest ensemble method achieved an accuracy of 99.08%, F-measure 0.99, AUC 1, and kappa statistic 0.99.

Yaman Emine, Subasi Abdulhamit

2019

General General

Application of Compressive Sensing to Ultrasound Images: A Review.

In BioMed research international ; h5-index 102.0

Compressive sensing (CS) offers compression of data below the Nyquist rate, making it an attractive solution in the field of medical imaging, and has been extensively used for ultrasound (US) compression and sparse recovery. In practice, CS offers a reduction in data sensing, transmission, and storage. Compressive sensing relies on the sparsity of data; i.e., data should be sparse in original or in some transformed domain. A look at the literature reveals that rich variety of algorithms have been suggested to recover data using compressive sensing from far fewer samples accurately, but with tradeoffs for efficiency. This paper reviews a number of significant CS algorithms used to recover US images from the undersampled data along with the discussion of CS in 3D US images. In this paper, sparse recovery algorithms applied to US are classified in five groups. Algorithms in each group are discussed and summarized based on their unique technique, compression ratio, sparsifying transform, 3D ultrasound, and deep learning. Research gaps and future directions are also discussed in the conclusion of this paper. This study is aimed to be beneficial for young researchers intending to work in the area of CS and its applications, specifically to US.

Yousufi Musyyab, Amir Muhammad, Javed Umer, Tayyib Muhammad, Abdullah Suheel, Ullah Hayat, Qureshi Ijaz Mansoor, Alimgeer Khurram Saleem, Akram Muhammad Waseem, Khan Khan Bahadar

2019

Public Health Public Health

Data mining polycystic ovary morphology in electronic medical record ultrasound reports.

In Fertility research and practice ; h5-index 0.0

Background : Polycystic ovary syndrome (PCOS) is characterized by hyperandrogenemia, oligo-anovulation, and numerous ovarian cysts. Hospital electronic medical records provide an avenue for investigating polycystic ovary morphology commonly seen in PCOS at a large scale. The purpose of this study was to develop and evaluate the performance of two machine learning text algorithms, for classification of polycystic ovary morphology (PCOM) in pelvic ultrasounds.

Methods : Pelvic ultrasound reports from patients at Boston Medical Center between October 1, 2003 and December 12, 2016 were included for analysis, which resulted in 39,093 ultrasound reports from 25,535 unique women. Following the 2003 Rotterdam Consensus Criteria for polycystic ovary syndrome, 2000 randomly selected ultrasounds were expert labeled for PCOM status as present, absent, or unidentifiable (not able to be determined from text alone). An ovary was marked as having PCOM if there was mention of numerous peripheral follicles or if the volume was greater than 10 ml in the absence of a dominant follicle or other confounding pathology. Half of the labeled data was used to develop and refine the algorithms, and the other half was used as a test set for evaluating its accuracy.

Results : On the evaluation set of 1000 random US reports, the accuracy of the classifiers were 97.6% (95% CI: 96.5, 98.5%) and 96.1% (94.7, 97.2%). Both models were more adept at identifying PCOM-absent ultrasounds than either PCOM-unidentifiable or PCOM-present ultrasounds. The two classifiers estimated prevalence of PCOM within the whole set of 39,093 ultrasounds to be 44% PCOM-absent, 32% PCOM-unidentifiable, and 24% PCOM-present.

Conclusions : Although accuracy measured on the test set and inter-rater agreement between the two classifiers (Cohen's Kappa = 0.988) was high, a major limitation of our approach is that it uses the ultrasound report text as a proxy and does not directly count follicles from the ultrasound images themselves.

Cheng Jay Jojo, Mahalingaiah Shruthi

2019

Data mining, Electronic medical record, Machine learning, Polycystic ovary syndrome, Ultrasound

General General

Fully automated image-based estimation of postural point-features in children with cerebral palsy using deep learning.

In Royal Society open science ; h5-index 0.0

The aim of this study was to provide automated identification of postural point-features required to estimate the location and orientation of the head, multi-segmented trunk and arms from videos of the clinical test 'Segmental Assessment of Trunk Control' (SATCo). Three expert operators manually annotated 13 point-features in every fourth image of 177 short (5-10 s) videos (25 Hz) of 12 children with cerebral palsy (aged: 4.52 ± 2.4 years), participating in SATCo testing. Linear interpolation for the remaining images resulted in 30 825 annotated images. Convolutional neural networks were trained with cross-validation, giving held-out test results for all children. The point-features were estimated with error 4.4 ± 3.8 pixels at approximately 100 images per second. Truncal segment angles (head, neck and six thoraco-lumbar-pelvic segments) were estimated with error 6.4 ± 2.8°, allowing accurate classification (F1 > 80%) of deviation from a reference posture at thresholds up to 3°, 3° and 2°, respectively. Contact between arm point-features (elbow and wrist) and supporting surface was classified at F1 = 80.5%. This study demonstrates, for the first time, technical feasibility to automate the identification of (i) a sitting segmental posture including individual trunk segments, (ii) changes away from that posture, and (iii) support from the upper limb, required for the clinical SATCo.

Cunningham Ryan, Sánchez María B, Butler Penelope B, Southgate Matthew J, Loram Ian D

2019-Nov

SATCo, cerebral palsy, deep learning, feature tracking, pose estimation, video analysis

General General

Endogenetic structure of filter bubble in social networks.

In Royal Society open science ; h5-index 0.0

The filter bubble is an intermediate structure to provoke polarization and echo chambers in social networks, and it has become one of today's most urgent issues for social media. Previous studies usually equated filter bubbles with community structures and emphasized this exogenous isolation effect, but there is a lack of full discussion of the internal organization of filter bubbles. Here, we design an experiment for analysing filter bubbles taking advantage of social bots. We deployed 128 bots to Weibo (the largest microblogging network in China), and each bot consumed a specific topic (entertainment or sci-tech) and ran for at least two months. In total, we recorded about 1.3 million messages exposed to these bots and their social networks. By analysing the text received by the bots and motifs in their social networks, we found that a filter bubble is not only a dense community of users with the same preferences but also presents an endogenetic unidirectional star-like structure. The structure could spontaneously exclude non-preferred information and cause polarization. Moreover, our work proved that the felicitous use of artificial intelligence technology could provide a useful experimental approach that combines privacy protection and controllability in studying social media.

Min Yong, Jiang Tingjun, Jin Cheng, Li Qu, Jin Xiaogang

2019-Nov

controlled experiment, echo chamber, polarization, privacy protection, social network

Internal Medicine Internal Medicine

Urinary Metabolomic Markers of Protein Glycation, Oxidation, and Nitration in Early-Stage Decline in Metabolic, Vascular, and Renal Health.

In Oxidative medicine and cellular longevity ; h5-index 0.0

Glycation, oxidation, nitration, and crosslinking of proteins are implicated in the pathogenic mechanisms of type 2 diabetes, cardiovascular disease, and chronic kidney disease. Related modified amino acids formed by proteolysis are excreted in urine. We quantified urinary levels of these metabolites and branched-chain amino acids (BCAAs) in healthy subjects and assessed changes in early-stage decline in metabolic, vascular, and renal health and explored their diagnostic utility for a noninvasive health screen. We recruited 200 human subjects with early-stage health decline and healthy controls. Urinary amino acid metabolites were determined by stable isotopic dilution analysis liquid chromatography-tandem mass spectrometry. Machine learning was applied to optimise and validate algorithms to discriminate between study groups for potential diagnostic utility. Urinary analyte changes were as follows: impaired metabolic health-increased N ε -carboxymethyl-lysine, glucosepane, glutamic semialdehyde, and pyrraline; impaired vascular health-increased glucosepane; and impaired renal health-increased BCAAs and decreased N ε -(γ-glutamyl)lysine. Algorithms combining subject age, BMI, and BCAAs discriminated between healthy controls and impaired metabolic, vascular, and renal health study groups with accuracy of 84%, 72%, and 90%, respectively. In 2-step analysis, algorithms combining subject age, BMI, and urinary N ε -fructosyl-lysine and valine discriminated between healthy controls and impaired health (any type), accuracy of 78%, and then between types of health impairment with accuracy of 69%-78% (cf. random selection 33%). From likelihood ratios, this provided small, moderate, and conclusive evidence of early-stage cardiovascular, metabolic, and renal disease with diagnostic odds ratios of 6 - 7, 26 - 28, and 34 - 79, respectively. We conclude that measurement of urinary glycated, oxidized, crosslinked, and branched-chain amino acids provides the basis for a noninvasive health screen for early-stage health decline in metabolic, vascular, and renal health.

Masania Jinit, Faustmann Gernot, Anwar Attia, Hafner-Giessauf Hildegard, Rajpoot Nasir, Grabher Johanna, Rajpoot Kashif, Tiran Beate, Obermayer-Pietsch Barbara, Winklhofer-Roob Brigitte M, Roob Johannes M, Rabbani Naila, Thornalley Paul J

2019

General General

Emergency Department Capacity Planning: A Recurrent Neural Network and Simulation Approach.

In Computational and mathematical methods in medicine ; h5-index 0.0

Emergency departments (EDs) play a vital role in the whole healthcare system as they are the first point of care in hospitals for urgent and critically ill patients. Therefore, effective management of hospital's ED is crucial in improving the quality of the healthcare service. The effectiveness depends on how efficiently the hospital resources are used, particularly under budget constraints. Simulation modeling is one of the best methods to optimize resources and needs inputs such as patients' arrival time, patient's length of stay (LOS), and the route of patients in the ED. This study develops a simulation model to determine the optimum number of beds in an ED by minimizing the patients' LOS. The hospital data are analyzed, and patients' LOS and the route of patients in the ED are determined. To determine patients' arrival times, the features associated with patients' arrivals at ED are identified. Mean arrival rate is used as a feature in addition to climatic and temporal variables. The exhaustive feature-selection method has been used to determine the best subset of the features, and the mean arrival rate is determined as one of the most significant features. This study is executed using the one-year ED arrival data together with five-year (43.824 study hours) ED arrival data to improve the accuracy of predictions. Furthermore, ten different machine learning (ML) algorithms are used utilizing the same best subset of these features. After a tenfold cross-validation experiment, based on mean absolute percentage error (MAPE), the stateful long short-term memory (LSTM) model performed better than other models with an accuracy of 47%, followed by the decision tree and random forest methods. Using the simulation method, the LOS has been minimized by 7% and the number of beds at the ED has been optimized.

Nas Serkan, Koyuncu Melik

2019

Radiology Radiology

Texture analysis and machine learning algorithms accurately predict histologic grade in small (< 4 cm) clear cell renal cell carcinomas: a pilot study.

In Abdominal radiology (New York) ; h5-index 0.0

PURPOSE : To predict the histologic grade of small clear cell renal cell carcinomas (ccRCCs) using texture analysis and machine learning algorithms.

METHODS : Fifty-two noncontrast (NC), 26 corticomedullary (CM) phase, and 35 nephrographic (NG) phase CTs of small (< 4 cm) surgically resected ccRCCs were retrospectively identified. Surgical pathology classified the tumors as low- or high-Fuhrman histologic grade. The axial image with the largest cross-sectional tumor area was exported and segmented. Six histogram and 31 texture (gray-level co-occurrences (GLC) and gray-level run-lengths (GLRL)) features were calculated for each tumor in each phase. T testing compared feature values in low- and high-grade ccRCCs, with a (Benjamini-Hochberg) false discovery rate of 10%. Area under the receiver operating curve (AUC) was calculated for each feature to assess prediction of low- and high-grade ccRCCs in each phase. Histogram, texture, and combined histogram and texture data sets were used to train and test four algorithms (k-nearest neighbor (KNN), support vector machine (SVM), random forests, and decision tree) with tenfold cross-validation; AUCs were calculated for each algorithm in each phase to assess prediction of low- and high-grade ccRCCs.

RESULTS : Zero, 23, and 0 features in the NC, CM, and NG phases had statistically significant differences between low and high-grade ccRCCs. CM histogram skewness and GLRL short run emphasis had the highest AUCs (0.82) in predicting histologic grade. All four algorithms had the highest AUCs (0.97) predicting histologic grade using CM histogram features. The algorithms' AUCs decreased using histogram or texture features from NC or NG phases.

CONCLUSION : The histologic grade of small ccRCCs can be accurately predicted with machine learning algorithms using CM histogram features, which outperform NC and NG phase image data.

Haji-Momenian Shawn, Lin Zixian, Patel Bhumi, Law Nicole, Michalak Adam, Nayak Anishsanjay, Earls James, Loew Murray

2019-Dec-10

Clear cell renal cell carcinoma, Histology, Machine learning, Texture

Ophthalmology Ophthalmology

Correction to: Artificial intelligence for diabetic retinopathy screening: a review.

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

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Grzybowski Andrzej, Brona Piotr, Lim Gilbert, Ruamviboonsuk Paisan, Tan Gavin S W, Abramoff Michael, Ting Daniel S W

2019-Dec-10

Public Health Public Health

Brain-age in midlife is associated with accelerated biological aging and cognitive decline in a longitudinal birth cohort.

In Molecular psychiatry ; h5-index 103.0

An individual's brainAGE is the difference between chronological age and age predicted from machine-learning models of brain-imaging data. BrainAGE has been proposed as a biomarker of age-related deterioration of the brain. Having an older brainAGE has been linked to Alzheimer's, dementia, and mortality. However, these findings are largely based on cross-sectional associations which can confuse age differences with cohort differences. To illuminate the validity of brainAGE as a biomarker of accelerated brain aging, a study is needed of a large cohort all born in the same year who nevertheless vary on brainAGE. In the Dunedin Study, a population-representative 1972-73 birth cohort, we measured brainAGE at age 45 years, as well as the pace of biological aging and cognitive decline in longitudinal data from childhood to midlife (N = 869). In this cohort, all chronological age 45 years, brainAGE was measured reliably (ICC = 0.81) and ranged from 24 to 72 years. Those with older midlife brainAGEs tended to have poorer cognitive function in both adulthood and childhood, as well as impaired brain health at age 3. Furthermore, those with older brainAGEs had an accelerated pace of biological aging, older facial appearance, and early signs of cognitive decline from childhood to midlife. These findings help to validate brainAGE as a potential surrogate biomarker for midlife intervention studies that seek to measure dementia-prevention efforts in midlife. However, the findings also caution against the assumption that brainAGE scores represent only age-related deterioration of the brain as they may also index central nervous system variation present since childhood.

Elliott Maxwell L, Belsky Daniel W, Knodt Annchen R, Ireland David, Melzer Tracy R, Poulton Richie, Ramrakha Sandhya, Caspi Avshalom, Moffitt Terrie E, Hariri Ahmad R

2019-Dec-10

General General

Constraining modelled global vegetation dynamics and carbon turnover using multiple satellite observations.

In Scientific reports ; h5-index 158.0

The response of land ecosystems to future climate change is among the largest unknowns in the global climate-carbon cycle feedback. This uncertainty originates from how dynamic global vegetation models (DGVMs) simulate climate impacts on changes in vegetation distribution, productivity, biomass allocation, and carbon turnover. The present-day availability of a multitude of satellite observations can potentially help to constrain DGVM simulations within model-data integration frameworks. Here, we use satellite-derived datasets of the fraction of absorbed photosynthetic active radiation (FAPAR), sun-induced fluorescence (SIF), above-ground biomass of trees (AGB), land cover, and burned area to constrain parameters for phenology, productivity, and vegetation dynamics in the LPJmL4 DGVM. Both the prior and the optimized model accurately reproduce present-day estimates of the land carbon cycle and of temporal dynamics in FAPAR, SIF and gross primary production. However, the optimized model reproduces better the observed spatial patterns of biomass, tree cover, and regional forest carbon turnover. Using a machine learning approach, we found that remaining errors in simulated forest carbon turnover can be explained with bioclimatic variables. This demonstrates the need to improve model formulations for climate effects on vegetation turnover and mortality despite the apparent successful constraint of simulated vegetation dynamics with multiple satellite observations.

Forkel Matthias, Drüke Markus, Thurner Martin, Dorigo Wouter, Schaphoff Sibyll, Thonicke Kirsten, von Bloh Werner, Carvalhais Nuno

2019-Dec-10

General General

Decoding of single-trial EEG reveals unique states of functional brain connectivity that drive rapid speech categorization decisions.

In Journal of neural engineering ; h5-index 52.0

Categorical perception (CP) is an inherent property of speech perception. The response time (RT) of listeners' perceptual speech identification is highly sensitive to individual differences. While the neural correlates of CP have been well studied in terms of the regional contributions of the brain to behavior, functional connectivity patterns that signify individual differences in listeners' speed (RT) for speech categorization is less clear. To address these questions, we applied several computational approaches to the EEG, including graph mining, machine learning (i.e., support vector machine), and stability selection to investigate the unique brain states (functional neural connectivity) that predict the speed of listeners' behavioral decisions. We infer that (i) the listeners' perceptual speed is directly related to dynamic variations in their brain connectomics, (ii) global network assortativity and efficiency distinguished fast, medium, and slow RT, (iii) the functional network underlying speeded decisions increases in negative assortativity (i.e., became disassortative) for slower RTs, (iv) slower categorical speech decisions cause excessive use of neural resources and more aberrant information flow within the CP circuitry, (v) slower responders tended to utilize functional brain networks excessively (or inappropriately) whereas fast responders (with lower global efficiency) utilized the same neural pathways but with more restricted organization. Our results showed that neural classifiers (SVM) coupled with stability selection correctly classify behavioral RTs from functional connectivity alone with over 92% accuracy (AUC=0.9). Our results corroborate previous studies by supporting the engagement of similar temporal (STG), parietal, motor, and prefrontal regions in CP using an entirely data-driven approach.

Al-Fahad Rakib, Yeasin Mohammed, M Bidelman Gavin

2019-Dec-11

Categorical speech perception, functional connectivity, machine learning, speech processing, stability selection

General General

Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test.

In PloS one ; h5-index 176.0

Diabetes is a large healthcare burden worldwide. There is substantial evidence that lifestyle modifications and drug intervention can prevent diabetes, therefore, an early identification of high risk individuals is important to design targeted prevention strategies. In this paper, we present an automatic tool that uses machine learning techniques to predict the development of type 2 diabetes mellitus (T2DM). Data generated from an oral glucose tolerance test (OGTT) was used to develop a predictive model based on the support vector machine (SVM). We trained and validated the models using the OGTT and demographic data of 1,492 healthy individuals collected during the San Antonio Heart Study. This study collected plasma glucose and insulin concentrations before glucose intake and at three time-points thereafter (30, 60 and 120 min). Furthermore, personal information such as age, ethnicity and body-mass index was also a part of the data-set. Using 11 OGTT measurements, we have deduced 61 features, which are then assigned a rank and the top ten features are shortlisted using minimum redundancy maximum relevance feature selection algorithm. All possible combinations of the 10 best ranked features were used to generate SVM based prediction models. This research shows that an individual's plasma glucose levels, and the information derived therefrom have the strongest predictive performance for the future development of T2DM. Significantly, insulin and demographic features do not provide additional performance improvement for diabetes prediction. The results of this work identify the parsimonious clinical data needed to be collected for an efficient prediction of T2DM. Our approach shows an average accuracy of 96.80% and a sensitivity of 80.09% obtained on a holdout set.

Abbas Hasan T, Alic Lejla, Erraguntla Madhav, Ji Jim X, Abdul-Ghani Muhammad, Abbasi Qammer H, Qaraqe Marwa K

2019

General General

Evaluation of potential auras in generalized epilepsy from EEG signals using deep convolutional neural networks and time-frequency representation.

In Biomedizinische Technik. Biomedical engineering ; h5-index 0.0

The general uncertainty of epilepsy and its unpredictable seizures often affect badly the quality of life of people exposed to this disease. There are patients who can be considered fortunate in terms of prediction of any seizures. These are patients with epileptic auras. In this study, it was aimed to evaluate pre-seizure warning symptoms of the electroencephalography (EEG) signals by a convolutional neural network (CNN) inspired by the epileptic auras defined in the medical field. In this context, one-dimensional EEG signals were transformed into a spectrogram display form in the frequency-time domain by applying a short-time Fourier transform (STFT). Systemic changes in pre-epileptic seizure have been described by applying the CNN approach to the EEG signals represented in the image form, and the subjective EEG-Aura process has been tried to be determined for each patient. Considering all patients included in the evaluation, it was determined that the 1-min interval covering the time from the second minute to the third minute before the seizure had the highest mean and the lowest variance to determine the systematic changes before the seizure. Thus, the highest performing process is described as EEG-Aura. The average success for the EEG-Aura process was 90.38 ± 6.28%, 89.78 ± 8.34% and 90.47 ± 5.95% for accuracy, specificity and sensitivity, respectively. Through the proposed model, epilepsy patients who do not respond to medical treatment methods are expected to maintain their lives in a more comfortable and integrated way.

Polat Hasan, Aluçlu Mehmet Ufuk, Özerdem Mehmet Siraç

2019-Dec-11

EEG, deep learning, epilepsy, epileptic aura, time-frequency representation

General General

Automatic Detection of Pain from Facial Expressions: A Survey.

In IEEE transactions on pattern analysis and machine intelligence ; h5-index 127.0

Pain sensation is essential for survival, since it draws attention to physical threat to the body. Pain assessment is usually done through self-reports. However, self-assessment of pain is not available in the case of noncommunicative patients, and therefore, observer reports should be relied upon. Observer reports of pain could be prone to errors due to subjective biases of observers. Moreover, continuous monitoring by humans is impractical. Therefore, automatic pain detection technology could be deployed to assist human caregivers and complement their service, thereby improving the quality of pain management, especially for noncommunicative patients. Facial expressions are a reliable indicator of pain, and are used in all observer-based pain assessment tools. Following the advancements in automatic facial expression analysis, computer vision researchers have tried to use this technology for developing approaches for automatically detecting pain from facial expressions. This paper surveys the literature published in this field over the past decade, categorizes it, and identifies future research directions. The survey covers the pain datasets used in the reviewed literature, the learning tasks targeted by the approaches, the features extracted from images and image sequences to represent pain-related information, and finally, the machine learning methods used.

Hassan Teena, Seus Dominik, Wollenberg Johannes, Weitz Katharina, Kunz Miriam, Lautenbacher Stefan, Garbas Jens-Uwe, Schmid Ute

2019-Dec-09

General General

Spatial-Temporal Dependency Modeling and Network Hub Detection for Functional MRI Analysis via Convolutional-Recurrent Network.

In IEEE transactions on bio-medical engineering ; h5-index 0.0

Early identification of dementia at the stage of mild cognitive impairment (MCI) is crucial for timely diagnosis and intervention of Alzheimer's disease (AD). Although several pioneering studies have been devoted to automated AD diagnosis based on resting-state functional magnetic resonance imaging (rs-fMRI), their performance is somewhat limited due to non-effective mining of spatial-temporal dependency. Besides, few of these existing approaches consider the explicit detection and modeling of discriminative brain regions (i.e., network hubs) that are sensitive to AD progression. In this paper, we propose a unique Spatial-Temporal convolutional-recurrent neural Network (STNet) for automated prediction of AD progression and network hub detection from rs-fMRI time series. Our STNet incorporates the spatial-temporal information mining and AD-related hub detection into an end-to-end deep learning model. Specifically, we first partition rs-fMRI time series into a sequence of overlapping sliding windows. A sequence of convolutional components are then designed to capture the local-to-global spatially-dependent patterns within each sliding window, based on which we are able to identify discriminative hubs and characterize their unique contributions to disease diagnosis. A recurrent component with long short-term memory (LSTM) units is further employed to model the whole-brain temporal dependency from the spatially-dependent pattern sequences, thus capturing the temporal dynamics along time. We evaluate our method on 174 subjects with 563 rs-fMRI scans, with results suggesting the effectiveness of our method in disease prediction and hub detection.

Wang Mingliang, Lian Chunfeng, Yao Dongren, Zhang Daoqiang, Liu Mingxia, Shen Dinggang

2019-Dec-06

oncology Oncology

A Study of High-Grade Serous Ovarian Cancer Origins Implicates the SOX18 Transcription Factor in Tumor Development.

In Cell reports ; h5-index 119.0

Fallopian tube secretory epithelial cells (FTSECs) are likely the main precursor cell type of high-grade serous ovarian cancers (HGSOCs), but these tumors may also arise from ovarian surface epithelial cells (OSECs). We profiled global landscapes of gene expression and active chromatin to characterize molecular similarities between OSECs (n = 114), FTSECs (n = 74), and HGSOCs (n = 394). A one-class machine learning algorithm predicts that most HGSOCs derive from FTSECs, with particularly high FTSEC scores in mesenchymal-type HGSOCs (padj < 8 × 10-4). However, a subset of HGSOCs likely derive from OSECs, particularly HGSOCs of the proliferative type (padj < 2 × 10-4), suggesting a dualistic model for HGSOC origins. Super-enhancer (SE) landscapes were also more similar between FTSECs and HGSOCs than between OSECs and HGSOCs (p < 2.2 × 10-16). The SOX18 transcription factor (TF) coincided with a HGSOC-specific SE, and ectopic overexpression of SOX18 in FTSECs caused epithelial-to-mesenchymal transition, indicating that SOX18 plays a role in establishing the mesenchymal signature of fallopian-derived HGSOCs.

Lawrenson Kate, Fonseca Marcos A S, Liu Annie Y, Segato Dezem Felipe, Lee Janet M, Lin Xianzhi, Corona Rosario I, Abbasi Forough, Vavra Kevin C, Dinh Huy Q, Gill Navjot Kaur, Seo Ji-Heui, Coetzee Simon, Lin Yvonne G, Pejovic Tanja, Mhawech-Fauceglia Paulette, Rowat Amy C, Drapkin Ronny, Karlan Beth Y, Hazelett Dennis J, Freedman Matthew L, Gayther Simon A, Noushmehr Houtan

2019-Dec-10

RNA-seq, SOX18, dual origins, fallopian tube secretory epithelial cell, high-grade serous ovarian cancer, machine learning, one-class logistic regression models, ovarian surface epithelial cell, single-cell RNA-seq, super enhancers, transcription factors

oncology Oncology

A Deep Learning Framework for Predicting Response to Therapy in Cancer.

In Cell reports ; h5-index 119.0

A major challenge in cancer treatment is predicting clinical response to anti-cancer drugs on a personalized basis. Using a pharmacogenomics database of 1,001 cancer cell lines, we trained deep neural networks for prediction of drug response and assessed their performance on multiple clinical cohorts. We demonstrate that deep neural networks outperform the current state in machine learning frameworks. We provide a proof of concept for the use of deep neural network-based frameworks to aid precision oncology strategies.

Sakellaropoulos Theodore, Vougas Konstantinos, Narang Sonali, Koinis Filippos, Kotsinas Athanassios, Polyzos Alexander, Moss Tyler J, Piha-Paul Sarina, Zhou Hua, Kardala Eleni, Damianidou Eleni, Alexopoulos Leonidas G, Aifantis Iannis, Townsend Paul A, Panayiotidis Mihalis I, Sfikakis Petros, Bartek Jiri, Fitzgerald Rebecca C, Thanos Dimitris, Mills Shaw Kenna R, Petty Russell, Tsirigos Aristotelis, Gorgoulis Vassilis G

2019-Dec-10

DNN, deep neural networks, drug response prediction, machine learning, precision medicine

General General

Deep learning for inferring gene relationships from single-cell expression data.

In Proceedings of the National Academy of Sciences of the United States of America ; h5-index 0.0

Several methods were developed to mine gene-gene relationships from expression data. Examples include correlation and mutual information methods for coexpression analysis, clustering and undirected graphical models for functional assignments, and directed graphical models for pathway reconstruction. Using an encoding for gene expression data, followed by deep neural networks analysis, we present a framework that can successfully address all of these diverse tasks. We show that our method, convolutional neural network for coexpression (CNNC), improves upon prior methods in tasks ranging from predicting transcription factor targets to identifying disease-related genes to causality inference. CNNC's encoding provides insights about some of the decisions it makes and their biological basis. CNNC is flexible and can easily be extended to integrate additional types of genomics data, leading to further improvements in its performance.

Yuan Ye, Bar-Joseph Ziv

2019-Dec-10

causality inference, deep learning, gene interactions

General General

Predicting Retrosynthetic Reactions using Self-Corrected Transformer Neural Networks.

In Journal of chemical information and modeling ; h5-index 0.0

Synthesis planning is the process of recursively decomposing target molecules into available precursors. Computer-aided retrosynthesis can potentially assist chemists in designing synthetic routes, but at present it is cumbersome and can't provide results of satisfactory qualities. In this study, we have developed a template-free self-corrected retrosynthesis predictor (SCROP) to predict retrosynthesis by using Transformer neural networks. In the method, the retrosynthesis planning was converted to a machine translation problem from the products to molecular linear notations of reactants. By coupling with a neural network-based syntax corrector, our method achieved an accuracy of 59.0% on a standard benchmark dataset, which outperformed >21% over other deep learning methods and >6% over template-based methods. More importantly, our method was 1.7 times more accurate than other state-of-the-art methods for compounds not appearing in the training set.

Zheng Shuangjia, Rao Jiahua, Zhang Zhongyue, Xu Jun, Yang Yuedong

2019-Dec-11

General General

The Effect of Debiasing Protein Ligand Binding Data on Generalisation.

In Journal of chemical information and modeling ; h5-index 0.0

The structured nature of chemical data means machine learning models trained to predict protein-ligand binding risk overfitting the data, impairing their ability to generalize and make accurate predictions for novel candidate ligands. Data debiasing algorithms, which systematically partition the data to reduce bias and provide a more accurate metric of model performance, have the potential to address this issue. When models are trained using debiased data splits, the reward for simply memorising the training data is reduced, suggesting that the ability of the model to make accurate predictions for novel candidate ligands will improve. To test this hypothesis, we use distance-based data splits to measure how well a model can generalize. We first confirm that models perform better for randomly split held-out sets than for distant held-out sets. We then debias the data and find, surprisingly, that debiasing typically reduces the ability of models to make accurate predictions for distant held-out test sets and that model performance measured after debiasing is not representative of the ability of a model to generalize. These results suggest that debiasing reduces the information available to a model, impairing its ability to generalize.

Sundar Vikram, Colwell Lucy

2019-Dec-11

oncology Oncology

Prostate cancer research: The next generation; report from the 2019 Coffey-Holden Prostate Cancer Academy Meeting.

In The Prostate ; h5-index 42.0

INTRODUCTION : The 2019 Coffey-Holden Prostate Cancer Academy (CHPCA) Meeting, "Prostate Cancer Research: The Next Generation," was held 20 to 23 June, 2019, in Los Angeles, California.

METHODS : The CHPCA Meeting is an annual conference held by the Prostate Cancer Foundation, that is uniquely structured to stimulate intense discussion surrounding topics most critical to accelerating prostate cancer research and the discovery of new life-extending treatments for patients. The 7th Annual CHPCA Meeting was attended by 86 investigators and concentrated on many of the most promising new treatment opportunities and next-generation research technologies.

RESULTS : The topics of focus at the meeting included: new treatment strategies and novel agents for targeted therapies and precision medicine, new treatment strategies that may synergize with checkpoint immunotherapy, next-generation technologies that visualize tumor microenvironment (TME) and molecular pathology in situ, multi-omics and tumor heterogeneity using single cells, 3D and TME models, and the role of extracellular vesicles in cancer and their potential as biomarkers.

DISCUSSION : This meeting report provides a comprehensive summary of the talks and discussions held at the 2019 CHPCA Meeting, for the purpose of globally disseminating this knowledge and ultimately accelerating new treatments and diagnostics for patients with prostate cancer.

Miyahira Andrea K, Sharp Adam, Ellis Leigh, Jones Jennifer, Kaochar Salma, Larman H Benjamin, Quigley David A, Ye Huihui, Simons Jonathan W, Pienta Kenneth J, Soule Howard R

2019-Dec-11

artificial intelligence, cancer immunotherapy, precision medicine, therapeutics, tumor genomics

General General

Development and Validation of a Machine Learning Model to Aid Discharge Processes for Inpatient Surgical Care.

In JAMA network open ; h5-index 0.0

Importance : Inpatient overcrowding is associated with delays in care, including the deferral of surgical care until beds are available to accommodate postoperative patients. Timely patient discharge is critical to address inpatient overcrowding and requires coordination among surgeons, nurses, case managers, and others. This is difficult to achieve without early identification and systemwide transparency of discharge candidates and their respective barriers to discharge.

Objective : To validate the performance of a clinically interpretable feedforward neural network model that could improve the discharge process by predicting which patients would be discharged within 24 hours and their clinical and nonclinical barriers.

Design, Setting, and Participants : This prognostic study included adult patients discharged from inpatient surgical care from May 1, 2016, to August 31, 2017, at a quaternary care teaching hospital. Model performance was assessed with standard cross-validation techniques. The model's performance was compared with a baseline model using historical procedure median length of stay to predict discharges. In prospective cohort analysis, the feedforward neural network model was used to make predictions on general surgical care floors with 63 beds. If patients were not discharged when predicted, the causes of delay were recorded.

Main Outcomes and Measures : The primary outcome was the out-of-sample area under the receiver operating characteristic curve of the model. Secondary outcomes included the causes of discharge delay and the number of avoidable bed-days.

Results : The model was trained on 15 201 patients (median [interquartile range] age, 60 [46-70] years; 7623 [50.1%] men) discharged from inpatient surgical care. The estimated out-of-sample area under the receiver operating characteristic curve of the model was 0.840 (SD, 0.008; 95% CI, 0.839-0.844). Compared with the baseline model, the neural network model had higher sensitivity (52.5% vs 56.6%) and specificity (51.7% vs 82.6%). The neural network model identified 65 barriers to discharge. In the prospective study of 605 patients, causes of delays included clinical barriers (41 patients [30.1%]), variation in clinical practice (30 patients [22.1%]), and nonclinical reasons (65 patients [47.8%]). Summing patients who were not discharged owing to variation in clinical practice and nonclinical reasons, 128 bed-days, or 1.2 beds per day, were classified as avoidable.

Conclusions and Relevance : This cohort study found that a neural network model could predict daily inpatient surgical care discharges and their barriers. The model identified systemic causes of discharge delays. Such models should be studied for their ability to increase the timeliness of discharges.

Safavi Kyan C, Khaniyev Taghi, Copenhaver Martin, Seelen Mark, Zenteno Langle Ana Cecilia, Zanger Jonathan, Daily Bethany, Levi Retsef, Dunn Peter

2019-Dec-02

Surgery Surgery

Artificial Intelligence and Surgical Decision-Making.

In JAMA surgery ; h5-index 69.0

Importance : Surgeons make complex, high-stakes decisions under time constraints and uncertainty, with significant effect on patient outcomes. This review describes the weaknesses of traditional clinical decision-support systems and proposes that artificial intelligence should be used to augment surgical decision-making.

Observations : Surgical decision-making is dominated by hypothetical-deductive reasoning, individual judgment, and heuristics. These factors can lead to bias, error, and preventable harm. Traditional predictive analytics and clinical decision-support systems are intended to augment surgical decision-making, but their clinical utility is compromised by time-consuming manual data management and suboptimal accuracy. These challenges can be overcome by automated artificial intelligence models fed by livestreaming electronic health record data with mobile device outputs. This approach would require data standardization, advances in model interpretability, careful implementation and monitoring, attention to ethical challenges involving algorithm bias and accountability for errors, and preservation of bedside assessment and human intuition in the decision-making process.

Conclusions and Relevance : Integration of artificial intelligence with surgical decision-making has the potential to transform care by augmenting the decision to operate, informed consent process, identification and mitigation of modifiable risk factors, decisions regarding postoperative management, and shared decisions regarding resource use.

Loftus Tyler J, Tighe Patrick J, Filiberto Amanda C, Efron Philip A, Brakenridge Scott C, Mohr Alicia M, Rashidi Parisa, Upchurch Gilbert R, Bihorac Azra

2019-Dec-11

Radiology Radiology

[The future of computer-aided diagnostics in chest computed tomography].

In Khirurgiia ; h5-index 0.0

Recently, more and more attention has been paid to the utility of artificial intelligence in medicine. Radiology differs from other medical specialties with its high digitalization, so most software developers operationalize this area of medicine. The primary condition for machine learning is met because medical diagnostic images have high reproducibility. Today, the most common anatomic area for computed tomography is the thorax, particularly with the widespread lung cancer screening programs using low-dose computed tomography. In this regard, the amount of information that needs to be processed by a radiologist is snowballing. Thus, automatic image analysis will allow more studies to be interpreted. This review is aimed at highlighting the possibilities of machine learning in the chest computed tomography.

Nikolaev A E, Chernina V Yu, Blokhin I A, Shapiev A N, Gonchar A P, Gombolevskiy V A, Petraikin A V, Silin A Yu, Petrova G D, Morozov S P

2019

artificial intelligence, computed tomography, low-dose computed tomography, lung cancer screening, machine learning, neural network, thorax

General General

Exploring Older Adults' Beliefs About the Use of Intelligent Assistants for Consumer Health Information Management: A Participatory Design Study.

In JMIR aging ; h5-index 0.0

BACKGROUND : Intelligent assistants (IAs), also known as intelligent agents, use artificial intelligence to help users achieve a goal or complete a task. IAs represent a potential solution for providing older adults with individualized assistance at home, for example, to reduce social isolation, serve as memory aids, or help with disease management. However, to design IAs for health that are beneficial and accepted by older adults, it is important to understand their beliefs about IAs, how they would like to interact with IAs for consumer health, and how they desire to integrate IAs into their homes.

OBJECTIVE : We explore older adults' mental models and beliefs about IAs, the tasks they want IAs to support, and how they would like to interact with IAs for consumer health. For the purpose of this study, we focus on IAs in the context of consumer health information management and search.

METHODS : We present findings from an exploratory, qualitative study that investigated older adults' perspectives of IAs that aid with consumer health information search and management tasks. Eighteen older adults participated in a multiphase, participatory design workshop in which we engaged them in discussion, brainstorming, and design activities that helped us identify their current challenges managing and finding health information at home. We also explored their beliefs and ideas for an IA to assist them with consumer health tasks. We used participatory design activities to identify areas in which they felt IAs might be useful, but also to uncover the reasoning behind the ideas they presented. Discussions were audio-recorded and later transcribed. We compiled design artifacts collected during the study to supplement researcher transcripts and notes. Thematic analysis was used to analyze data.

RESULTS : We found that participants saw IAs as potentially useful for providing recommendations, facilitating collaboration between themselves and other caregivers, and for alerts of serious illness. However, they also desired familiar and natural interactions with IAs (eg, using voice) that could, if need be, provide fluid and unconstrained interactions, reason about their symptoms, and provide information or advice. Other participants discussed the need for flexible IAs that could be used by those with low technical resources or skills.

CONCLUSIONS : From our findings, we present a discussion of three key components of participants' mental models, including the people, behaviors, and interactions they described that were important for IAs for consumer health information management and seeking. We then discuss the role of access, transparency, caregivers, and autonomy in design for addressing participants' concerns about privacy and trust as well as its role in assisting others that may interact with an IA on the older adults' behalf.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) : RR2-10.1145/3240925.3240972.

Martin-Hammond Aqueasha, Vemireddy Sravani, Rao Kartik

2019-Dec-11

aging in place, artificial intelligence, chatbots, co-design, conversational agents, digital health, elderly, health information seeking, intelligent assistants, participatory design

General General

Deep learning meets nanophotonics: A generalized accurate predictor for near fields and far fields of arbitrary 3D nanostructures.

In Nano letters ; h5-index 188.0

Deep artificial neural networks are powerful tools with many possible applications in nanophotonics. Here, we demonstrate how a deep neural network can be used as a fast, general purpose predictor of the full near-field and far-field response of plasmonic and dielectric nanostructures. A trained neural network is shown to infer the internal fields of arbitrary three-dimensional nanostructures many orders of magnitude faster compared to conventional numerical simulations. Secondary physical quantities are derived from the deep learning predictions and faithfully reproduce a wide variety of physical effects without requiring specific training. We discuss the strengths and limitations of the neural network approach using a number of model studies of single particles and their near-field interactions. Our approach paves the way for fast, yet universal methods for design and analysis of nanophotonic systems.

Wiecha Peter R, Muskens Otto L

2019-Dec-11

General General

Data-driven approaches to optical patterned defect detection.

In OSA continuum ; h5-index 0.0

Computer vision and classification methods have become increasingly wide-spread in recent years due to ever-increasing access to computation power. Advances in semiconductor devices are the basis for this growth, but few publications have probed the benefits of data-driven methods for improving a critical component of semiconductor manufacturing, the detection and inspection of defects for such devices. As defects become smaller, intensity threshold-based approaches eventually fail to adequately discern differences between faulty and non-faulty structures. To overcome these challenges we present machine learning methods including convolutional neural networks (CNN) applied to image-based defect detection. These images are formed from the simulated scattering of realistic geometries with and without key defects while also taking into account line edge roughness (LER). LER is a known and challenging problem in fabrication as it yields additional scattering that further complicates defect inspection. Simulating images of an intentional defect array, a CNN approach is applied to extend detectability and enhance classification to these defects, even those that are more than 20 times smaller than the inspection wavelength.

Henn Mark-Alexander, Zhou Hui, Barnes Bryan M

2019-Sep-15

Radiology Radiology

Classification of brain tumor isocitrate dehydrogenase status using MRI and deep learning.

In Journal of medical imaging (Bellingham, Wash.) ; h5-index 0.0

Isocitrate dehydrogenase (IDH) mutation status is an important marker in glioma diagnosis and therapy. We propose an automated pipeline for noninvasively predicting IDH status using deep learning and T2-weighted (T2w) magnetic resonance (MR) images with minimal preprocessing (N4 bias correction and normalization to zero mean and unit variance). T2w MR images and genomic data were obtained from The Cancer Imaging Archive dataset for 260 subjects (120 high-grade and 140 low-grade gliomas). A fully automated two-dimensional densely connected model was trained to classify IDH mutation status on 208 subjects and tested on another held-out set of 52 subjects using fivefold cross validation. Data leakage was avoided by ensuring subject separation during the slice-wise randomization. Mean classification accuracy of 90.5% was achieved for each axial slice in predicting the three classes of no tumor, IDH mutated, and IDH wild type. Test accuracy of 83.8% was achieved in predicting IDH mutation status for individual subjects on the test dataset of 52 subjects. We demonstrate a deep learning method to predict IDH mutation status using T2w MRI alone. Radiologic imaging studies using deep learning methods must address data leakage (subject duplication) in the randomization process to avoid upward bias in the reported classification accuracy.

Nalawade Sahil, Murugesan Gowtham K, Vejdani-Jahromi Maryam, Fisicaro Ryan A, Bangalore Yogananda Chandan G, Wagner Ben, Mickey Bruce, Maher Elizabeth, Pinho Marco C, Fei Baowei, Madhuranthakam Ananth J, Maldjian Joseph A

2019-Oct

convolutional neural network, deep learning, isocitrate dehydrogenase, magnetic resonance imaging, segmentation, tumor classification

General General

Tremor Assessment during Virtual Reality Brain Tumor Resection.

In Journal of surgical education ; h5-index 0.0

OBJECTIVE : Assessment of physiological tremor during neurosurgical procedures may provide further insights into the composites of surgical expertise. Virtual reality platforms may provide a mechanism for the quantitative assessment of physiological tremor. In this study, a virtual reality simulator providing haptic feedback was used to study physiological tremor in a simulated tumor resection task with participants from a "skilled" group and a "novice" group.

DESIGN : The task involved using a virtual ultrasonic aspirator to remove a series of virtual brain tumors with different visual and tactile characteristics without causing injury to surrounding tissue. Power spectral density analysis was employed to quantitate hand tremor during tumor resection. Statistical t test was used to determine tremor differences between the skilled and novice groups obtained from the instrument tip x, y, z coordinates, the instrument roll, pitch, yaw angles, and the instrument haptic force applied during tumor resection.

SETTING : The study was conducted at the Neurosurgical Simulation and Artificial Intelligence Learning Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.

PARTICIPANTS : The skilled group comprised 23 neurosurgeons and senior residents and the novice group comprised 92 junior residents and medical students.

RESULTS : The spectral analysis allowed quantitation of physiological tremor during virtual reality tumor resection. The skilled group displayed smaller physiological tremor than the novice group in all cases. In 3 out of 7 cases the difference was statistically significant.

CONCLUSIONS : The first investigation of the application of a virtual reality platform is presented for the quantitation of physiological tremor during a virtual reality tumor resection task. The goal of introducing such methodology to assess tremor is to highlight its potential educational application in neurosurgical resident training and in helping to further define the psychomotor skill set of surgeons.

Siyar Samaneh, Azarnoush Hamed, Rashidi Saeid, Del Maestro Rolando F

2019-Dec-07

NeuroTouch/NeuroVR, Patient Care, Practice-Based Learning and Improvement, Systems-Based Practice, neurosurgery, physiological tremor, simulation, tumor resection, virtual reality

General General

Using deep learning for a diffusion-based segmentation of the dentate nucleus and its benefits over atlas-based methods.

In Journal of medical imaging (Bellingham, Wash.) ; h5-index 0.0

The dentate nucleus (DN) is a gray matter structure deep in the cerebellum involved in motor coordination, sensory input integration, executive planning, language, and visuospatial function. The DN is an emerging biomarker of disease, informing studies that advance pathophysiologic understanding of neurodegenerative and related disorders. The main challenge in defining the DN radiologically is that, like many deep gray matter structures, it has poor contrast in T1-weighted magnetic resonance (MR) images and therefore requires specialized MR acquisitions for visualization. Manual tracing of the DN across multiple acquisitions is resource-intensive and does not scale well to large datasets. We describe a technique that automatically segments the DN using deep learning (DL) on common imaging sequences, such as T1-weighted, T2-weighted, and diffusion MR imaging. We trained a DL algorithm that can automatically delineate the DN and provide an estimate of its volume. The automatic segmentation achieved higher agreement to the manual labels compared to template registration, which is the current common practice in DN segmentation or multiatlas segmentation of manual labels. Across all sequences, the FA maps achieved the highest mean Dice similarity coefficient (DSC) of 0.83 compared to T1 imaging ( DSC = 0.76 ), T2 imaging ( DSC = 0.79 ), or a multisequence approach ( DSC = 0.80 ). A single atlas registration approach using the spatially unbiased atlas template of the cerebellum and brainstem template achieved a DSC of 0.23, and multi-atlas segmentation achieved a DSC of 0.33. Overall, we propose a method of delineating the DN on clinical imaging that can reproduce manual labels with higher accuracy than current atlas-based tools.

Bermudez Noguera Camilo, Bao Shunxing, Petersen Kalen J, Lopez Alexander M, Reid Jacqueline, Plassard Andrew J, Zald David H, Claassen Daniel O, Dawant Benoit M, Landman Bennett A

2019-Oct

automatic image segmentation, deep learning, dentate nucleus, magnetic resonance imaging, multisequence Imaging

oncology Oncology

Deep Learning for Whole Slide Image Analysis: An Overview.

In Frontiers in medicine ; h5-index 0.0

The widespread adoption of whole slide imaging has increased the demand for effective and efficient gigapixel image analysis. Deep learning is at the forefront of computer vision, showcasing significant improvements over previous methodologies on visual understanding. However, whole slide images have billions of pixels and suffer from high morphological heterogeneity as well as from different types of artifacts. Collectively, these impede the conventional use of deep learning. For the clinical translation of deep learning solutions to become a reality, these challenges need to be addressed. In this paper, we review work on the interdisciplinary attempt of training deep neural networks using whole slide images, and highlight the different ideas underlying these methodologies.

Dimitriou Neofytos, Arandjelović Ognjen, Caie Peter D

2019

cancer, computer vision, digital pathology, image analysis, machine learning, oncology, personalized pathology

Radiology Radiology

An Artificial Intelligent Signal Amplification System for in vivo Detection of miRNA.

In Frontiers in bioengineering and biotechnology ; h5-index 0.0

MicroRNAs (miRNA) have been identified as oncogenic drivers and tumor suppressors in every major cancer type. In this work, we design an artificial intelligent signal amplification (AISA) system including double-stranded SQ (S, signal strand; Q, quencher strand) and FP (F, fuel strand; P, protect strand) according to thermodynamics principle for sensitive detection of miRNA in vitro and in vivo. In this AISA system for miRNA detection, strand S carries a quenched imaging marker inside the SQ. Target miRNA is constantly replaced by a reaction intermediate and circulatively participates in the reaction, similar to enzyme. Therefore, abundant fluorescent substances from S and SP are dissociated from excessive SQ for in vitro and in vivo visualization. The versatility and feasibility for disease diagnosis using this system were demonstrated by constructing two types of AISA system to detect Hsa-miR-484 and Hsa-miR-100, respectively. The minimum target concentration detected by the system in vitro (10 min after mixing) was 1/10th that of the control group. The precancerous lesions of liver cancer were diagnosed, and the detection accuracy were larger than 94% both in terms of location and concentration. The ability to establish this design framework for AISA system with high specificity provides a new way to monitor tumor progression and to assess therapeutic responses.

Ma Xibo, Chen Lei, Yang Yingcheng, Zhang Weiqi, Wang Peixia, Zhang Kun, Zheng Bo, Zhu Lin, Sun Zheng, Zhang Shuai, Guo Yingkun, Liang Minmin, Wang Hongyang, Tian Jie

2019

an artificial intelligent signal amplification system, early diagnosis of precancerous lesions, fluorescent molecular tomography, in vivo detection of non-coding RNA, stem cell tracing

General General

Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities.

In Frontiers in chemistry ; h5-index 0.0

The drug development is generally arduous, costly, and success rates are low. Thus, the identification of drug-target interactions (DTIs) has become a crucial step in early stages of drug discovery. Consequently, developing computational approaches capable of identifying potential DTIs with minimum error rate are increasingly being pursued. These computational approaches aim to narrow down the search space for novel DTIs and shed light on drug functioning context. Most methods developed to date use binary classification to predict if the interaction between a drug and its target exists or not. However, it is more informative but also more challenging to predict the strength of the binding between a drug and its target. If that strength is not sufficiently strong, such DTI may not be useful. Therefore, the methods developed to predict drug-target binding affinities (DTBA) are of great value. In this study, we provide a comprehensive overview of the existing methods that predict DTBA. We focus on the methods developed using artificial intelligence (AI), machine learning (ML), and deep learning (DL) approaches, as well as related benchmark datasets and databases. Furthermore, guidance and recommendations are provided that cover the gaps and directions of the upcoming work in this research area. To the best of our knowledge, this is the first comprehensive comparison analysis of tools focused on DTBA with reference to AI/ML/DL.

Thafar Maha, Raies Arwa Bin, Albaradei Somayah, Essack Magbubah, Bajic Vladimir B

2019

artificial intelligence, bioinformatics, deep learning, drug repurposing, drug-target binding affinity, drug-target interaction, information integration, machine learning

General General

Development of Predictive Models for Identifying Potential S100A9 Inhibitors Based on Machine Learning Methods.

In Frontiers in chemistry ; h5-index 0.0

S100A9 is a potential therapeutic target for various disease including prostate cancer, colorectal cancer, and Alzheimer's disease. However, the sparsity of atomic level data, such as protein-protein interaction of S100A9 with RAGE, TLR4/MD2, or CD147 (EMMPRIN) hinders the rational drug design of S100A9 inhibitors. Herein we first report predictive models of S100A9 inhibitory effect by applying machine learning classifiers on 2D-molecular descriptors. The models were optimized through feature selectors as well as classifiers to produce the top eight random forest models with robust predictability and high cost-effectiveness. Notably, optimal feature sets were obtained after the reduction of 2,798 features into dozens of features with the chopping of fingerprint bits. Moreover, the high efficiency of compact feature sets allowed us to further screen a large-scale dataset (over 6,000,000 compounds) within a week. Through a consensus vote of the top models, 46 hits (hit rate = 0.000713%) were identified as potential S100A9 inhibitors. We expect that our models will facilitate the drug discovery process by providing high predictive power as well as cost-reduction ability and give insights into designing novel drugs targeting S100A9.

Lee Jihyeun, Kumar Surendra, Lee Sang-Yoon, Park Sung Jean, Kim Mi-Hyun

2019

“Alzheimers disease”, S100, classification, consensus vote, feature selection, ligand-based virtual screening, machine learning, random forest

General General

Integrative Multi-Kinase Approach for the Identification of Potent Antiplasmodial Hits.

In Frontiers in chemistry ; h5-index 0.0

Malaria is a tropical infectious disease that affects over 219 million people worldwide. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new antimalarial drugs is a global health priority. Multi-target drug discovery is a promising and innovative strategy for drug discovery and it is currently regarded as one of the best strategies to face drug resistance. Aiming to identify new multi-target antimalarial drug candidates, we developed an integrative computational approach to select multi-kinase inhibitors for Plasmodium falciparum calcium-dependent protein kinases 1 and 4 (CDPK1 and CDPK4) and protein kinase 6 (PK6). For this purpose, we developed and validated shape-based and machine learning models to prioritize compounds for experimental evaluation. Then, we applied the best models for virtual screening of a large commercial database of drug-like molecules. Ten computational hits were experimentally evaluated against asexual blood stages of both sensitive and multi-drug resistant P. falciparum strains. Among them, LabMol-171, LabMol-172, and LabMol-181 showed potent antiplasmodial activity at nanomolar concentrations (EC50 ≤ 700 nM) and selectivity indices >15 folds. In addition, LabMol-171 and LabMol-181 showed good in vitro inhibition of P. berghei ookinete formation and therefore represent promising transmission-blocking scaffolds. Finally, docking studies with protein kinases CDPK1, CDPK4, and PK6 showed structural insights for further hit-to-lead optimization studies.

Lima Marilia N N, Cassiano Gustavo C, Tomaz Kaira C P, Silva Arthur C, Sousa Bruna K P, Ferreira Leticia T, Tavella Tatyana A, Calit Juliana, Bargieri Daniel Y, Neves Bruno J, Costa Fabio T M, Andrade Carolina Horta

2019

Plasmodium falciparum, machine learning, malaria, multi-target, shape-based, virtual screening

Radiology Radiology

Evaluation of Lymph Node Metastasis in Advanced Gastric Cancer Using Magnetic Resonance Imaging-Based Radiomics.

In Frontiers in oncology ; h5-index 0.0

Objective: To develop and evaluate a diffusion-weighted imaging (DWI)-based radiomic nomogram for lymph node metastasis (LNM) prediction in advanced gastric cancer (AGC) patients. Overall Study: This retrospective study was conducted with 146 consecutively included pathologically confirmed AGC patients from two centers. All patients underwent preoperative 3.0 T magnetic resonance imaging (MRI) examination. The dataset was allocated to a training cohort (n = 71) and an internal validation cohort (n = 47) from one center along with an external validation cohort (n = 28) from another. A summary of 1,305 radiomic features were extracted per patient. The least absolute shrinkage and selection operator (LASSO) logistic regression and learning vector quantization (LVQ) methods with cross-validations were adopted to select significant features in a radiomic signature. Combining the radiomic signature and independent clinical factors, a radiomic nomogram was established. The MRI-reported N staging and the MRI-derived model were built for comparison. Model performance was evaluated considering receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA). Results: A two-feature radiomic signature was found significantly associated with LNM (p < 0.01, training and internal validation cohorts). A radiomic nomogram was established by incorporating the clinical minimum apparent diffusion coefficient (ADC) and MRI-reported N staging. The radiomic nomogram showed a favorable classification ability with an area under ROC curve of 0.850 [95% confidence interval (CI), 0.758-0.942] in the training cohort, which was then confirmed with an AUC of 0.857 (95% CI, 0.714-1.000) in internal validation cohort and 0.878 (95% CI, 0.696-1.000) in external validation cohort. Meanwhile, the specificity, sensitivity, and accuracy were 0.846, 0.853, and 0.851 in internal validation cohort, and 0.714, 0.952, and 0.893 in external validation cohort, compensating for the MRI-reported N staging and MRI-derived model. DCA demonstrated good clinical use of radiomic nomogram. Conclusions: This study put forward a DWI-based radiomic nomogram incorporating the radiomic signature, minimum ADC, and MRI-reported N staging for individualized preoperative detection of LNM in patients with AGC.

Chen Wujie, Wang Siwen, Dong Di, Gao Xuning, Zhou Kefeng, Li Jiaying, Lv Bin, Li Hailin, Wu Xiangjun, Fang Mengjie, Tian Jie, Xu Maosheng

2019

advanced gastric cancer, diffusion-weighted imaging, lymph node metastasis, magnetic resonance imaging, radiomics

General General

Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review.

In F1000Research ; h5-index 0.0

Background: Clinical decision support (CDS) systems have emerged as tools providing intelligent decision making to address challenges of critical care. CDS systems can be based on existing guidelines or best practices; and can also utilize machine learning to provide a diagnosis, recommendation, or therapy course. Methods: This research aimed to identify evidence-based study designs and outcome measures to determine the clinical effectiveness of clinical decision support systems in the detection and prediction of hemodynamic instability, respiratory distress, and infection within critical care settings. PubMed, ClinicalTrials.gov and Cochrane Database of Systematic Reviews were systematically searched to identify primary research published in English between 2013 and 2018. Studies conducted in the USA, Canada, UK, Germany and France with more than 10 participants per arm were included. Results: In studies on hemodynamic instability, the prediction and management of septic shock were the most researched topics followed by the early prediction of heart failure. For respiratory distress, the most popular topics were pneumonia detection and prediction followed by pulmonary embolisms. Given the importance of imaging and clinical notes, this area combined Machine Learning with image analysis and natural language processing. In studies on infection, the most researched areas were the detection, prediction, and management of sepsis, surgical site infections, as well as acute kidney injury. Overall, a variety of Machine Learning algorithms were utilized frequently, particularly support vector machines, boosting techniques, random forest classifiers and neural networks. Sensitivity, specificity, and ROC AUC were the most frequently reported performance measures. Conclusion: This review showed an increasing use of Machine Learning for CDS in all three areas. Large datasets are required for training these algorithms; making it imperative to appropriately address, challenges such as class imbalance, correct labelling of data and missing data. Recommendations are formulated for the development and successful adoption of CDS systems.

Medic Goran, Kosaner Kließ Melodi, Atallah Louis, Weichert Jochen, Panda Saswat, Postma Maarten, El-Kerdi Amer

2019

clinical trials, critical care., hemodynamic instability, infection, machine learning, respiratory distress, sepsis

General General

The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling.

In Frontiers in genetics ; h5-index 62.0

Pathway-centric approaches are widely used to interpret and contextualize -omics data. However, databases contain different representations of the same biological pathway, which may lead to different results of statistical enrichment analysis and predictive models in the context of precision medicine. We have performed an in-depth benchmarking of the impact of pathway database choice on statistical enrichment analysis and predictive modeling. We analyzed five cancer datasets using three major pathway databases and developed an approach to merge several databases into a single integrative one: MPath. Our results show that equivalent pathways from different databases yield disparate results in statistical enrichment analysis. Moreover, we observed a significant dataset-dependent impact on the performance of machine learning models on different prediction tasks. In some cases, MPath significantly improved prediction performance and also reduced the variance of prediction performances. Furthermore, MPath yielded more consistent and biologically plausible results in statistical enrichment analyses. In summary, this benchmarking study demonstrates that pathway database choice can influence the results of statistical enrichment analysis and predictive modeling. Therefore, we recommend the use of multiple pathway databases or integrative ones.

Mubeen Sarah, Hoyt Charles Tapley, Gemünd André, Hofmann-Apitius Martin, Fröhlich Holger, Domingo-Fernández Daniel

2019

benchmarking, databases, machine learning, pathway enrichment, statistical hypothesis testing

General General

CircSLNN: Identifying RBP-Binding Sites on circRNAs via Sequence Labeling Neural Networks.

In Frontiers in genetics ; h5-index 62.0

The interactions between RNAs and RNA binding proteins (RBPs) are crucial for understanding post-transcriptional regulation mechanisms. A lot of computational tools have been developed to automatically predict the binding relationship between RNAs and RBPs. However, most of the methods can only predict the presence or absence of binding sites for a sequence fragment, without providing specific information on the position or length of the binding sites. Besides, the existing tools focus on the interaction between RBPs and linear RNAs, while the binding sites on circular RNAs (circRNAs) have been rarely studied. In this study, we model the prediction of binding sites on RNAs as a sequence labeling problem, and propose a new model called circSLNN to identify the specific location of RBP-binding sites on circRNAs. CircSLNN is driven by pretrained RNA embedding vectors and a composite labeling model. On our constructed circRNA datasets, our model has an average F1 score of 0.790. We assess the performance on full-length RNA sequences, the proposed model outperforms previous classification-based models by a large margin.

Ju Yuqi, Yuan Liangliang, Yang Yang, Zhao Hai

2019

RNA–protein binding sites, bidirectional LSTM neural network, convolutional neural network, deep learning, sequence labeling

General General

Using machine learning models to improve stroke risk level classification methods of China national stroke screening.

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

BACKGROUND : With the character of high incidence, high prevalence and high mortality, stroke has brought a heavy burden to families and society in China. In 2009, the Ministry of Health of China launched the China national stroke screening and intervention program, which screens stroke and its risk factors and conducts high-risk population interventions for people aged above 40 years old all over China. In this program, stroke risk factors include hypertension, diabetes, dyslipidemia, smoking, lack of exercise, apparently overweight and family history of stroke. People with more than two risk factors or history of stroke or transient ischemic attack (TIA) are considered as high-risk. However, it is impossible for this criterion to classify stroke risk levels for people with unknown values in fields of risk factors. The missing of stroke risk levels results in reduced efficiency of stroke interventions and inaccuracies in statistical results at the national level. In this paper, we use 2017 national stroke screening data to develop stroke risk classification models based on machine learning algorithms to improve the classification efficiency.

METHOD : Firstly, we construct training set and test sets and process the imbalance training set based on oversampling and undersampling method. Then, we develop logistic regression model, Naïve Bayesian model, Bayesian network model, decision tree model, neural network model, random forest model, bagged decision tree model, voting model and boosting model with decision trees to classify stroke risk levels.

RESULT : The recall of the boosting model with decision trees is the highest (99.94%), and the precision of the model based on the random forest is highest (97.33%). Using the random forest model (recall: 98.44%), the recall will be increased by about 2.8% compared with the method currently used, and several thousands more people with high risk of stroke can be identified each year.

CONCLUSION : Models developed in this paper can improve the current screening method in the way that it can avoid the impact of unknown values, and avoid unnecessary rescreening and intervention expenditures. The national stroke screening program can choose classification models according to the practice need.

Li Xuemeng, Bian Di, Yu Jinghui, Li Mei, Zhao Dongsheng

2019-Dec-10

Machine learning models, National Stroke Screening, Risk level classification

General General

Graph Embedding Deep Learning Guides Microbial Biomarkers' Identification.

In Frontiers in genetics ; h5-index 62.0

The microbiome-wide association studies are to figure out the relationship between microorganisms and humans, with the goal of discovering relevant biomarkers to guide disease diagnosis. However, the microbiome data is complex, with high noise and dimensions. Traditional machine learning methods are limited by the models' representation ability and cannot learn complex patterns from the data. Recently, deep learning has been widely applied to fields ranging from text processing to image recognition due to its efficient flexibility and high capacity. But the deep learning models must be trained with enough data in order to achieve good performance, which is impractical in reality. In addition, deep learning is considered as black box and hard to interpret. These factors make deep learning not widely used in microbiome-wide association studies. In this work, we construct a sparse microbial interaction network and embed this graph into deep model to alleviate the risk of overfitting and improve the performance. Further, we explore a Graph Embedding Deep Feedforward Network (GEDFN) to conduct feature selection and guide meaningful microbial markers' identification. Based on the experimental results, we verify the feasibility of combining the microbial graph model with the deep learning model, and demonstrate the feasibility of applying deep learning and feature selection on microbial data. Our main contributions are: firstly, we utilize different methods to construct a variety of microbial interaction networks and combine the network via graph embedding deep learning. Secondly, we introduce a feature selection method based on graph embedding and validate the biological meaning of microbial markers. The code is available at https://github.com/MicroAVA/GEDFN.git.

Zhu Qiang, Jiang Xingpeng, Zhu Qing, Pan Min, He Tingting

2019

General General

Projection-Based Neighborhood Non-Negative Matrix Factorization for lncRNA-Protein Interaction Prediction.

In Frontiers in genetics ; h5-index 62.0

Many long ncRNAs (lncRNA) make their effort by interacting with the corresponding RNA-binding proteins, and identifying the interactions between lncRNAs and proteins is important to understand the functions of lncRNA. Compared with the time-consuming and laborious experimental methods, more and more computational models are proposed to predict lncRNA-protein interactions. However, few models can effectively utilize the biological network topology of lncRNA (protein) and combine its sequence structure features, and most models cannot effectively predict new proteins (lncRNA) that do not interact with any lncRNA (proteins). In this study, we proposed a projection-based neighborhood non-negative matrix decomposition model (PMKDN) to predict potential lncRNA-protein interactions by integrating multiple biological features of lncRNAs (proteins). First, according to lncRNA (protein) sequences and lncRNA expression profile data, we extracted multiple features of lncRNA (protein). Second, based on protein GO ontology annotation, lncRNA sequences, lncRNA(protein) feature information, and modified lncRNA-protein interaction network, we calculated multiple similarities of lncRNA (protein), and fused them to obtain a more accurate lncRNA(protein) similarity network. Finally, combining the similarity and various feature information of lncRNA (protein), as well as the modified interaction network, we proposed a projection-based neighborhood non-negative matrix decomposition algorithm to predict the potential lncRNA-protein interactions. On two benchmark datasets, PMKDN showed better performance than other state-of-the-art methods for the prediction of new lncRNA-protein interactions, new lncRNAs, and new proteins. Case study further indicates that PMKDN can be used as an effective tool for lncRNA-protein interaction prediction.

Ma Yingjun, He Tingting, Jiang Xingpeng

2019

feature projection, graph non-negative matrix factorization, kernel neighborhood similarity, lncRNA-protein interaction, neighborhood completion

General General

Deepprune: Learning Efficient and Interpretable Convolutional Networks Through Weight Pruning for Predicting DNA-Protein Binding.

In Frontiers in genetics ; h5-index 62.0

Convolutional neural network (CNN) based methods have outperformed conventional machine learning methods in predicting the binding preference of DNA-protein binding. Although studies in the past have shown that more convolutional kernels help to achieve better performance, visualization of the model can be obscured by the use of many kernels, resulting in overfitting and reduced interpretation because the number of motifs in true models is limited. Therefore, we aim to arrive at high performance, but with limited kernel numbers, in CNN-based models for motif inference. We herein present Deepprune, a novel deep learning framework, which prunes the weights in the dense layer and fine-tunes iteratively. These two steps enable the training of CNN-based models with limited kernel numbers, allowing easy interpretation of the learned model. We demonstrate that Deepprune significantly improves motif inference performance for the simulated datasets. Furthermore, we show that Deepprune outperforms the baseline with limited kernel numbers when inferring DNA-binding sites from ChIP-seq data.

Luo Xiao, Chi Weilai, Deng Minghua

2019

convolutional neural networks, deep neural networks, interpretation, motif inference, network pruning

General General

Phenotype Prediction and Genome-Wide Association Study Using Deep Convolutional Neural Network of Soybean.

In Frontiers in genetics ; h5-index 62.0

Genomic selection uses single-nucleotide polymorphisms (SNPs) to predict quantitative phenotypes for enhancing traits in breeding populations and has been widely used to increase breeding efficiency for plants and animals. Existing statistical methods rely on a prior distribution assumption of imputed genotype effects, which may not fit experimental datasets. Emerging deep learning technology could serve as a powerful machine learning tool to predict quantitative phenotypes without imputation and also to discover potential associated genotype markers efficiently. We propose a deep-learning framework using convolutional neural networks (CNNs) to predict the quantitative traits from SNPs and also to investigate genotype contributions to the trait using saliency maps. The missing values of SNPs are treated as a new genotype for the input of the deep learning model. We tested our framework on both simulation data and experimental datasets of soybean. The results show that the deep learning model can bypass the imputation of missing values and achieve more accurate results for predicting quantitative phenotypes than currently available other well-known statistical methods. It can also effectively and efficiently identify significant markers of SNPs and SNP combinations associated in genome-wide association study.

Liu Yang, Wang Duolin, He Fei, Wang Juexin, Joshi Trupti, Xu Dong

2019

deep learning, genome-wide association study, genomic selection, genotype contribution, soybean

General General

Cross-Validation of Functional MRI and Paranoid-Depressive Scale: Results From Multivariate Analysis.

In Frontiers in psychiatry ; h5-index 0.0

Introduction: There exists over the past decades a constant debate driven by controversies in the validity of psychiatric diagnosis. This debate is grounded in queries about both the validity and evidence strength of clinical measures. Materials and Methods: The objective of the study is to construct a bottom-up unsupervised machine learning approach, where the brain signatures identified by three principal components based on activations yielded from the three kinds of diagnostically relevant stimuli are used in order to produce cross-validation markers which may effectively predict the variance on the level of clinical populations and eventually delineate diagnostic and classification groups. The stimuli represent items from a paranoid-depressive self-evaluation scale, administered simultaneously with functional magnetic resonance imaging (fMRI). Results: We have been able to separate the two investigated clinical entities - schizophrenia and recurrent depression by use of multivariate linear model and principal component analysis. Following the individual and group MLM, we identified the three brain patterns that summarized all the individual variabilities of the individual brain patterns. Discussion: This is a confirmation of the possibility to achieve bottom-up classification of mental disorders, by use of the brain signatures relevant to clinical evaluation tests.

Stoyanov Drozdstoy, Kandilarova Sevdalina, Paunova Rositsa, Barranco Garcia Javier, Latypova Adeliya, Kherif Ferath

2019

classification, functional MRI, machine learning, psychopathology, validation

Radiology Radiology

Hippocampus Radiomic Biomarkers for the Diagnosis of Amnestic Mild Cognitive Impairment: A Machine Learning Method.

In Frontiers in aging neuroscience ; h5-index 64.0

Background: Recent evidence suggests the presence of hippocampal neuroanatomical abnormalities in subjects of amnestic mild cognitive impairment (aMCI). Our study aimed to identify the radiomic biomarkers of the hippocampus for building the classification models in aMCI diagnosis. Methods: For this target, we recruited 42 subjects with aMCI and 44 normal controls (NC). The right and left hippocampi were segmented for each subject using an efficient learning-based method. Then, the radiomic analysis was applied to calculate and select the radiomic features. Finally, two logistic regression models were built based on the selected features obtained from the right and left hippocampi. Results: There were 385 features derived after calculation, and four features remained after feature selection from each group of data. The area under the receiver operating characteristic (ROC) curve, specificity, sensitivity, positive predictive value, negative predictive value, precision, recall, and F-score of the classification evaluation index of the right hippocampus logistic regression model were 0.76, 0.71, 0.69, 0.69, 0.71, 0.69, 0.69, and 0.69, and those of the left hippocampus model were 0.79, 0.71, 0.54, 0.64, 0.63, 0.64, 0.54, and 0.58, respectively. Conclusion: Results demonstrate the potential hippocampal radiomic biomarkers are valid for the aMCI diagnosis. The MRI-based radiomic analysis, with further improvement and validation, can be used to identify patients with aMCI and guide the individual treatment.

Feng Qi, Song Qiaowei, Wang Mei, Pang PeiPei, Liao Zhengluan, Jiang Hongyang, Shen Dinggang, Ding Zhongxiang

2019

Alzheimer’s disease, amnestic mild cognitive impairment, hippocampus, machine learning, magnetic resonance imaging, radiomics

General General

Open-Environment Robotic Acoustic Perception for Object Recognition.

In Frontiers in neurorobotics ; h5-index 0.0

Object recognition in containers is extremely difficult for robots. Dynamic audio signals are more responsive to an object's internal property. Therefore, we adopt the dynamic contact method to collect acoustic signals in the container and recognize objects in containers. Traditional machine learning is to recognize objects in a closed environment, which is not in line with practical applications. In real life, exploring objects is dynamically changing, so it is necessary to develop methods that can recognize all classes of objects in an open environment. A framework for recognizing objects in containers using acoustic signals in an open environment is proposed, and then the kernel k nearest neighbor algorithm in an open environment (OSKKNN) is set. An acoustic dataset is collected, and the feasibility of the method is verified on the dataset, which greatly promotes the recognition of objects in an open environment. And it also proves that the use of acoustic to recognize objects in containers has good value.

Jin Shaowei, Liu Huaping, Wang Bowen, Sun Fuchun

2019

acoustic features, interactive perception, kernel k nearest neighbor, object recognition, objects in containers, open environment

General General

Whole-Brain Exploratory Analysis of Functional Task Response Following Erythropoietin Treatment in Mood Disorders: A Supervised Machine Learning Approach.

In Frontiers in neuroscience ; h5-index 72.0

A core symptom of mood disorders is cognitive impairment in attention, memory and executive functions. Erythropoietin (EPO) is a candidate treatment for cognitive impairment in unipolar and bipolar disorders (UD and BD) and modulates cognition-related neural activity across a fronto-temporo-parietal network. This report investigates predicting the pharmacological treatment from functional magnetic resonance imaging (fMRI) data using a supervised machine learning approach. A total of 84 patients with UD or BD were included in a randomized double-blind parallel-group study in which they received eight weekly infusions of either EPO (40 000 IU) or saline. Task fMRI data were collected before EPO/saline infusions started (baseline) and 6 weeks after last infusion (follow-up). During the scanning sessions, participants were given an n-back working memory and a picture encoding task. Linear classification models with different regularization techniques were used to predict treatment status from both cross-sectional data (at follow-up) and longitudinal data (difference between baseline and follow-up). For the n-back and picture encoding tasks, data were available and analyzed for 52 (EPO; n = 28, Saline; n = 24) and 59 patients (EPO; n = 31, Saline; n = 28), respectively. We found limited evidence that the classifiers used could predict treatment status at a reliable level of performance (≤60% accuracy) when tested using repeated cross-validation. There was no difference in using cross-sectional versus longitudinal data. Whole-brain multivariate decoding applied to pharmaco-fMRI in small to moderate samples seems to be suboptimal for exploring data driven neuronal treatment mechanisms.

Nielsen Søren F V, Madsen Kristoffer H, Vinberg Maj, Kessing Lars V, Siebner Hartwig R, Miskowiak Kamilla W

2019

cognitive dysfunction, erythropoietin, functional magnetic resonance imaging, machine learning, mood disorders

General General

A homotopy training algorithm for fully connected neural networks.

In Proceedings. Mathematical, physical, and engineering sciences ; h5-index 0.0

In this paper, we present a homotopy training algorithm (HTA) to solve optimization problems arising from fully connected neural networks with complicated structures. The HTA dynamically builds the neural network starting from a simplified version and ending with the fully connected network via adding layers and nodes adaptively. Therefore, the corresponding optimization problem is easy to solve at the beginning and connects to the original model via a continuous path guided by the HTA, which provides a high probability of obtaining a global minimum. By gradually increasing the complexity of the model along the continuous path, the HTA provides a rather good solution to the original loss function. This is confirmed by various numerical results including VGG models on CIFAR-10. For example, on the VGG13 model with batch normalization, HTA reduces the error rate by 11.86% on the test dataset compared with the traditional method. Moreover, the HTA also allows us to find the optimal structure for a fully connected neural network by building the neutral network adaptively.

Chen Qipin, Hao Wenrui

2019-Nov

homotopy method, machine learning, neural network, training algorithm

Public Health Public Health

Cost-Effectiveness Analysis Of EGFR Mutation Testing And Afatinib Versus Gemcitabine-Cisplatin As First-Line Therapy For Advanced Non-Small-Cell Lung Cancer In China.

In Cancer management and research ; h5-index 0.0

Objective : The purpose of this study was to evaluate the cost-effectiveness of the combined use of afatinib and epidermal growth factor receptor (EGFR) testing versus gemcitabine-cisplatin as the first-line treatment for patients with non-small cell lung cancer (NSCLC) in China.

Methods : A decision-analytic model, based on clinical phase III trials, was developed to simulate patient transitions. Direct costs were estimated from the perspective of the Chinese healthcare system. Quality-adjusted life-years (QALYs) and incremental cost-effectiveness ratios (ICER) were calculated over a 5-year lifetime horizon. Model robustness was conducted in sensitivity analyses.

Results : For the base case, EGFR mutation testing followed by afatinib treatment for advanced NSCLC increased 0.15 QALYs compared with standard chemotherapy at an additional cost of $5069.12. The ICER for afatinib maintenance was $33,416.39 per QALY gained. The utility of PFS and the cost of afatinib had the most important impact on the ICER. Scenario analyses suggested that when a patient assistance program (PAP) was available, ICER decreased to $22,972.52/QALY lower than the willingness-to-pay (WTP) threshold of China ($26,508/QALY).

Conclusion : Our results suggest that gene-guided maintenance therapy with afatinib with the PAP might be a cost-effective treatment option compared with gemcitabine - cisplatin in China.

You Ruxu, Liu Jinyu, Wu David Bin-Chia, Qian XinYu, Lyu Boxiang, Zhang Yu, Luo Nan

2019

Afatinib, EGER mutation testing, Economic analysis, NSCLC, incremental cost-effectiveness ratio

General General

evERdock BAI: Machine-learning-guided selection of protein-protein complex structure.

In The Journal of chemical physics ; h5-index 0.0

Computational techniques for accurate and efficient prediction of protein-protein complex structures are widely used for elucidating protein-protein interactions, which play important roles in biological systems. Recently, it has been reported that selecting a structure similar to the native structure among generated structure candidates (decoys) is possible by calculating binding free energies of the decoys based on all-atom molecular dynamics (MD) simulations with explicit solvent and the solution theory in the energy representation, which is called evERdock. A recent version of evERdock achieves a higher-accuracy decoy selection by introducing MD relaxation and multiple MD simulations/energy calculations; however, huge computational cost is required. In this paper, we propose an efficient decoy selection method using evERdock and the best arm identification (BAI) framework, which is one of the techniques of reinforcement learning. The BAI framework realizes an efficient selection by suppressing calculations for nonpromising decoys and preferentially calculating for the promising ones. We evaluate the performance of the proposed method for decoy selection problems of three protein-protein complex systems. Their results show that computational costs are successfully reduced by a factor of 4.05 (in the best case) compared to a standard decoy selection approach without sacrificing accuracy.

Terayama Kei, Shinobu Ai, Tsuda Koji, Takemura Kazuhiro, Kitao Akio

2019-Dec-07

Surgery Surgery

A personalized computational model predicts cancer risk level of oral potentially malignant disorders and its web application for promotion of non-invasive screening.

In Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology ; h5-index 0.0

BACKGROUND : Despite their high accuracy to recognize oral potentially malignant disorders (OPMDs) with cancer risk, non-invasive oral assays are poor in discerning whether the risk is high or low. However, it is critical to identify the risk levels, since high-risk patients need active intervention, while low-risk ones simply need to be follow-up. This study aimed at developing a personalized computational model to predict cancer risk level of OPMDs and explore its potential web application in OPMDs screening.

METHODS : Each enrolled patient was subjected to the following procedure: personal information collection, non-invasive oral examination, oral tissue biopsy and histopathological analysis, treatment and follow-up. Patients were randomly divided into a training set (N=159) and a test set (N=107). Random forest was used to establish classification models. A baseline model (model-B) and a personalized model (model-P) were created. The former used the non-invasive scores only, while the latter was incremented with appropriate personal features.

RESULTS : We compared the respective performance of cancer risk level prediction by model-B, model-P and clinical experts. Our data suggested that all three have a similar level of specificity around 90%. In contrast, the sensitivity of model-P is beyond 80% and superior to the other two. The improvement of sensitivity by model-P reduced the misclassification of high-risk patients as low-risk ones. We deployed model-P in web.opmd-risk.com, which can be freely and conveniently accessed.

CONCLUSION : We have proposed a novel machine-learning model for precise and cost-effective OPMDs screening, which integrates clinical examinations, machine learning and information technology.

Wang Xiangjian, Yang Jin, Wei Changlei, Zhou Gang, Wu Lanyan, Gao Qinghong, He Xin, Shi Jiahong, Mei Yingying, Liu Ying, Shi Xueke, Wu Fanglong, Luo Jingjing, Guo Yiqing, Zhou Qizhi, Yin Jiaxin, Hu Tao, Lin Mei, Liang Zhi, Zhou Hongmei

2019-Dec-10

Cancer risk level prediction, Non-invasive screening, Oral potentially malignant disorders, Personalized model, Web application

General General

Application of automated electron microscopy imaging and machine learning to characterise and quantify nanoparticle dispersion in aqueous media.

In Journal of microscopy ; h5-index 0.0

For many nanoparticle applications it is important to understand dispersion in liquids. For nanomedicinal and nanotoxicological research this is complicated by the often complex nature of the biological dispersant and ultimately this leads to severe limitations in the analysis of the nanoparticle dispersion by light scattering techniques. Here we present an alternative analysis and associated workflow which utilises electron microscopy. The need to collect large, statistically-relevant datasets by imaging vacuum dried, plunge frozen aliquots of suspension was accomplished by developing an automated STEM imaging protocol implemented in an SEM fitted with a transmission detector. Automated analysis of images of agglomerates was achieved by machine learning using two free open-source software tools: CellProfiler and ilastik. The specific results and overall workflow described enable accurate nanoparticle agglomerate analysis of particles suspended in aqueous media containing other potential confounding components such as salts, vitamins and proteins. This article is protected by copyright. All rights reserved.

Ilett M, Wills J, Rees P, Sharma S, Micklethwaite S, Brown A, Brydson R, Hondow N

2019-Dec-11

Automated imaging, agglomeration, machine learning, nanoparticles

General General

Massive Open Online Courses: Current and Future Trends in Biomedical Sciences.

In Advances in experimental medicine and biology ; h5-index 0.0

The first massive open online courses or MOOCs were offered in 2008 in the USA, since then MOOCs have hit the higher education (HE) section by storm and have continued to grow rapidly since 2012, with hundreds of HE establishments across the globe engaged in providing MOOCs. MOOCs are online courses that are open to everyone and anyone to join with typically no limits to the number of participants or prerequisite qualifications. In some MOOCs there is an option to pay for a certificate upon completion. This chapter captures the use and future of MOOCs in the biomedical sciences. As the number of MOOCs available in biomedical subject areas grow, so do the number of participants taking these courses, with many of these learners and professionals looking to update their knowledge in the biomedical sciences.There is also a growing use of MOOCs in higher education as a recourse for campus degree programmes, known as hybrid MOOCs, where the MOOC provides the learning and the assessment is undertaken by the educational institution. The growing number of MOOCs available for credit is changing the way some learners are accessing higher education and the development of micro degrees obtained through the completion of a number of MOOCs may potentially change the way higher education is provided in the future. Finally, the potential of artificial intelligence to provide virtual classroom assistants is also a possible game changer, allowing more personalised learning to be delivered at scale.

Murray Jo-Anne

2019

Biomedical, Open online courses (MOOC)

General General

Ethical considerations about artificial intelligence for prognostication in intensive care.

In Intensive care medicine experimental ; h5-index 0.0

BACKGROUND : Prognosticating the course of diseases to inform decision-making is a key component of intensive care medicine. For several applications in medicine, new methods from the field of artificial intelligence (AI) and machine learning have already outperformed conventional prediction models. Due to their technical characteristics, these methods will present new ethical challenges to the intensivist.

RESULTS : In addition to the standards of data stewardship in medicine, the selection of datasets and algorithms to create AI prognostication models must involve extensive scrutiny to avoid biases and, consequently, injustice against individuals or groups of patients. Assessment of these models for compliance with the ethical principles of beneficence and non-maleficence should also include quantification of predictive uncertainty. Respect for patients' autonomy during decision-making requires transparency of the data processing by AI models to explain the predictions derived from these models. Moreover, a system of continuous oversight can help to maintain public trust in this technology. Based on these considerations as well as recent guidelines, we propose a pathway to an ethical implementation of AI-based prognostication. It includes a checklist for new AI models that deals with medical and technical topics as well as patient- and system-centered issues.

CONCLUSION : AI models for prognostication will become valuable tools in intensive care. However, they require technical refinement and a careful implementation according to the standards of medical ethics.

Beil Michael, Proft Ingo, van Heerden Daniel, Sviri Sigal, van Heerden Peter Vernon

2019-Dec-10

Artificial intelligence, Intensive care, Machine learning, Medical ethics, Prognostication

General General

Prediction of lower limb joint angles and moments during gait using artificial neural networks.

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

In recent years, gait analysis outside the laboratory attracts more and more attention in clinical applications as well as in life sciences. Wearable sensors such as inertial sensors show high potential in these applications. Unfortunately, they can only measure kinematic motions patterns indirectly and the outcome is currently jeopardized by measurement discrepancies compared with the gold standard of optical motion tracking. The aim of this study was to overcome the limitation of measurement discrepancies and the missing information on kinetic motion parameters using a machine learning application based on artificial neural networks. For this purpose, inertial sensor data-linear acceleration and angular rate-was simulated from a database of optical motion tracking data and used as input for a feedforward and long short-term memory neural network to predict the joint angles and moments of the lower limbs during gait. Both networks achieved mean correlation coefficients higher than 0.80 in the minor motion planes, and correlation coefficients higher than 0.98 in the sagittal plane. These results encourage further applications of artificial intelligence to support gait analysis. Graphical Abstract The graphical abstract displays the processing of the data: IMU data is used as input to a feedforward and a long short-term memory neural network to predict the joint kinematics and kinetics of the lower limbs during gait.

Mundt Marion, Thomsen Wolf, Witter Tom, Koeppe Arnd, David Sina, Bamer Franz, Potthast Wolfgang, Markert Bernd

2019-Dec-11

Data augmentation, Data simulation, IMU, Inertial sensors, Machine learning

Surgery Surgery

A novel machine learning based computational framework for homogenization of heterogeneous soft materials: application to liver tissue.

In Biomechanics and modeling in mechanobiology ; h5-index 36.0

Real-time simulation of organs increases comfort and safety for patients during the surgery. Proper generalized decomposition (PGD) is an efficient numerical method with coordinate errors below 1 mm and response time below 0.1 s that can be used for simulated surgery. For input of this approach, nonlinear mechanical properties of each segment of the liver need to be calculated based on the geometries of the patient's liver extracted using medical imaging techniques. In this research work, a map of the mechanical properties of the liver tissue has been estimated with a novel combined method of the finite element (FE) optimization. Due to the existence of major-size vessels in the liver that makes the surrounding tissue anisotropic, the simulation of hyperelastic material with two different sections is computationally expensive. Thus, a homogenized, anisotropic, and hyperelastic model with the nearest response to the real heterogeneous model was developed and presented. Because of various possibilities of the vessel orientation, position, and size, homogenization has been carried out for adequate samples of heterogeneous models to train artificial neural networks (ANNs) as machine learning tools. Then, an unknown sample of heterogeneous material was categorized and mapped to its homogenized material parameters with the trained networks for the fast and low-cost generalization of our combined FE optimization method. The results showed the efficiency of the proposed novel machine learning based technique for the prediction of effective material properties of unknown heterogeneous tissues.

Hashemi Mohammad Saber, Baniassadi Majid, Baghani Mostafa, George Daniel, Remond Yves, Sheidaei Azadeh

2019-Dec-10

Anisotropic hyperelastic material, Artificial neural network (ANN), Finite element analysis (FEA), Mechanical homogenization, Optimization

General General

Estimation of Arterial Blood Pressure Based on Artificial Intelligence Using Single Earlobe Photoplethysmography during Cardiopulmonary Resuscitation.

In Journal of medical systems ; h5-index 48.0

This study investigates the feasibility of estimation of blood pressure (BP) using a single earlobe photoplethysmography (Ear PPG) during cardiopulmonary resuscitation (CPR). We have designed a system that carries out Ear PPG for estimation of BP. In particular, the BP signals are estimated according to a long short-term memory (LSTM) model using an Ear PPG. To investigate the proposed method, two statistical analyses were conducted for comparison between BP measured by the micromanometer-based gold standard method (BPMEAS) and the Ear PPG-based proposed method (BPEST) for swine cardiac model. First, Pearson's correlation analysis showed high positive correlations (r = 0.92, p < 0.01) between BPMEAS and BPEST. Second, the paired-samples t-test on the BP parameters (systolic and diastolic blood pressure) of the two methods indicated no significant differences (p > 0.05). Therefore, the proposed method has the potential for estimation of BP for CPR biofeedback based on LSTM using a single Ear PPG.

Park Jong-Uk, Kang Dong-Won, Erdenebayar Urtnasan, Kim Yoon-Ji, Cha Kyoung-Chul, Lee Kyoung-Joung

2019-Dec-10

Biofeedback, Blood pressure (BP), Cardiopulmonary resuscitation (CPR), Earlobe photoplethysmography (ear PPG), Long short-term memory (LSTM)

Surgery Surgery

Artificial Intelligence: A New Tool in Operating Room Management. Role of Machine Learning Models in Operating Room Optimization.

In Journal of medical systems ; h5-index 48.0

We conducted a systematic review of literature to better understand the role of new technologies in the perioperative period; in particular we focus on the administrative and managerial Operating Room (OR) perspective. Studies conducted on adult (≥ 18 years) patients between 2015 and February 2019 were deemed eligible. A total of 19 papers were included. Our review suggests that the use of Machine Learning (ML) in the field of OR organization has many potentials. Predictions of the surgical case duration were obtain with a good performance; their use could therefore allow a more precise scheduling, limiting waste of resources. ML is able to support even more complex models, which can coordinate multiple spaces simultaneously, as in the case of the post-anesthesia care unit and operating rooms. Types of Artificial Intelligence could also be used to limit another organizational problem, which has important economic repercussions: cancellation. Random Forest has proven effective in identifing surgeries with high risks of cancellation, allowing to plan preventive measures to reduce the cancellation rate accordingly. In conclusion, although data in literature are still limited, we believe that ML has great potential in the field of OR organization; however, further studies are needed to assess the effective role of these new technologies in the perioperative medicine.

Bellini Valentina, Guzzon Marco, Bigliardi Barbara, Mordonini Monica, Filippelli Serena, Bignami Elena

2019-Dec-10

Anesthesia, Artificial intelligence, Big data, Block time, Hospital administration, Machine learning, Operating room, Operating room efficiency, Perioperative, Recovery room, Robotic assisted surgery, Scheduling

General General

Multiple genetic programming: a new approach to improve genetic-based month ahead rainfall forecasts.

In Environmental monitoring and assessment ; h5-index 0.0

It is well documented that standalone machine learning methods are not suitable for rainfall forecasting in long lead-time horizons. The task is more difficult in arid and semiarid regions. Addressing these issues, the present paper introduces a hybrid machine learning model, namely multiple genetic programming (MGP), that improves the predictive accuracy of the standalone genetic programming (GP) technique when used for 1-month ahead rainfall forecasting. The new model uses a multi-step evolutionary search algorithm in which high-performance rain-borne genes from a multigene GP solution are recombined through a classic GP engine. The model is demonstrated using rainfall measurements from two meteorology stations in Lake Urmia Basin, Iran. The efficiency of the MGP was cross-validated against the benchmark models, namely standard GP and autoregressive state-space. The results indicated that the MGP statistically outperforms the benchmarks at both rain gauge stations. It may reduce the absolute and relative errors by approximately up to 15% and 40%, respectively. This significant improvement over standalone GP together with the explicit structure of the MGP model endorse its application for 1-month ahead rainfall forecasting in practice.

Danandeh Mehr Ali, Safari Mir Jafar Sadegh

2019-Dec-10

Genetic programming, Hybrid models, Rainfall, Stochastic modelling

Radiology Radiology

Artificial intelligence for detecting small FDG-positive lung nodules in digital PET/CT: impact of image reconstructions on diagnostic performance.

In European radiology ; h5-index 62.0

OBJECTIVES : To evaluate the diagnostic performance of a deep learning algorithm for automated detection of small 18F-FDG-avid pulmonary nodules in PET scans, and to assess whether novel block sequential regularized expectation maximization (BSREM) reconstruction affects detection accuracy as compared to ordered subset expectation maximization (OSEM) reconstruction.

METHODS : Fifty-seven patients with 92 18F-FDG-avid pulmonary nodules (all ≤ 2 cm) undergoing PET/CT for oncological (re-)staging were retrospectively included and a total of 8824 PET images of the lungs were extracted using OSEM and BSREM reconstruction. Per-slice and per-nodule sensitivity of a deep learning algorithm was assessed, with an expert readout by a radiologist/nuclear medicine physician serving as standard of reference. Receiver-operator characteristic (ROC) curve of OSEM and BSREM were assessed and the areas under the ROC curve (AUC) were compared. A maximum standardized uptake value (SUVmax)-based sensitivity analysis and a size-based sensitivity analysis with subgroups defined by nodule size was performed.

RESULTS : The AUC of the deep learning algorithm for nodule detection using OSEM reconstruction was 0.796 (CI 95%; 0.772-0.869), and 0.848 (CI 95%; 0.828-0.869) using BSREM reconstruction. The AUC was significantly higher for BSREM compared to OSEM (p = 0.001). On a per-slice analysis, sensitivity and specificity were 66.7% and 79.0% for OSEM, and 69.2% and 84.5% for BSREM. On a per-nodule analysis, the overall sensitivity of OSEM was 81.5% compared to 87.0% for BSREM.

CONCLUSIONS : Our results suggest that machine learning algorithms may aid detection of small 18F-FDG-avid pulmonary nodules in clinical PET/CT. AI performed significantly better on images with BSREM than OSEM.

KEY POINTS : • The diagnostic value of deep learning for detecting small lung nodules (≤ 2 cm) in PET images using BSREM and OSEM reconstruction was assessed. • BSREM yields higher SUV maxof small pulmonary nodules as compared to OSEM reconstruction. • The use of BSREM translates into a higher detectability of small pulmonary nodules in PET images as assessed with artificial intelligence.

Schwyzer Moritz, Martini Katharina, Benz Dominik C, Burger Irene A, Ferraro Daniela A, Kudura Ken, Treyer Valerie, von Schulthess Gustav K, Kaufmann Philipp A, Huellner Martin W, Messerli Michael

2019-Dec-10

Artificial intelligence, Deep learning, Diagnostic imaging, Neoplasm Metastasis, Positron-emission tomography

Radiology Radiology

The Algorithmic Audit: Working with Vendors to Validate Radiology-AI Algorithms-How We Do It.

In Academic radiology ; h5-index 0.0

There is a plethora of Artificial Intelligence (AI) tools that are being developed around the world aiming at either speeding up or improving the accuracy of radiologists. It is essential for radiologists to work with the developers of such algorithms to determine true clinical utility and risks associated with these algorithms. We present a framework, called an Algorithmic Audit, for working with the developers of such algorithms to test and improve the performance of the algorithms. The framework includes concepts of true independent validation on data that the algorithm has not seen before, curating datasets for such testing, deep examination of false positives and false negatives (to examine implications of such errors) and real-world deployment and testing of algorithms.

Mahajan Vidur, Venugopal Vasantha Kumar, Murugavel Murali, Mahajan Harsh

2020-Jan

Accuracy, Artificial Intelligence, Deployment, Testing, Validation

Radiology Radiology

Machine Learning Principles for Radiology Investigators.

In Academic radiology ; h5-index 0.0

Artificial intelligence and deep learning are areas of high interest for radiology investigators at present. However, the field of machine learning encompasses multiple statistics-based techniques useful for investigators, which may be complementary to deep learning approaches. After a refresher in basic statistical concepts, relevant considerations for machine learning practitioners are reviewed: regression, classification, decision boundaries, and bias-variance tradeoff. Regularization, ground truth, and populations are discussed along with compute and data management principles. Advanced statistical machine learning techniques including bootstrapping, bagging, boosting, decision trees, random forest, XGboost, and support vector machines are reviewed along with relevant examples from the radiology literature.

Borstelmann Stephen M

2020-Jan

AI, Artificial Intelligence, Data Science, Machine Learning, Radiology, Review, Statistics

Radiology Radiology

Artificial Intelligence in Radiology: Some Ethical Considerations for Radiologists and Algorithm Developers.

In Academic radiology ; h5-index 0.0

As artificial intelligence (AI) is finding its place in radiology, it is important to consider how to guide the research and clinical implementation in a way that will be most beneficial to patients. Although there are multiple aspects of this issue, I consider a specific one: a potential misalignment of the self-interests of radiologists and AI developers with the best interests of the patients. Radiologists know that supporting research into AI and advocating for its adoption in clinical settings could diminish their employment opportunities and reduce respect for their profession. This provides an incentive to oppose AI in various ways. AI developers have an incentive to hype their discoveries to gain attention. This could provide short-term personal gains, however, it could also create a distrust toward the field if it became apparent that the state of the art was far from where it was promised to be. The future research and clinical implementation of AI in radiology will be partially determined by radiologist and AI researchers. Therefore, it is very important that we recognize our own personal motivations and biases and act responsibly to ensure the highest benefit of the AI transformation to the patients.

Mazurowski Maciej A

2020-Jan

Algorithm development, Artificial intelligence, Ethics, Machine learning

Radiology Radiology

Artificial Intelligence in Radiology: Summary of the AUR Academic Radiology and Industry Leaders Roundtable.

In Academic radiology ; h5-index 0.0

The AUR Academic Radiology and Industry Leaders Roundtable was organized as an open discussion between academic leaders of top US academic radiology departments and industry leaders from top companies that provide equipment and services to radiology, including manufacturers, pharmaceutical companies, software developers and electronic medical record (EMR) providers. The format was that of a structured brainstorming session with pre-selected discussion topics. This roundtable was instrumental in widening perspectives and providing insights into the challenges and opportunities for our specialty, such as in the case of Artificial Intelligence (AI).

Chan Stephen, Bailey Janet, Ros Pablo R

2020-Jan

academic radiology, academic-industry partnerships, artificial intelligence

General General

Representation learning in intraoperative vital signs for heart failure risk prediction.

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

BACKGROUND : The probability of heart failure during the perioperative period is 2% on average and it is as high as 17% when accompanied by cardiovascular diseases in China. It has been the most significant cause of postoperative death of patients. However, the patient is managed by the flow of information during the operation, but a lot of clinical information can make it difficult for medical staff to identify the information relevant to patient care. There are major practical and technical barriers to understand perioperative complications.

METHODS : In this work, we present three machine learning methods to estimate risks of heart failure, which extract intraoperative vital signs monitoring data into different modal representations (statistical learning representation, text learning representation, image learning representation). Firstly, we extracted features of vital signs monitoring data of surgical patients by statistical analysis. Secondly, the vital signs data is converted into text information by Piecewise Approximate Aggregation (PAA) and Symbolic Aggregate Approximation (SAX), then Latent Dirichlet Allocation (LDA) model is used to extract text topics of patients for heart failure prediction. Thirdly, the vital sign monitoring time series data of the surgical patient is converted into a grid image by using the grid representation, and then the convolutional neural network is directly used to identify the grid image for heart failure prediction. We evaluated the proposed methods in the monitoring data of real patients during the perioperative period.

RESULTS : In this paper, the results of our experiment demonstrate the Gradient Boosting Decision Tree (GBDT) classifier achieves the best results in the prediction of heart failure by statistical feature representation. The sensitivity, specificity and the area under the curve (AUC) of the best method can reach 83, 85 and 84% respectively.

CONCLUSIONS : The experimental results demonstrate that representation learning model of vital signs monitoring data of intraoperative patients can effectively capture the physiological characteristics of postoperative heart failure.

Chen Yuwen, Qi Baolian

2019-Dec-09

Heart failure, Machine learning, Perioperative period

General General

tRNA functional signatures classify plastids as late-branching cyanobacteria.

In BMC evolutionary biology ; h5-index 0.0

BACKGROUND : Eukaryotes acquired the trait of oxygenic photosynthesis through endosymbiosis of the cyanobacterial progenitor of plastid organelles. Despite recent advances in the phylogenomics of Cyanobacteria, the phylogenetic root of plastids remains controversial. Although a single origin of plastids by endosymbiosis is broadly supported, recent phylogenomic studies are contradictory on whether plastids branch early or late within Cyanobacteria. One underlying cause may be poor fit of evolutionary models to complex phylogenomic data.

RESULTS : Using Posterior Predictive Analysis, we show that recently applied evolutionary models poorly fit three phylogenomic datasets curated from cyanobacteria and plastid genomes because of heterogeneities in both substitution processes across sites and of compositions across lineages. To circumvent these sources of bias, we developed CYANO-MLP, a machine learning algorithm that consistently and accurately phylogenetically classifies ("phyloclassifies") cyanobacterial genomes to their clade of origin based on bioinformatically predicted function-informative features in tRNA gene complements. Classification of cyanobacterial genomes with CYANO-MLP is accurate and robust to deletion of clades, unbalanced sampling, and compositional heterogeneity in input tRNA data. CYANO-MLP consistently classifies plastid genomes into a late-branching cyanobacterial sub-clade containing single-cell, starch-producing, nitrogen-fixing ecotypes, consistent with metabolic and gene transfer data.

CONCLUSIONS : Phylogenomic data of cyanobacteria and plastids exhibit both site-process heterogeneities and compositional heterogeneities across lineages. These aspects of the data require careful modeling to avoid bias in phylogenomic estimation. Furthermore, we show that amino acid recoding strategies may be insufficient to mitigate bias from compositional heterogeneities. However, the combination of our novel tRNA-specific strategy with machine learning in CYANO-MLP appears robust to these sources of bias with high accuracy in phyloclassification of cyanobacterial genomes. CYANO-MLP consistently classifies plastids as late-branching Cyanobacteria, consistent with independent evidence from signature-based approaches and some previous phylogenetic studies.

Lawrence Travis J, Amrine Katherine Ch, Swingley Wesley D, Ardell David H

2019-Dec-09

Cyanobacteria, Machine learning, Plastids, Primary endosymbiosis, tRNAs

General General

Body Composition Analysis of Computed Tomography Scans in Clinical Populations: The Role of Deep Learning.

In Lifestyle genomics ; h5-index 0.0

BACKGROUND : Body composition is increasingly being recognized as an important prognostic factor for health outcomes across cancer, liver cirrhosis, and critically ill patients. Computed tomography (CT) scans, when taken as part of routine care, provide an excellent opportunity to precisely measure the quantity and quality of skeletal muscle and adipose tissue. However, manual analysis of CT scans is costly and time-intensive, limiting the widespread adoption of CT-based measurements of body composition.

SUMMARY : Advances in deep learning have demonstrated excellent success in biomedical image analysis. Several recent publications have demonstrated excellent accuracy in comparison to human raters for the measurement of skeletal muscle, visceral adipose, and subcutaneous adipose tissue from the lumbar vertebrae region, indicating that analysis of body composition may be successfully automated using deep neural networks. Key Messages: The high accuracy and drastically improved speed of CT body composition analysis (<1 s/scan for neural networks vs. 15 min/scan for human analysis) suggest that neural networks may aid researchers and clinicians in better understanding the role of body composition in clinical populations by enabling cost-effective, large-scale research studies. As the role of body composition in clinical settings and the field of automated analysis advance, it will be critical to examine how clinicians interact with these systems and to evaluate whether these technologies are beneficial in improving treatment and health outcomes for patients.

Paris Michael T

2019-Dec-10

Automated body composition analysis, Computed tomography, Deep learning, Sarcopenia

General General

Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture.

In Neural networks : the official journal of the International Neural Network Society ; h5-index 0.0

A deep learning classifier for detecting seizures in neonates is proposed. This architecture is designed to detect seizure events from raw electroencephalogram (EEG) signals as opposed to the state-of-the-art hand engineered feature-based representation employed in traditional machine learning based solutions. The seizure detection system utilises only convolutional layers in order to process the multichannel time domain signal and is designed to exploit the large amount of weakly labelled data in the training stage. The system performance is assessed on a large database of continuous EEG recordings of 834h in duration; this is further validated on a held-out publicly available dataset and compared with two baseline SVM based systems. The developed system achieves a 56% relative improvement with respect to a feature-based state-of-the art baseline, reaching an AUC of 98.5%; this also compares favourably both in terms of performance and run-time. The effect of varying architectural parameters is thoroughly studied. The performance improvement is achieved through novel architecture design which allows more efficient usage of available training data and end-to-end optimisation from the front-end feature extraction to the back-end classification. The proposed architecture opens new avenues for the application of deep learning to neonatal EEG, where the performance becomes a function of the amount of training data with less dependency on the availability of precise clinical labels.

O’Shea Alison, Lightbody Gordon, Boylan Geraldine, Temko Andriy

2019-Nov-30

Convolutional neural networks, EEG, Neonatal seizure detection, Weak labels

General General

Deep learning segmentation of orbital fat to calibrate conventional MRI for longitudinal studies.

In NeuroImage ; h5-index 117.0

In conventional non-quantitative magnetic resonance imaging, image contrast is consistent within images, but absolute intensity can vary arbitrarily between scans. For quantitative analysis of intensity data, images are typically normalized to a consistent reference. The most convenient reference is a tissue that is always present in the image, and is unlikely to be affected by pathological processes. In multiple sclerosis neuroimaging, both the white and gray matter are affected, so normalization techniques that depend on brain tissue may introduce bias or remove biological changes of interest. We introduce a complementary procedure, image "calibration," the goal of which is to remove technical intensity artifacts while preserving biological differences. We demonstrate a deep learning approach to segmenting fat from within the orbit of the eyes on T1-weighted images at 1.5 and 3 T to use as a reference tissue, and use it to calibrate 1018 scans from 256 participants in a study of pediatric-onset multiple sclerosis. The machine segmentations agreed with the adjudicating expert (DF) segmentations better than did those of other expert humans, and calibration resulted in better agreement with semi-quantitative magnetization transfer ratio imaging than did normalization with the WhiteStripe1 algorithm. We suggest that our method addresses two key priorities in the field: (1) it provides a robust option for serial calibration of conventional scans, allowing comparison of disease change in persons imaged at multiple time points in their disease; and (ii) the technique is fast, as the deep learning segmentation takes only 0.5 s/scan, which is feasible for both large and small datasets.

Brown Robert A, Fetco Dumitru, Fratila Robert, Fadda Giulia, Jiang Shangge, Alkhawajah Nuha M, Yeh E Ann, Banwell Brenda, Bar-Or Amit, Arnold Douglas L

2019-Dec-07

Radiology Radiology

Deep Learning for Caries Lesion Detection in Near-Infrared Light Transillumination Images: A Pilot Study.

In Journal of dentistry ; h5-index 59.0

OBJECTIVES : In this pilot study, we applied deep convolutional neural networks (CNNs) to detect caries lesions in Near-Infrared-Light Transillumination (NILT) images.

METHODS : 226 extracted posterior permanent human teeth (113 premolars, 113 molars) were allocated to groups of 2 + 2 teeth, and mounted in a pilot-tested diagnostic model in a dummy head. NILT images of single-tooth-segments were generated using DIAGNOcam (KaVo, Biberach). For each segment (on average 435 × 407 × 3 pixels), occlusal and/or proximal caries lesions were annotated by two experienced dentists using an in-house developed digital annotation tool. The pixel-based annotations were translated into binary class levels. We trained two state-of-the-art CNNs (Resnet18, Resnext50) and validated them via 10-fold cross validation. During the training process, we applied data augmentation (random resizing, rotations and flipping) and one-cycle-learning rate policy, setting the minimum and maximum learning rates to 10-5 and 10-3, respectively. Metrics for model performance were the area-under-the-receiver-operating-characteristics-curve (AUC), sensitivity, specificity, and positive/negative predictive values (PPV/NPV). Feature visualization was additionally applied to assess if the CNNs built on features dentists would also use.

RESULTS : The tooth-level prevalence of caries lesions was 41%. The two models performed similar on predicting caries on tooth segments of NILT images. The marginal better model with respect to AUC was Resnext50, where we retrained the last 9 network layers, using the Adam optimizer, a learning rate of 0.5 × 10-4, and a batch size of 10. The mean (95% CI) AUC was 0.74 (0.66-0.82). Sensitivity and specificity were 0.59 (0.47-0.70) and 0.76 (0.68-0.84) respectively. The resulting PPV was 0.63 (0.51-0.74), the NPV 0.73 (0.65-0.80). Visual inspection of model predictions found the model to be sensitive to areas affected by caries lesions.

CONCLUSIONS : A moderately deep CNN trained on a limited amount of NILT image data showed satisfying discriminatory ability to detect caries lesions.

CLINICAL SIGNIFICANCE : CNNs may be useful to assist NILT-based caries detection. This could be especially relevant in non-conventional dental settings, like schools, care homes or rural outpost centers.

Schwendicke Falk, Elhennawy Karim, Paris Sebastian, Friebertshäuser Philipp, Krois Joachim

2019-Dec-07

Artificial Intelligence, Caries, Diagnostics, Digital imaging/radiology, Mathematical modeling

General General

Pred-MutHTP: Prediction of disease-causing and neutral mutations in human transmembrane proteins.

In Human mutation ; h5-index 53.0

Membrane proteins are unique in that segments thereof concurrently reside in vastly different physicochemical environments: the extracellular space, the lipid bilayer, and the cytoplasm. Accordingly, the effects of missense variants disrupting their sequence depend greatly on the characteristics of the environment of the protein segment affected as well as the function it performs. Because membrane proteins have many crucial roles (transport, signal transduction, cell adhesion, etc.), compromising their functionality often leads to diseases including cancers, diabetes mellitus or cystic fibrosis. Here, we report a suite of sequence-based computational methods "Pred-MutHTP" for discriminating between disease-causing and neutral alterations in their sequence. With a data set of 11,846 disease-causing and 9,533 neutral mutations, we obtained an accuracy of 74% and 78% with 10-fold group-wise cross-validation and test set, respectively. The features used in the models include evolutionary information, physiochemical properties, neighboring residue information, and specialized membrane protein attributes incorporating the number of transmembrane segments, substitution matrices specific to membrane proteins as well as residue distributions occurring in specific topological regions. Across 11 disease classes, the method achieved accuracies in the range of 75-85%. The model designed specifically for the transmembrane segments achieved an accuracy of 85% on the test set with a sensitivity and specificity of 86% and 83%, respectively. This renders our method the current state-of-the-art with regard to predicting the effects of variants in the transmembrane protein segments. Pred-MutHTP allows predicting the effect of any variant occurring in a membrane protein-available at https://www.iitm.ac.in/bioinfo/PredMutHTP/.

Kulandaisamy A, Zaucha Jan, Sakthivel Ramasamy, Frishman Dmitrij, Michael Gromiha M

2019-Dec-10

disease-causing, machine learning, missense variant, mutation, neutral, transmembrane proteins

oncology Oncology

Incorporating human and learned domain knowledge into training deep neural networks: A differentiable dose volume histogram and adversarial inspired framework for generating Pareto optimal dose distributions in radiation therapy.

In Medical physics ; h5-index 59.0

PURPOSE : We propose a novel domain specific loss, which is a differentiable loss function based on the dose volume histogram, and combine it with an adversarial loss for the training of deep neural networks. In this study, we trained a neural network for generating Pareto optimal dose distributions, and evaluate the effects of the domain specific loss on the model performance.

METHODS : In this study, 3 loss functions-mean squared error (MSE) loss, dose volume histogram (DVH) loss, and adversarial (ADV) loss-were used to train and compare 4 instances of the neural network model: 1) MSE, 2) MSE+ADV, 3) MSE+DVH, and 4) MSE+DVH+ADV. The data for 70 prostate patients, including the planning target volume (PTV), and the organs-at-risk (OAR) were acquired as 96 x 96 x 24 dimension arrays at 5 mm3 voxel size. The dose influence arrays were calculated for 70 prostate patients, using a 7 equidistant coplanar beam setup. Using a scalarized multicriteria optimization for intensity modulated radiation therapy, 1200 Pareto surface plans per patient were generated by pseudo-randomizing the PTV and OAR tradeoff weights. With 70 patients, the total number of plans generated was 84,000 plans. We divided the data into 54 training, 6 validation, and 10 testing patients. Each model was trained for a total of 100,000 iterations, with a batch size of 2. All models used the Adam optimizer, with a learning rate of 1×10-3 .

RESULTS : Training for 100,000 iterations took 1.5 days (MSE), 3.5 days (MSE+ADV), 2.3 days (MSE+DVH), 3.8 days (MSE+DVH+ADV). After training, the prediction time of each model is 0.052 seconds. Quantitatively, the MSE+DVH+ADV model had the lowest prediction error of 0.038 (conformation), 0.026 (homogeneity), 0.298 (R50), 1.65% (D95), 2.14% (D98), 2.43% (D99). The MSE model had the worst prediction error of 0.134 (conformation), 0.041 (homogeneity), 0.520 (R50), 3.91% (D95), 4.33% (D98), 4.60% (D99). For both the mean dose PTV error and the max dose PTV, Body, Bladder and rectum error, the MSE+DVH+ADV outperformed all other models. Regardless of model, all predictions have an average mean and max dose error less than 2.8% and 4.2%, respectively.

CONCLUSION : The MSE+DVH+ADV model performed the best in these categories, illustrating the importance of both human and learned domain knowledge. Expert human domain specific knowledge can be the largest driver in the performance improvement, and adversarial learning can be used to further capture nuanced attributes in the data. The real-time prediction capabilities allow for a physician to quickly navigate the tradeoff space for a patient, and produce a dose distribution as a tangible endpoint for the dosimetrist to use for planning. This is expected to considerably reduce the treatment planning time, allowing for clinicians to focus their efforts on the difficult and demanding cases.

Nguyen Dan, McBeth Rafe, Sadeghnejad Barkousaraie Azar, Bohara Gyanendra, Shen Chenyang, Jia Xun, Jiang Steve

2019-Dec-10

Internal Medicine Internal Medicine

Automatic Detection of Granuloma Necrosis in Pulmonary Tuberculosis Using a Two-Phase Algorithm: 2D-TB.

In Microorganisms ; h5-index 0.0

Granuloma necrosis occurs in hosts susceptible to pathogenic mycobacteria and is a diagnostic visual feature of pulmonary tuberculosis (TB) in humans and in super-susceptible Diversity Outbred (DO) mice infected with Mycobacterium tuberculosis. Currently, no published automated algorithms can detect granuloma necrosis in pulmonary TB. However, such a method could reduce variability, and transform visual patterns into quantitative data for statistical and machine learning analyses. Here, we used histopathological images from super-susceptible DO mice to train, validate, and performance test an algorithm to detect regions of cell-poor necrosis. The algorithm, named 2D-TB, works on 2-dimensional histopathological images in 2 phases. In phase 1, granulomas are detected following background elimination. In phase 2, 2D-TB searches within granulomas for regions of cell-poor necrosis. We used 8 lung sections from 8 different super-susceptible DO mice for training and 10-fold cross validation. We used 13 new lung sections from 10 different super-susceptible DO mice for performance testing. 2D-TB reached 100.0% sensitivity and 91.8% positive prediction value. Compared to an expert pathologist, agreement was 95.5% and there was a statistically significant positive correlation for area detected by 2D-TB and the pathologist. These results show the development, validation, and accurate performance of 2D-TB to detect granuloma necrosis.

Kus Pelin, Gurcan Metin N, Beamer Gillian

2019-Dec-07

algorithm, granuloma, machine learning, necrosis, tuberculosis

Cardiology Cardiology

New AI Prediction Model Using Serial PT-INR Measurements in AF Patients on VKAs: GARFIELD-AF.

In European heart journal. Cardiovascular pharmacotherapy ; h5-index 0.0

BACKGROUND : Most clinical risk stratification models are based on measurement at a single time-point rather than serial measurements. Artificial intelligence (AI) is able to predict one-dimensional outcomes from multi-dimensional datasets. Using data from GARFIELD-AF registry, a new AI model was developed for predicting clinical outcomes in atrial fibrillation (AF) patients up to 1 year based on sequential measures of PT-INR within 30 days of enrolment.

METHODS AND RESULTS : Patients with newly diagnosed AF who were treated with vitamin K antagonists (VKA) and had at least 3 measurements of PT-INR taken over the first 30 days after prescription were analyzed. The AI model was constructed with multilayer neural network including long short-term memory (LSTM) and one-dimensional convolution layers. The neural network was trained using PT-INR measurements within days 0-30 after starting treatment and clinical outcomes over days 31-365 in a derivation cohort (cohorts 1-3; n = 3185). Accuracy of the AI model at predicting major bleed, stroke/SE, and death was assessed in a validation cohort (cohorts 4-5; n = 1523). The model's c-statistic for predicting major bleed, stroke/SE, and all-cause death was 0.75, 0.70, and 0.61, respectively.

CONCLUSIONS : Using serial PT-INR values collected within 1 month after starting VKA, the new AI model performed better than time in therapeutic range (TTR) at predicting clinical outcomes occurring up to 12 months thereafter. Serial PT-INR values contain important information that can be analyzed by computer to help predict adverse clinical outcomes.

Goto Shinichi, Goto Shinya, Pieper Karen S, Bassand Jean-Pierre, Camm A John, Fitzmaurice David A, Goldhaber Samuel Z, Haas Sylvia, Parkhomenko Alexander, Oto Ali, Misselwitz Frank, Turpie Alexander G G, Verheugt Freek W A, Fox Keith A A, Gersh Bernard J, Kakkar Ajay K

2019-Dec-10

artificial intelligence (AI), atrial fibrillation (AF), machine learning

General General

Formal axioms in biomedical ontologies improve analysis and interpretation of associated data.

In Bioinformatics (Oxford, England) ; h5-index 0.0

MOTIVATION : Over the past years, significant resources have been invested into formalizing biomedical ontologies. Formal axioms in ontologies have been developed and used to detect and ensure ontology consistency, find unsatisfiable classes, improve interoperability, guide ontology extension through the application of axiom-based design patterns, and encode domain background knowledge. The domain knowledge in biomedical ontologies may also have the potential to provide background knowledge for machine learning and predictive modelling.

RESULTS : We use ontology-based machine learning methods to evaluate the contribution of formal axioms and ontology meta-data to the prediction of protein-protein interactions and gene-disease associations. We find that the background knowledge provided by the Gene Ontology and other ontologies significantly improves the performance of ontology-based prediction models through provision of domain-specific background knowledge. Furthermore, we find that the labels, synonyms and definitions in ontologies can also provide background knowledge that may be exploited for prediction. The axioms and meta-data of different ontologies contribute to improving data analysis in a context-specific manner. Our results have implications on the further development of formal knowledge bases and ontologies in the life sciences, in particular as machine learning methods are more frequently being applied. Our findings motivate the need for further development, and the systematic, application-driven evaluation and improvement, of formal axioms in ontologies.

AVAILABILITY : https://github.com/bio-ontology-research-group/tsoe.

Smaili Fatima Zohra, Gao Xin, Hoehndorf Robert

2019-Dec-10

General General

[Development and national rollout of electronic decision support systems using artificial intelligence in the field of onco-hematology].

In Magyar onkologia ; h5-index 0.0

Systematic, structured and longitudinal collection of realtime Big Patient Data and the analysis of aggregated diagnostic, therapeutic and therapy response data of onco-hematologic patients leads to the development of nationwide dynamic disease registries providing a platform for medical, health industrial and data science research, hospital and health insurance cost analysis, measurement of innovative diagnostics and therapeutics performance, evaluation of compassion-based treatments and general support for insurance and health policy decisions. First in Hungary, we developed a complex computerized case management, data collection, processing, and analysis program (OncoGenomic) and a self-learning artificial intelligence (AI) precision medicine decision support application (Oncompass Calculator) that organize basic research (R), applied research and development (R and D) and innovation (I) under a common umbrella. These progams support the national dynamic hematologic disease registry. Exchange of data through the Electronic Health Service Space (EESZT) supports equal opportunity access of patients to innovative diagnostics and therapy.

Vályi-Nagy István, Peták István

2019-Dec-09

Public Health Public Health

Modelling tick bite risk by combining random forests and count data regression models.

In PloS one ; h5-index 176.0

The socio-economic and demographic changes that occurred over the past 50 years have dramatically expanded urban areas around the globe, thus bringing urban settlers in closer contact with nature. Ticks have trespassed the limits of forests and grasslands to start inhabiting green spaces within metropolitan areas. Hence, the transmission of pathogens causing tick-borne diseases is an important threat to public health. Using volunteered tick bite reports collected by two Dutch initiatives, here we present a method to model tick bite risk using human exposure and tick hazard predictors. Our method represents a step forward in risk modelling, since we combine a well-known ensemble learning method, Random Forest, with four count data models of the (zero-inflated) Poisson family. This combination allows us to better model the disproportions inherent in the volunteered tick bite reports. Unlike canonical machine learning models, our method can capture the overdispersion or zero-inflation inherent in data, thus yielding tick bite risk predictions that resemble the original signal captured by volunteers. Mapping model predictions enables a visual inspection of the spatial patterns of tick bite risk in the Netherlands. The Veluwe national park and the Utrechtse Heuvelrug forest, which are large forest-urban interfaces with several cities, are areas with high tick bite risk. This is expected, since these are popular places for recreation and tick activity is high in forests. However, our model can also predict high risk in less-intensively visited recreational areas, such as the patchy forests in the northeast of the country, the natural areas along the coastline, or some of the Frisian Islands. Our model could help public health specialists to design mitigation strategies for tick-borne diseases, and to target risky areas with awareness and prevention campaigns.

Garcia-Marti Irene, Zurita-Milla Raul, Swart Arno

2019

General General

How New Technologies Can Improve Prediction, Assessment, and Intervention in Obsessive-Compulsive Disorder (e-OCD): Review.

In JMIR mental health ; h5-index 0.0

BACKGROUND : New technologies are set to profoundly change the way we understand and manage psychiatric disorders, including obsessive-compulsive disorder (OCD). Developments in imaging and biomarkers, along with medical informatics, may well allow for better assessments and interventions in the future. Recent advances in the concept of digital phenotype, which involves using computerized measurement tools to capture the characteristics of a given psychiatric disorder, is one paradigmatic example.

OBJECTIVE : The impact of new technologies on health professionals' practice in OCD care remains to be determined. Recent developments could disrupt not just their clinical practices, but also their beliefs, ethics, and representations, even going so far as to question their professional culture. This study aimed to conduct an extensive review of new technologies in OCD.

METHODS : We conducted the review by looking for titles in the PubMed database up to December 2017 that contained the following terms: [Obsessive] AND [Smartphone] OR [phone] OR [Internet] OR [Device] OR [Wearable] OR [Mobile] OR [Machine learning] OR [Artificial] OR [Biofeedback] OR [Neurofeedback] OR [Momentary] OR [Computerized] OR [Heart rate variability] OR [actigraphy] OR [actimetry] OR [digital] OR [virtual reality] OR [Tele] OR [video].

RESULTS : We analyzed 364 articles, of which 62 were included. Our review was divided into 3 parts: prediction, assessment (including diagnosis, screening, and monitoring), and intervention.

CONCLUSIONS : The review showed that the place of connected objects, machine learning, and remote monitoring has yet to be defined in OCD. Smartphone assessment apps and the Web Screening Questionnaire demonstrated good sensitivity and adequate specificity for detecting OCD symptoms when compared with a full-length structured clinical interview. The ecological momentary assessment procedure may also represent a worthy addition to the current suite of assessment tools. In the field of intervention, CBT supported by smartphone, internet, or computer may not be more effective than that delivered by a qualified practitioner, but it is easy to use, well accepted by patients, reproducible, and cost-effective. Finally, new technologies are enabling the development of new therapies, including biofeedback and virtual reality, which focus on the learning of coping skills. For them to be used, these tools must be properly explained and tailored to individual physician and patient profiles.

Ferreri Florian, Bourla Alexis, Peretti Charles-Siegfried, Segawa Tomoyuki, Jaafari Nemat, Mouchabac Stéphane

2019-Dec-10

biofeedback, digital biomarkers, digital phenotyping, ecological momentary assessment, machine learning, mobile health, obsessive-compulsive disorder, virtual reality

General General

Artificial intelligence in diagnostic imaging: Impact on the radiography profession.

In The British journal of radiology ; h5-index 0.0

The arrival of artificially intelligent systems into the domain of medical imaging has focused attention and sparked much debate on the role and responsibilities of the radiologist. However, discussion about the impact of such technology on the radiographer role is lacking. This paper discusses the potential impact of artificial intelligence (AI) on the radiography profession by assessing current workflow and cross-mapping potential areas of AI automation such as procedure planning, image acquisition and processing. We also highlight the opportunities that AI brings including enhancing patient-facing care, increased cross-modality education and working, increased technological expertise and expansion of radiographer responsibility into AI-supported image reporting and auditing roles.

Hardy Maryann, Harvey Hugh

2019-Dec-10

General General

Machine Learning Enabled Tailor-Made Design of Application-Specific Metal-Organic Frameworks.

In ACS applied materials & interfaces ; h5-index 147.0

In the development of advanced nanoporous materials, one clear and unavoidable challenge in hand is the sheer size (in principle, infinite) of the materials space to be explored. While high-throughput screening techniques allow us to narrow down the enormous-scale database of nanoporous materials, there are still practical limitations stemming from a costly molecular simulation in estimating material's performance and necessity of a sophisticated descriptor identifying materials. With an attempt to transitioning away from the screening-based approaches, this paper presents a computational approach combining the Monte Carlo tree search and recurrent neural networks for the tailor-made design of metal-organic frameworks toward the desired target applications. In the demonstration cases for methane storage and carbon capture applications, our approach showed significant efficiency in designing promising and novel metal-organic frameworks. We expect that this approach would easily be extended to other applications by simply adjusting the reward function according to target performance property.

Zhang Xiangyu, Zhang Kexin, Lee Yongjin

2019-Dec-10

General General

Medication-rights detection using incident reports: A natural language processing and deep neural network approach.

In Health informatics journal ; h5-index 25.0

Medication errors often occurred due to the breach of medication rights that are the right patient, the right drug, the right time, the right dose and the right route. The aim of this study was to develop a medication-rights detection system using natural language processing and deep neural networks to automate medication-incident identification using free-text incident reports. We assessed the performance of deep neural network models in classifying the Advanced Incident Reporting System reports and compared the models' performance with that of other common classification methods (including logistic regression, support vector machines and the decision-tree method). We also evaluated the effects on prediction outcomes of several deep neural network model settings, including number of layers, number of neurons and activation regularisation functions. The accuracy of the models was measured at 0.9 or above across model settings and algorithms. The average values obtained for accuracy and area under the curve were 0.940 (standard deviation: 0.011) and 0.911 (standard deviation: 0.019), respectively. It is shown that deep neural network models were more accurate than the other classifiers across all of the tested class labels (including wrong patient, wrong drug, wrong time, wrong dose and wrong route). The deep neural network method outperformed other binary classifiers and our default base case model, and parameter arguments setting generally performed well for the five medication-rights datasets. The medication-rights detection system developed in this study successfully uses a natural language processing and deep-learning approach to classify patient-safety incidents using the Advanced Incident Reporting System reports, which may be transferable to other mandatory and voluntary incident reporting systems worldwide.

Wong Zoie Shui-Yee, So H Y, Kwok Belinda Sc, Lai Mavis Ws, Sun David Tf

2019-Dec-10

artificial intelligence, hospital incident reporting, medication errors, patient safety, text mining

General General

A self-organizing map of the fathead-minnow liver transcriptome to identify consistent toxicogenomic patterns across chemical fingerprints.

In Environmental toxicology and chemistry ; h5-index 0.0

Lack of consistent findings in different experimental settings remains to be a major challenge in toxicogenomics. The present study investigated, whether consistency between findings of different microarray experiments can be improved when the analysis is based on a common reference frame ("toxicogenomic universe"), which can be generated using the machine learning-algorithm of the self-organizing map (SOM). This algorithm arranges and clusters genes on a two-dimensional grid according to their similarity in expression across all considered data. In the present study, nineteen data sets, comprising of 54 different adult fathead minnow liver exposure experiments, were retrieved from Gene Expression Omnibus and used to train a SOM. The resulting toxicogenomic universe aggregates 58,872 probes to 2,500 nodes and was used to project, visualize and compare the fingerprints of these 54 different experiments. For example, we could identify a common pattern, with 14% of significantly regulated nodes in common, in the data sets of an interlaboratory study of ethinylestradiol exposures, previously published by Feswick et al. (2017). Consistency could be improved compared to the 5% total overlap in regulated genes reported before. Furthermore, we could determine a specific and consistent estrogen-related pattern of differentially expressed nodes and clusters in the toxicogenomic universe applying additional clustering steps and comparing all obtained fingerprints. This study shows that the SOM-based approach is useful for generating comparable toxicogenomic fingerprints and improving consistency between results of different experiments. This article is protected by copyright. All rights reserved.

Krämer Stefan, Busch Wibke, Schüttler Andreas

2019-Dec-09

consistency, estrogens, gene expression, self-organizing map, toxicogenomics

Radiology Radiology

Diagnostic performance and inter-operator variability of apparent diffusion coefficient analysis for differentiating pleomorphic adenoma and carcinoma ex pleomorphic adenoma: comparing one-point measurement and whole-tumor measurement including radiomics approach.

In Japanese journal of radiology ; h5-index 0.0

BACKGROUND AND PURPOSE : The purpose of this study was to compare the diagnostic performance between apparent diffusion coefficient (ADC) analysis of one-point measurement and whole-tumor measurement, including radiomics for differentiating pleomorphic adenoma (PA) from carcinoma ex pleomorphic adenoma (CXPA), and to evaluate the impact of inter-operator segmentation variability.

MATERIALS AND METHODS : One hundred and fifteen patients with PA and 22 with CXPA were included. Four radiologists with different experience independently placed one-point and whole-tumor ROIs and a radiomics-predictive model was constructed from the extracted imaging features. We calculated the area under the receiver-operator characteristic curve (AUC) for the diagnostic performance of imaging features and the radiomics-predictive model.

RESULTS : AUCs of the imaging features from whole-tumor varied between readers (0.50-0.89). The most experienced radiologist (Reader 1) produced significantly high AUCs than less experienced radiologists (Reader 3 and 4; P = 0.01 and 0.009). AUCs were higher for the radiomics-predictive model (0.82-0.87) than for one-point (0.66-0.79) in all readers.

CONCLUSION : Some imaging features of whole-tumor and radiomics-predictive model had higher diagnostic performance than one-point. The diagnostic performance of imaging features from whole-tumor alone varied depending on operator experience. Operator experience appears less likely to affect diagnostic performance in the radiomics-predictive model.

Wada Takeshi, Yokota Hajime, Horikoshi Takuro, Starkey Jay, Hattori Shinya, Hashiba Jun, Uno Takashi

2019-Dec-09

Carcinoma ex pleomorphic adenoma, Diagnostic performance, Machine learning, Pleomorphic adenoma, Radiomics

Public Health Public Health

Predicting Long-Term Health-Related Quality of Life after Bariatric Surgery Using a Conventional Neural Network: A Study Based on the Scandinavian Obesity Surgery Registry.

In Journal of clinical medicine ; h5-index 0.0

Severe obesity has been associated with numerous comorbidities and reduced health-related quality of life (HRQoL). Although many studies have reported changes in HRQoL after bariatric surgery, few were long-term prospective studies. We examined the performance of the convolution neural network (CNN) for predicting 5-year HRQoL after bariatric surgery based on the available preoperative information from the Scandinavian Obesity Surgery Registry (SOReg). CNN was used to predict the 5-year HRQoL after bariatric surgery in a training dataset and evaluated in a test dataset. In general, performance of the CNN model (measured as mean squared error, MSE) increased with more convolution layer filters, computation units, and epochs, and decreased with a larger batch size. The CNN model showed an overwhelming advantage in predicting all the HRQoL measures. The MSEs of the CNN model for training data were 8% to 80% smaller than those of the linear regression model. When the models were evaluated using the test data, the CNN model performed better than the linear regression model. However, the issue of overfitting was apparent in the CNN model. We concluded that the performance of the CNN is better than the traditional multivariate linear regression model in predicting long-term HRQoL after bariatric surgery; however, the overfitting issue needs to be mitigated using more features or more patients to train the model.

Cao Yang, Raoof Mustafa, Montgomery Scott, Ottosson Johan, Näslund Ingmar

2019-Dec-05

bariatric surgery, conventional neural network, deep learning, health-related quality of life, prediction

Internal Medicine Internal Medicine

Pulse Wave Velocity and Machine Learning to Predict Cardiovascular Outcomes in Prediabetic and Diabetic Populations.

In Journal of medical systems ; h5-index 48.0

Few studies have addressed the predictive value of arterial stiffness determined by pulse wave velocity (PWV) in a high-risk population with no prevalent cardiovascular disease and with obesity, hypertension, hyperglycemia, and preserved kidney function. This longitudinal, retrospective study enrolled 88 high-risk patients and had a follow-up time of 12.4 years. We collected clinical and laboratory data, as well as information on arterial stiffness parameters using arterial tonometry and measurements from ambulatory blood pressure monitoring. We considered nonfatal, incident cardiovascular events as the primary outcome. Given the small size of our dataset, we used survival analysis (i.e., Cox proportional hazards model) combined with a machine learning-based algorithm/penalization method to evaluate the data. Our predictive model, calculated with Cox regression and least absolute shrinkage and selection operator (LASSO), included body mass index, diabetes mellitus, gender (male), and PWV. We recorded 16 nonfatal cardiovascular events (5 myocardial infarctions, 5 episodes of heart failure, and 6 strokes). The adjusted hazard ratio for PWV was 1.199 (95% confidence interval: 1.09-1.37, p < 0.001). Arterial stiffness was a predictor of cardiovascular disease development, as determined by PWV in a high-risk population. Thus, in obese, hypertensive, hyperglycemic patients with preserved kidney function, PWV can serve as a prognostic factor for major adverse cardiac events.

Garcia-Carretero Rafael, Vigil-Medina Luis, Barquero-Perez Oscar, Ramos-Lopez Javier

2019-Dec-09

Cardiovascular risk, LASSO regression, Machine learning, Pulse wave velocity

Radiology Radiology

Diffusion tensor imaging radiomics in lower-grade glioma: improving subtyping of isocitrate dehydrogenase mutation status.

In Neuroradiology ; h5-index 0.0

PURPOSE : To evaluate whether diffusion tensor imaging (DTI) radiomics with machine learning improves the prediction of isocitrate dehydrogenase (IDH) mutation status of lower-grade gliomas beyond radiomic features from conventional MRI and DTI histogram parameters.

METHODS : A total of 168 patients with pathologically confirmed lower-grade gliomas were retrospectively enrolled. A total of 158 and 253 radiomic features were extracted from DTI (DTI radiomics) and conventional MRI (T1-weighted image with contrast enhancement, T2-weighted image, and FLAIR [conventional radiomics]), respectively. The random forest models for predicting IDH status were trained with variable combinations as follows: (1) DTI radiomics, (2) conventional radiomics, (3) conventional radiomics + DTI radiomics, and (4) conventional radiomics + DTI histogram. The models were validated with nested cross-validation. The predictive performances of those models were compared by using area under the curve (AUC) from receiver operating characteristic analysis, and 95% confidence interval (CI) was calculated.

RESULTS : Adding DTI radiomics to conventional radiomics significantly improved the accuracy of IDH status subtyping (AUC, 0.900 [95% CI, 0.855-0.945], p = 0.006), whereas adding DTI histogram parameters yielded nonsignificant trend toward improvement (0.869 [95% CI, 0.816-0.922], p = 0.150) compared with the model with conventional radiomics alone (0.835 [95% CI, 0.773-0.896]). The performance of the model consisting of both DTI and conventional radiomics was significantly superior than that of model consisting of both DTI histogram parameters and conventional radiomics (0.900 vs 0.869, p = 0.040).

CONCLUSION : DTI radiomics with machine learning can help improve the subtyping of IDH status beyond conventional radiomics and DTI histogram parameters in patients with lower-grade gliomas.

Park Chae Jung, Choi Yoon Seong, Park Yae Won, Ahn Sung Soo, Kang Seok-Gu, Chang Jong-Hee, Kim Se Hoon, Lee Seung-Koo

2019-Dec-09

Diffusion tensor imaging, Isocitrate dehydrogenase, Lower-grade glioma, Machine learning, Radiomics

Radiology Radiology

[A primer on radiomics].

In Der Radiologe ; h5-index 0.0

CLINICAL ISSUE : The reproducible and exhaustive extraction of information from radiological images is a central task in the practice of radiology. Dynamic developments in the fields of artificial intelligence (AI) and machine learning are introducing new methods for this task. Radiomics is one such method and offers new opportunities and challenges for the future of radiology.

METHODOLOGICAL INNOVATIONS : Radiomics describes the quantitative evaluation, interpretation, and clinical assessment of imaging markers in radiological data. Components of a radiomics analysis are data acquisition, data preprocessing, data management, segmentation of regions of interest, computation and selection of imaging markers, as well as the development of a radiomics model used for diagnosis and prognosis. This article explains these components and aims at providing an introduction to the field of radiomics while highlighting existing limitations.

MATERIALS AND METHODS : This article is based on a selective literature search with the PubMed search engine.

ASSESSMENT : Even though radiomics applications have yet to arrive in routine clinical practice, the quantification of radiological data in terms of radiomics is underway and will increase in the future. This holds the potential for lasting change in the discipline of radiology. Through the successful extraction and interpretation of all the information encoded in radiological images the next step in the direction of a more personalized, future-oriented form of medicine can be taken.

Murray Jacob M, Kaissis Georgios, Braren Rickmer, Kleesiek Jens

2019-Dec-09

Artificial intelligence, Artificial neural networks, Machine learning, Personalized medicine, Radiogenomics

Ophthalmology Ophthalmology

Deep Learning Algorithm for Automated Diagnosis of Retinopathy of Prematurity Plus Disease.

In Translational vision science & technology ; h5-index 0.0

Purpose : This study describes the initial development of a deep learning algorithm, ROP.AI, to automatically diagnose retinopathy of prematurity (ROP) plus disease in fundal images.

Methods : ROP.AI was trained using 6974 fundal images from Australasian image databases. Each image was given a diagnosis as part of real-world routine ROP screening and classified as normal or plus disease. The algorithm was trained using 80% of the images and validated against the remaining 20% within a hold-out test set. Performance in diagnosing plus disease was evaluated against an external set of 90 images. Performance in detecting pre-plus disease was also tested. As a screening tool, the algorithm's operating point was optimized for sensitivity and negative predictive value, and its performance reevaluated.

Results : For plus disease diagnosis within the 20% hold-out test set, the algorithm achieved a 96.6% sensitivity, 98.0% specificity, and 97.3% ± 0.7% accuracy. Area under the receiver operating characteristic curve was 0.993. Within the independent test set, the algorithm achieved a 93.9% sensitivity, 80.7% specificity, and 95.8% negative predictive value. For detection of pre-plus and plus disease, the algorithm achieved 81.4% sensitivity, 80.7% specificity, and 80.7% negative predictive value. Following the identification of an optimized operating point, the algorithm diagnosed plus disease with a 97.0% sensitivity and 97.8% negative predictive value.

Conclusions : ROP.AI is a deep learning algorithm able to automatically diagnose ROP plus disease with high sensitivity and negative predictive value.

Translational Relevance : In the context of increasing global disease burden, future development may improve access to ROP diagnosis and care.

Tan Zachary, Simkin Samantha, Lai Connie, Dai Shuan

2019-Nov

artificial intelligence, deep learning, retinopathy of prematurity

General General

Identification of Targetable Pathways in Oral Cancer Patients via Random Forest and Chemical Informatics.

In Cancer informatics ; h5-index 0.0

Treatment of head and neck cancer has been slow to change with epidermal growth factor receptor (EGFR) inhibitors, PD1 inhibitors, and taxane-/plant-alkaloid-derived chemotherapies being the only therapies approved by the U.S. Food and Drug Administration (FDA) in the last 10 years for the treatment of head and neck cancers. Head and neck cancer is a relatively rare cancer compared to breast or lung cancers. However, it is possible that existing therapies for more common solid tumors or for the treatment of other diseases could also prove effective against oral cancers. Many therapies have molecular targets that could be appropriate in oral cancer as well as the cancer in which the drug gained initial FDA approval. Also, there may be targets in oral cancer for which existing FDA-approved drugs could be applied. This study describes informatics methods that use machine learning to identify influential gene targets in patients receiving platinum-based chemotherapy, non-platinum-based chemotherapy, and genes influential in both groups of patients. This analysis yielded 6 small molecules that had a high Tanimoto similarity (>50%) to ligands binding genes shown to be highly influential in determining treatment response in oral cancer patients. In addition to influencing treatment response, these genes were also found to act as gene hubs connected to more than 100 other genes in pathways enriched with genes determined to be influential in treatment response by a random forest classifier with 20 000 trees trying 320 variables at each tree node. This analysis validates the use of multiple informatics methods to identify small molecules that have a greater likelihood of efficacy in a given cancer of interest.

Schomberg John

2019

Chemical informatics, machine learning, oral cancer, pathway analysis, random forest, traditional chinese medicine, virtual screening

General General

Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning.

In Nature methods ; h5-index 152.0

Predicting interactions between proteins and other biomolecules solely based on structure remains a challenge in biology. A high-level representation of protein structure, the molecular surface, displays patterns of chemical and geometric features that fingerprint a protein's modes of interactions with other biomolecules. We hypothesize that proteins participating in similar interactions may share common fingerprints, independent of their evolutionary history. Fingerprints may be difficult to grasp by visual analysis but could be learned from large-scale datasets. We present MaSIF (molecular surface interaction fingerprinting), a conceptual framework based on a geometric deep learning method to capture fingerprints that are important for specific biomolecular interactions. We showcase MaSIF with three prediction challenges: protein pocket-ligand prediction, protein-protein interaction site prediction and ultrafast scanning of protein surfaces for prediction of protein-protein complexes. We anticipate that our conceptual framework will lead to improvements in our understanding of protein function and design.

Gainza P, Sverrisson F, Monti F, Rodolà E, Boscaini D, Bronstein M M, Correia B E

2019-Dec-09

General General

The functional landscape of the human phosphoproteome.

In Nature biotechnology ; h5-index 151.0

Protein phosphorylation is a key post-translational modification regulating protein function in almost all cellular processes. Although tens of thousands of phosphorylation sites have been identified in human cells, approaches to determine the functional importance of each phosphosite are lacking. Here, we manually curated 112 datasets of phospho-enriched proteins, generated from 104 different human cell types or tissues. We re-analyzed the 6,801 proteomics experiments that passed our quality control criteria, creating a reference phosphoproteome containing 119,809 human phosphosites. To prioritize functional sites, we used machine learning to identify 59 features indicative of proteomic, structural, regulatory or evolutionary relevance and integrate them into a single functional score. Our approach identifies regulatory phosphosites across different molecular mechanisms, processes and diseases, and reveals genetic susceptibilities at a genomic scale. Several regulatory phosphosites were experimentally validated, including identifying a role in neuronal differentiation for phosphosites in SMARCC2, a member of the SWI/SNF chromatin-remodeling complex.

Ochoa David, Jarnuczak Andrew F, Viéitez Cristina, Gehre Maja, Soucheray Margaret, Mateus André, Kleefeldt Askar A, Hill Anthony, Garcia-Alonso Luz, Stein Frank, Krogan Nevan J, Savitski Mikhail M, Swaney Danielle L, Vizcaíno Juan A, Noh Kyung-Min, Beltrao Pedro

2019-Dec-09

General General

Modeling somatic computation with non-neural bioelectric networks.

In Scientific reports ; h5-index 158.0

The field of basal cognition seeks to understand how adaptive, context-specific behavior occurs in non-neural biological systems. Embryogenesis and regeneration require plasticity in many tissue types to achieve structural and functional goals in diverse circumstances. Thus, advances in both evolutionary cell biology and regenerative medicine require an understanding of how non-neural tissues could process information. Neurons evolved from ancient cell types that used bioelectric signaling to perform computation. However, it has not been shown whether or how non-neural bioelectric cell networks can support computation. We generalize connectionist methods to non-neural tissue architectures, showing that a minimal non-neural Bio-Electric Network (BEN) model that utilizes the general principles of bioelectricity (electrodiffusion and gating) can compute. We characterize BEN behaviors ranging from elementary logic gates to pattern detectors, using both fixed and transient inputs to recapitulate various biological scenarios. We characterize the mechanisms of such networks using dynamical-systems and information-theory tools, demonstrating that logic can manifest in bidirectional, continuous, and relatively slow bioelectrical systems, complementing conventional neural-centric architectures. Our results reveal a variety of non-neural decision-making processes as manifestations of general cellular biophysical mechanisms and suggest novel bioengineering approaches to construct functional tissues for regenerative medicine and synthetic biology as well as new machine learning architectures.

Manicka Santosh, Levin Michael

2019-Dec-09

General General

The rise and fall of MRI studies in major depressive disorder.

In Translational psychiatry ; h5-index 60.0

Structural and functional brain alterations are common in patients with major depressive disorder (MDD). In this review, we assessed the recent literature (1995-2018) on the structural and functional magnetic resonance imaging (MRI) studies of MDD. Despite the growing number of MRI studies on MDD, reverse inference is not possible as MRI scans cannot be used to aid in the diagnosis or treatment planning of patients with MDD. Hence, researchers must develop "bridges" to overcome the reverse inference fallacy in order to build effective tools for MDD diagnostics. From our findings, we proposed that the "bridges" may be built using multidisciplinary technologies, such as artificial intelligence, multimodality imaging, and nanotheranostics, allowing for the further study of MDD at the biological level. In return, the "bridges" will aid in the development of future diagnostics for MDD and other mental disorders.

Zhuo Chuanjun, Li Gongying, Lin Xiaodong, Jiang Deguo, Xu Yong, Tian Hongjun, Wang Wenqiang, Song Xueqin

2019-Dec-09

Radiology Radiology

Region-specific agreement in ASPECTS estimation between neuroradiologists and e-ASPECTS software.

In Journal of neurointerventional surgery ; h5-index 49.0

BACKGROUND AND PURPOSE : The Alberta Stroke Program Early CT Score (ASPECTS) is a widely used measure of ischemic change on non-contrast CT. Although predictive of long-term outcome, ASPECTS is limited by its modest interobserver agreement. One potential solution to this is the use of machine learning strategies, such as e-ASPECTS, to detect ischemia. Here, we compared e-ASPECTS with manual scoring by experienced neuroradiologists for all 10 individual ASPECTS regions.

MATERIALS AND METHODS : We retrospectively reviewed 178 baseline non-contrast CT scans from patients with acute ischemic stroke undergoing endovascular thrombectomy. All scans were reviewed by two independent neuroradiologists with a third reader arbitrating disagreements for a consensus read. Each ASPECTS region was scored individually. All scans were then evaluated using a machine learning-based software package (e-ASPECTS, Brainomix). Interobserver agreement between readers and the software for each region was calculated with a kappa statistic.

RESULTS : The median ASPECTS was 9 for manual scoring and 8.5 for e-ASPECTS, with an overall agreement of κ=0.248. Regional agreement varied from κ=0.094 (M1) to κ=0.555 (lentiform), with better performance in subcortical regions. When corrected for the low number of infarcts in any given region, prevalence-adjusted bias-adjusted kappa ranged from 0.483 (insula) to 0.888 (M3), with greater agreement for cortical areas. Intraclass correlation coefficients were between 0.09 (M1) and 0.556 (lentiform).

CONCLUSION : Manual scoring and e-ASPECTS had fair agreement in our dataset on a per-region basis. This warrants further investigation using follow-up scans or MRI as the gold standard measure of true ASPECTS.

Neuhaus Ain, Seyedsaadat Seyed Mohammad, Mihal David, Benson John, Mark Ian, Kallmes David F, Brinjikji Waleed

2019-Dec-09

CT, stroke, thrombectomy

General General

Construction of a Robust Cofactor Self-Sufficient Bienzyme Biocatalytic System for Dye Decolorization and its Mathematical Modeling.

In International journal of molecular sciences ; h5-index 102.0

A triphenylmethane reductase derived from Citrobacter sp. KCTC 18061P was coupled with a glucose 1-dehydrogenase from Bacillus sp. ZJ to construct a cofactor self-sufficient bienzyme biocatalytic system for dye decolorization. Fed-batch experiments showed that the system is robust to maintain its activity after 15 cycles without the addition of any expensive exogenous NADH. Subsequently, three different machine learning approaches, including multiple linear regression (MLR), random forest (RF), and artificial neural network (ANN), were employed to explore the response of decolorization efficiency to the variables of the bienzyme system. Statistical parameters of these models suggested that a three-layered ANN model with six hidden neurons was capable of predicting the dye decolorization efficiency with the best accuracy, compared with the models constructed by MLR and RF. Weights analysis of the ANN model showed that the ratio between two enzymes appeared to be the most influential factor, with a relative importance of 54.99% during the decolorization process. The modeling results confirmed that the neural networks could effectively reproduce experimental data and predict the behavior of the decolorization process, especially for complex systems containing multienzymes.

Ding Haitao, Luo Wei, Yu Yong, Chen Bo

2019-Dec-03

artificial neural network, cofactor regeneration, dye decolorization, glucose 1-dehydrogenase, modeling, multiple linear regression, random forest, triphenylmethane reductase

Ophthalmology Ophthalmology

Use of smartphones for detecting diabetic retinopathy: a protocol for a scoping review of diagnostic test accuracy studies.

In BMJ open ; h5-index 0.0

INTRODUCTION : Diabetic retinopathy (DR) is a common microvascular complication of diabetes mellitus and the leading cause of impaired vision in adults worldwide. Early detection and treatment for DR could improve patient outcomes. Traditional methods of detecting DR include the gold standard Early Treatment Diabetic Retinopathy Study seven standard fields fundus photography, ophthalmoscopy and slit-lamp biomicroscopy. These modalities can be expensive, difficult to access and require involvement of specialised healthcare professionals. With the development of mobile phone technology, there is a growing interest in their use for DR identification as this approach is potentially more affordable, accessible and easier to use. Smartphones can be employed in a variety of ways for ophthalmoscopy including the use of smartphone camera, various attachments and artificial intelligence for obtaining and grading of retinal images. The aim of this scoping review is to determine the diagnostic test accuracy of various smartphone ophthalmoscopy approaches for detecting DR in diabetic patients.

METHODS AND ANALYSIS : We will perform an electronic search of MEDLINE, Embase and Cochrane Library for literature published from 2000 onwards. Two reviewers will independently analyse studies for eligibility and assess study quality using the QUADAS-2 tool. Data for a 2⨉2 contingency table will be extracted. If possible, we will pool sensitivity and specificity data using the random-effects model and construct a summary receiver operating characteristic curve. In case of high heterogeneity, we will present the findings narratively. Subgroup analysis and sensitivity analysis will be performed where appropriate.

ETHICS AND DISSEMINATION : This scoping review aims to provide an overview of smartphone ophthalmoscopy in DR identification. It will present findings on the accuracy of smartphone ophthalmoscopy in detecting DR, identify gaps in the literature and provide recommendations for future research. This review does not require ethical approval as we will not collect primary data.

Tan Choon Han, Quah Willie-Henri, Tan Colin S H, Smith Helen, Tudor Car Lorainne

2019-Dec-08

diabetic retinopathy, ophthalmology, telemedicine

Radiology Radiology

Deep Learning Signature Based on Staging CT for Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer.

In Academic radiology ; h5-index 0.0

RATIONALE AND OBJECTIVES : To evaluate the noninvasive predictive performance of deep learning features based on staging CT for sentinel lymph node (SLN) metastasis of breast cancer.

MATERIALS AND METHODS : A total of 348 breast cancer patients were enrolled in this study, with their SLN metastases pathologically confirmed. All patients received contrast-enhanced CT preoperative examinations and CT images were segmented and analyzed to extract deep features. After the feature selection, deep learning signature was built with the selected key features. The performance of the deep learning signatures was assessed with respect to discrimination, calibration, and clinical usefulness in the primary cohort (184 patients from January 2016 to March 2017) and then validated in the independent validation cohort (164 patients from April 2017 to December 2018).

RESULTS : Ten deep learning features were automatically selected in the primary cohort to establish the deep learning signature of SLN metastasis. The deep learning signature shows favorable discriminative ability with an area under curve of 0.801 (95% confidence interval: 0.736-0.867) in primary cohort and 0.817 (95% confidence interval: 0.751-0.884) in validation cohort. To further distinguish the number of metastatic SLNs (1-2 or more than two metastatic SLN), another deep learning signature was constructed and also showed moderate performance (area under curve 0.770).

CONCLUSION : We developed the deep learning signatures for preoperative prediction of SLN metastasis status and numbers (1-2 or more than two metastatic SLN) in patients with breast cancer. The deep learning signature may potentially provide a noninvasive approach to assist clinicians in predicting SLN metastasis in patients with breast cancer.

Yang Xiaojun, Wu Lei, Ye Weitao, Zhao Ke, Wang Yingyi, Liu Weixiao, Li Jiao, Li Hanxiao, Liu Zaiyi, Liang Changhong

2019-Dec-06

Breast cancer, Computed tomography, Deep learning, Sentinel lymph node metastasis

General General

Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method.

In The Science of the total environment ; h5-index 0.0

Flash-floods are increasingly recognized as a frequent natural hazard worldwide. Iran has been among the mostdevastated regions affected by the major floods. While the temporal flash-flood forecasting models are mainly developed for warning systems, the models for assessing hazardous areas can greatly contribute to adaptation and mitigation policy-making and disaster risk reduction. Former researches in the flash-flood hazard mapping have heightened the urge for the advancement of more accurate models. Thus, the current research proposes the state-of-the-art ensemble models of boosted generalized linear model (GLMBoost) and random forest (RF), and Bayesian generalized linear model (BayesGLM) methods for higher performance modeling. Furthermore, a pre-processing method, namely simulated annealing (SA), is used to eliminate redundant variables from the modeling process. Results of the modeling based on the hit and miss analysis indicates high performance for both models (accuracy = 90-92%, Kappa = 79-84%, Success ratio = 94-96%, Threat score = 80-84%, and Heidke skill score = 79-84%). The variables of distance from the stream, vegetation, drainage density, land use, and elevation have shown more contribution among others for modeling the flash-flood. The results of this study can significantly facilitate mapping the hazardous areas and further assist watershed managers to control and remediate induced damages of flood in the data-scarce regions.

Hosseini Farzaneh Sajedi, Choubin Bahram, Mosavi Amir, Nabipour Narjes, Shamshirband Shahaboddin, Darabi Hamid, Haghighi Ali Torabi

2019-Nov-21

Bayesian, Ensemble machine learning, Flash-flood, Hazard, Simulated annealing

Radiology Radiology

Augmented Radiologist Workflow Improves Report Value and Saves Time: A Potential Model for Implementation of Artificial Intelligence.

In Academic radiology ; h5-index 0.0

RATIONALE AND OBJECTIVES : Our primary aim was to improve radiology reports by increasing concordance of target lesion measurements with oncology records using radiology preprocessors (RP). Faster notification of incidental actionable findings to referring clinicians and clinical radiologist exam interpretation time savings with RPs quantifying tumor burden were also assessed.

MATERIALS AND METHODS : In this prospective quality improvement initiative, RPs annotated lesions before radiologist interpretation of CT exams. Clinical radiologists then hyperlinked approved measurements into interactive reports during interpretations. RPs evaluated concordance with our tumor measurement radiologist, the determinant of tumor burden. Actionable finding detection and notification times were also deduced. Clinical radiologist interpretation times were calculated from established average CT chest, abdomen, and pelvis interpretation times.

RESULTS : RPs assessed 1287 body CT exams with 812 follow-up CT chest, abdomen, and pelvis studies; 95 (11.7%) of which had 241 verified target lesions. There was improved concordance (67.8% vs. 22.5%) of target lesion measurements. RPs detected 93.1% incidental actionable findings with faster clinician notification by a median time of 1 hour (range: 15 minutes-16 hours). Radiologist exam interpretation times decreased by 37%.

CONCLUSIONS : This workflow resulted in three-fold improved target lesion measurement concordance with oncology records, earlier detection and faster notification of incidental actionable findings to referring clinicians, and decreased exam interpretation times for clinical radiologists. These findings demonstrate potential roles for automation (such as AI) to improve report value, worklist prioritization, and patient care.

Do Huy M, Spear Lillian G, Nikpanah Moozhan, Mirmomen S Mojdeh, Machado Laura B, Toscano Alexandra P, Turkbey Baris, Bagheri Mohammad Hadi, Gulley James L, Folio Les R

2020-Jan

Actionable findings, Artificial intelligence, Cancer clinical trials, Radiology preprocessors, Tumor quantification

Radiology Radiology

Deep Learning Reconstruction at CT: Phantom Study of the Image Characteristics.

In Academic radiology ; h5-index 0.0

OBJECTIVES : Noise, commonly encountered on computed tomography (CT) images, can impact diagnostic accuracy. To reduce the image noise, we developed a deep-learning reconstruction (DLR) method that integrates deep convolutional neural networks into image reconstruction. In this phantom study, we compared the image noise characteristics, spatial resolution, and task-based detectability on DLR images and images reconstructed with other state-of-the art techniques.

METHODS : We scanned a phantom harboring cylindrical modules with different contrast on a 320-row detector CT scanner. Phantom images were reconstructed with filtered back projection, hybrid iterative reconstruction, model-based iterative reconstruction, and DLR. The standard deviation of the CT number and the noise power spectrum were calculated for noise characterization. The 10% modulation-transfer function (MTF) level was used to evaluate spatial resolution; task-based detectability was assessed using the model observer method.

RESULTS : On images reconstructed with DLR, the noise was lower than on images subjected to other reconstructions, especially at low radiation dose settings. Noise power spectrum measurements also showed that the noise amplitude was lower, especially for low-frequency components, on DLR images. Based on the MTF, spatial resolution was higher on model-based iterative reconstruction image than DLR image, however, for lower-contrast objects, the MTF on DLR images was comparable to images reconstructed with other methods. The machine observer study showed that at reduced radiation-dose settings, DLR yielded the best detectability.

CONCLUSION : On DLR images, the image noise was lower, and high-contrast spatial resolution and task-based detectability were better than on images reconstructed with other state-of-the art techniques. DLR also outperformed other methods with respect to task-based detectability.

Higaki Toru, Nakamura Yuko, Zhou Jian, Yu Zhou, Nemoto Takuya, Tatsugami Fuminari, Awai Kazuo

2020-Jan

Phantoms, X-ray computed tomography, artificial intelligence, imaging, machine learning, neural networks

Radiology Radiology

How the FDA Regulates AI.

In Academic radiology ; h5-index 0.0

Recent years have seen digital technologies increasingly leveraged to multiply conventional imaging modalities' diagnostic power. Artificial intelligence (AI) is most prominent among these in the radiology space, touted as the "stethoscope of the 21st century" for its potential to revolutionize diagnostic precision, provider workflow, and healthcare expenditure. Partially owing to AI's unique characteristics, and partially due to its novelty, existing regulatory paradigms are not well suited to balancing patient safety with furthering the growth of this new sector. The current review examines the historic, current, and proposed regulatory treatment of AI-empowered medical devices by the US Food and Drug Administration (FDA). An innovative framework proposed by the FDA seeks to address these issues by looking to current good manufacturing practices (cGMP) and adopting a total product lifecycle (TPLC) approach. If brought into force, this may reduce the regulatory burden incumbent on developers, while holding them to rigorous quality standards, maximizing safety, and permitting the field to mature.

Harvey H Benjamin, Gowda Vrushab

2020-Jan

Artificial intelligence, FDA, Medical Device, Policy, Radiology, Regulation

Radiology Radiology

School of Block-Review of Blockchain for the Radiologists.

In Academic radiology ; h5-index 0.0

Blockchain, the underlying technology for Bitcoin, is a distributed digital ledger technology that enables record verification by many independent parties rather than a centralized authority, therefore making it more difficult to tamper with the data. This emerging technology has the potential to enhance various authentication and verification processes in image sharing and data security. It has the potential to promote patient-centered healthcare by giving greater control to patients over their own data. Blockchain can also be utilized for administrative tasks, such as credentialing, claims adjudication, and billing management. It can also be utilized to enhance software supporting research and clinical trials. Blockchain complements artificial intelligence (AI) and these can work synergistically to create better solutions. Although many challenges exist for increased adoption of blockchain within radiology and healthcare in general, it can play a major role in our practice and consequently, it is important for medical imaging professionals to become familiar with the technology.

Abdullah Selwan, Rothenberg Steven, Siegel Eliot, Kim Woojin

2020-Jan

Bitcoin, Blockchain, Ethereum, Machine learning, Radiology

Radiology Radiology

Breast Cancer Radiogenomics: Current Status and Future Directions.

In Academic radiology ; h5-index 0.0

Radiogenomics is an area of research that aims to identify associations between imaging phenotypes ("radio-") and tumor genome ("-genomics"). Breast cancer radiogenomics research in particular has been an especially prolific area of investigation in recent years as evidenced by the wide number and variety of publications and conferences presentations. To date, research has primarily been focused on dynamic contrast enhanced pre-operative breast MRI and breast cancer molecular subtypes, but investigations have extended to all breast imaging modalities as well as multiple additional genetic markers including those that are commercially available. Furthermore, both human and computer-extracted features as well as deep learning techniques have been explored. This review will summarize the specific imaging modalities used in radiogenomics analysis, describe the methods of extracting imaging features, and present the types of genomics, molecular, and related information used for analysis. Finally, the limitations and future directions of breast cancer radiogenomics research will be discussed.

Grimm Lars J, Mazurowski Maciej A

2020-Jan

MRI, breast cancer, deep learning, radiogenomics

Ophthalmology Ophthalmology

A Review of Perceptual Expertise in Radiology-How it develops, How we can test it, and Why humans still matter in the era of Artificial Intelligence.

In Academic radiology ; h5-index 0.0

As the first step in image interpretation is detection, an error in perception can prematurely end the diagnostic process leading to missed diagnoses. Because perceptual errors of this sort-"failure to detect"-are the most common interpretive error (and cause of litigation) in radiology, understanding the nature of perceptual expertise is essential in decreasing radiology's long-standing error rates. In this article, we review what constitutes a perceptual error, the existing models of radiologic image perception, the development of perceptual expertise and how it can be tested, perceptual learning methods in training radiologists, and why understanding perceptual expertise is still relevant in the era of artificial intelligence. Adding targeted interventions, such as perceptual learning, to existing teaching practices, has the potential to enhance expertise and reduce medical error.

Waite Stephen, Farooq Zerwa, Grigorian Arkadij, Sistrom Christopher, Kolla Srinivas, Mancuso Anthony, Martinez-Conde Susana, Alexander Robert G, Kantor Alan, Macknik Stephen L

2020-Jan

Artificial intelligence, Attention, Expertise, Gist, Holistic processing, Perceptual learning, Radiology, Visual perception, Visual search

Radiology Radiology

Impact of the Artificial Nudge.

In Academic radiology ; h5-index 0.0

RATIONALE AND OBJECTIVES : Artificial intelligence (AI) is playing a growing role in the field of radiology. This article seeks to help readers quantify its impact when put into practice, using a lung nodule flagger as an example.

MATERIALS AND METHODS : The one-time and ongoing costs associated with AI are explored. Costs are divided into three categories: direct costs, costs associated with operational changes, and downstream costs. Examples of each are provided.

RESULTS : A framework for estimating the financial impact of AI is provided.

CONCLUSION : The impact of AI is quantifiable, but estimates of its financial impact may not be portable across contexts. Different organizations may implement AI in different ways due to differences in clinical practices. Furthermore, different organizations have different hurdle rates for their investments. Finally, international cost-effectiveness analyses may not be generalizable due to differences in both practice patterns and the valuation placed upon quality. When quantifying the impact of AI, organizations should consider relying upon pilots and data from other similarly-situated organizations.

Powell Adam C

2020-Jan

AI, Artificial intelligence, Cost, Financial impact, Radiology, Time-value of money

General General

Decision tree machine learning applied to bovine tuberculosis risk factors to aid disease control decision making.

In Preventive veterinary medicine ; h5-index 37.0

Identifying and understanding the risk factors for endemic bovine tuberculosis (TB) in cattle herds is critical for the control of this disease. Exploratory machine learning techniques can uncover complex non-linear relationships and interactions within disease causation webs, and enhance our knowledge of TB risk factors and how they are interrelated. Classification tree analysis was used to reveal associations between predictors of TB in England and each of the three surveillance risk areas (High Risk, Edge, and Low Risk) in 2016, identifying the highest risk herds. The main classifying predictor for farms in England overall related to the TB prevalence in the 100 nearest cattle herds. In the High Risk and Edge areas it was the number of slaughterhouse destinations and in the Low Risk area it was the number of cattle tested in surveillance tests. How long ago the last confirmed incident was resolved was the most frequent classifier in trees; if within two years, leading to the highest risk group of herds in the High Risk and Low Risk areas. At least two different slaughterhouse destinations led to the highest risk group of herds in England, whereas in the Edge area it was a combination of no contiguous low-risk neighbours (i.e. in a 1 km radius) and a minimum proportion of 6-23 month-old cattle in November. A threshold value of prevalence in 100 nearest neighbours increased the risk in all areas, although the value was specific to each area. Having low-risk contiguous neighbours reduced the risk in the Edge and High Risk areas, whereas high-risk ones increased the risk in England overall and in the Edge area specifically. The best classification tree models informed multivariable binomial logistic regression models in each area, adding statistical inference outputs. These two approaches showed similar predictive performance although there were some disparities regarding what constituted high-risk predictors. Decision tree machine learning approaches can identify risk factors from webs of causation: information which may then be used to inform decision making for disease control purposes.

Romero M Pilar, Chang Yu-Mei, Brunton Lucy A, Parry Jessica, Prosser Alison, Upton Paul, Rees Eleanor, Tearne Oliver, Arnold Mark, Stevens Kim, Drewe Julian A

2019-Nov-30

Bovine tuberculosis, Classification tree, England, Logistic regression, Machine learning, Risk factors

General General

Heartbeat classification using deep residual convolutional neural network from 2-lead electrocardiogram.

In Journal of electrocardiology ; h5-index 0.0

BACKGROUND : The electrocardiogram (ECG) has been widely used in the diagnosis of heart disease such as arrhythmia due to its simplicity and non-invasive nature. Arrhythmia can be classified into many types, including life-threatening and non-life-threatening. Accurate detection of arrhythmic types can effectively prevent heart disease and reduce mortality.

METHODS : In this study, a novel deep learning method for classification of cardiac arrhythmia according to deep residual network (ResNet) is presented. We developed a 31-layer one-dimensional (1D) residual convolutional neural network. The algorithm includes four residual blocks, each of which consists of three 1D convolution layers, three batch normalization (BP) layers, three rectified linear unit (ReLU) layers, and an "identity shortcut connections" structure. In addition, we propose to use 2-lead ECG signals in combination with deep learning methods to automatically identify five different types of heartbeats.

RESULTS : We have obtained an average accuracy, sensitivity and positive predictivity of 99.06%, 93.21% and 96.76% respectively for single-lead ECG heartbeats. In the 2-lead datasets, the results show that the deep ResNet model has high classification performance, achieving an accuracy of 99.38%, sensitivity of 94.54%, and specificity of 98.14%.

CONCLUSION : The proposed method can be used as an adjunct tool to assist clinicians in their diagnosis.

Li Zhi, Zhou Dengshi, Wan Li, Li Jian, Mou Wenfeng

2019-Nov-22

2-Lead, Arrhythmia, Deep learning, ECG signals, Heartbeat classification, Residual convolutional neural network

General General

The NAD+-mitophagy axis in healthy longevity and in artificial intelligence-based clinical applications.

In Mechanisms of ageing and development ; h5-index 0.0

Nicotinamide adenine dinucleotide (NAD+) is an important natural molecule involved in fundamental biological processes, including the TCA cycle, OXPHOS, β-oxidation, and is a co-factor for proteins promoting healthy longevity. NAD+ depletion is associated with the hallmarks of ageing and may contribute to a wide range of age-related diseases including metabolic disorders, cancer, and neurodegenerative diseases. One of the central pathways by which NAD+ promotes healthy ageing is through regulation of mitochondrial homeostasis via mitochondrial biogenesis and the clearance of damaged mitochondria via mitophagy. Here, we highlight the contribution of the NAD+-mitophagy axis to ageing and age-related diseases, and evaluate how boosting NAD+ levels may emerge as a promising therapeutic strategy to counter ageing as well as neurodegenerative diseases including Alzheimer's disease. The potential use of artificial intelligence to understand the roles and molecular mechanisms of the NAD+-mitophagy axis in ageing is discussed, including possible applications in drug target identification and validation, compound screening and lead compound discovery, biomarker development, as well as efficacy and safety assessment. Advances in our understanding of the molecular and cellular roles of NAD+ in mitophagy will lead to novel approaches for facilitating healthy mitochondrial homoeostasis that may serve as a promising therapeutic strategy to counter ageing-associated pathologies and/or accelerated ageing.

Aman Yahyah, Frank Johannes, Lautrup Sofie Hindkjær, Matysek Adrian, Niu Zhangming, Yang Guang, Shi Liu, Bergersen Linda H, Storm-Mathisen Jon, Rasmussen Lene J, Bohr Vilhelm A, Nilsen Hilde, Fang Evandro F

2019-Dec-05

Alzheimer’s disease, NAD(+), age-related diseases, ageing, artificial intelligence, mitophagy

General General

Eutrophication and heavy metal pollution patterns in the water suppling lakes of China's south-to-north water diversion project.

In The Science of the total environment ; h5-index 0.0

This study used non-supervised machine learning self-organizing maps (SOM) in conjunction with traditional multivariate statistical techniques (e.g., hierarchical cluster analysis, principle component analysis, Pearson's correlation analysis) to investigate spatio-temporal patterns of eutrophication and heavy metal pollution in the water supplying lakes (i.e., the Gao-Bao-Shaobo Lake, GBSL) of the eastern route of China's South-to-North Water Diversion Project (SNWDP-ER). A total of 28 water quality parameters were seasonally monitored at 33 sampling sites in the GBSL during 2016 to 2017 (i.e., 132 water samples were collected in four seasons). The results indicated that: 1) spatially, the western and south-western GBSL was relatively more eutrophic and polluted with heavy metals; and 2) temporally, the lakes suffered from high risks of heavy metal contamination in spring, but eutrophication in summer while water quality in winter was the best among the four seasons. Two main potential sources of pollution and transport routes were identified and discussed based on the pollution patterns. These findings contributed considerably to providing in-depth understanding of water pollution patterns, as well as potential pollution sources in the water-supplying region. Such understanding is crucial for developing pollution control and management strategies for this mega inter-basin water transfer project.

Guo Chuanbo, Chen Yushun, Xia Wentong, Qu Xiao, Yuan Hui, Xie Songguang, Lin Lian-Shin

2019-Nov-18

Eutrophication, Heavy metals, Multivariate analysis, Self-organizing Map, South-to-North Water Diversion Project, Water quality safety

General General

Explore a Multivariate Bayesian Uncertainty Processor driven by artificial neural networks for probabilistic PM2.5 forecasting.

In The Science of the total environment ; h5-index 0.0

Quantifying predictive uncertainty inherent in the nonlinear multivariate dependence structure of multi-step-ahead PM2.5 forecasts is challenging. This study integrates a Multivariate Bayesian Uncertainty Processor (MBUP) and an artificial neural network (ANN) to make accurate probabilistic PM2.5 forecasts. The contributions of the proposed approach are two-fold. First, the MBUP can capture the nonlinear multivariate dependence structure between observed and forecasted data. Second, the MBUP can alleviate predictive uncertainty encountered in PM2.5 forecast models that are configured by ANNs. The reliability of the proposed approach was assessed by a case study on air quality in Taipei City of Taiwan. We consider forecasts of PM2.5 concentrations as a function of meteorological and air quality factors based on long-term (2010-2018) hourly observational datasets. Firstly, the Back Propagation Neural Network (BPNN) and the Adaptive Neural Fuzzy Inference System (ANFIS) were investigated to produce deterministic forecasts. Results revealed that the ANFIS model could learn different air pollutant emission mechanisms (i.e. primary, secondary and natural processes) from the clustering-based fuzzy inference system and produce more accurate deterministic forecasts than the BPNN. The ANFIS model then provided inputs (i.e. point estimates) to probabilistic forecast models. Next, two post-processing techniques (MBUP and the Univariate Bayesian Uncertainty Processor (UBUP)) were separately employed to produce probabilistic forecasts. The Bayesian Uncertainty Processors (BUPs) can model the dependence structure (i.e. posterior density function) between observed and forecasted data using a prior density function and a likelihood density function. Here in BUPs, the Monte Carlo simulation was introduced to create a probabilistic predictive interval of PM2.5 concentrations. The results demonstrated that the MBUP not only outperformed the UBUP but also suitably characterized the complex nonlinear multivariate dependence structure between observations and forecasts. Consequently, the proposed approach could reduce predictive uncertainty while significantly improving model reliability and PM2.5 forecast accuracy for future horizons.

Zhou Yanlai, Chang Li-Chiu, Chang Fi-John

2019-Oct-31

Air quality, Artificial intelligence, Bayesian Uncertainty Processor, Probabilistic forecast, Taipei City

General General

Medication Regimen Extraction From Clinical Conversations

arxiv preprint

Extracting relevant information from clinical conversations and providing it to doctors and patients might help in addressing doctor burnout and patient forgetfulness. In this paper, we focus on extracting the Medication Regimen (dosage and frequency for medications) discussed in a clinical conversation. We frame the problem as a Question Answering (QA) task and perform comparative analysis over: a QA approach, a new combined QA and Information Extraction approach and other baselines. We use a small corpus of 6,692 annotated doctor-patient conversations for the task. Clinical conversation corpora are costly to create, difficult to handle (because of data privacy concerns), and thus `scarce'. We address this data scarcity challenge through data augmentation methods, using publicly available embeddings and pretrain part of the network on a related task of summarization to improve the model's performance. Compared to the baseline, our best-performing models improve the dosage and frequency extractions' ROUGE-1 F1 scores from 54.28 and 37.13 to 89.57 and 45.94, respectively. Using our best-performing model, we present the first fully automated system that can extract Medication Regimen (MR) tags from spontaneous doctor-patient conversations with about ~71% accuracy.

Sai P. Selvaraj, Sandeep Konam

2019-12-10

General General

Spatial pattern and determinants of global invasion risk of an invasive species, sharpbelly Hemiculter leucisculus (Basilesky, 1855).

In The Science of the total environment ; h5-index 0.0

Invasive species have imposed huge negative impacts on worldwide aquatic ecosystems and are generally difficult or impossible to be eradicated once established. Consequently, it becomes particularly important to ascertain their invasion risk and its determinants since such information can help us formulate more effective preventive or management actions and direct these measures to those areas where they are truly needed so as to ease regulatory burdens. Here, we examined the global invasion risk and its determinants of sharpbelly (Hemiculter leucisculus), one freshwater fish which has a high invasive potential, by using species distribution models (SDMs) and a layer overlay method. Specifically, first an ensemble species distribution model and its basal models (developed from seven machine learning algorithms) were explored to forecast the global habitat-suitability and variables importance for this species, and then a global invasion risk map was created by combining habitat-suitability with a proxy for introduction likelihood (entailing propagule pressure and dispersal constraints) of exotic sharpbelly. The results revealed that (1) the ensemble model had the highest predictive power in forecasting sharpbelly's global habitat-suitability; (2) areas with high invasion risk by sharpbelly patchily spread over the world except Antarctica; and (3) the Human Influence Index (HII), rather than any of the bioclimatic variables, was the most important factor influencing sharpbelly' future invasion. Based on these results, the present study also attempted to propose a series of prevention and management strategies to eliminate or alleviate the adverse effects caused by this species' further expansion.

Dong Xianghong, Ju Tao, Grenouillet Gaël, Laffaille Pascal, Lek Sovan, Liu Jiashou

2019-Nov-02

Aquatic invasive species, Ensemble predicting, Habitat-suitability, Invasion risk, Management strategies, Species distribution models

General General

Context-Dependent Models for Predicting and Characterizing Facial Expressiveness

arxiv preprint

In recent years, extensive research has emerged in affective computing on topics like automatic emotion recognition and determining the signals that characterize individual emotions. Much less studied, however, is expressiveness, or the extent to which someone shows any feeling or emotion. Expressiveness is related to personality and mental health and plays a crucial role in social interaction. As such, the ability to automatically detect or predict expressiveness can facilitate significant advancements in areas ranging from psychiatric care to artificial social intelligence. Motivated by these potential applications, we present an extension of the BP4D+ dataset with human ratings of expressiveness and develop methods for (1) automatically predicting expressiveness from visual data and (2) defining relationships between interpretable visual signals and expressiveness. In addition, we study the emotional context in which expressiveness occurs and hypothesize that different sets of signals are indicative of expressiveness in different contexts (e.g., in response to surprise or in response to pain). Analysis of our statistical models confirms our hypothesis. Consequently, by looking at expressiveness separately in distinct emotional contexts, our predictive models show significant improvements over baselines and achieve comparable results to human performance in terms of correlation with the ground truth.

Victoria Lin, Jeffrey M. Girard, Louis-Philippe Morency

2019-12-10

General General

Flocculation-dewatering prediction of fine mineral tailings using a hybrid machine learning approach.

In Chemosphere ; h5-index 0.0

Polymer-assisted flocculation-dewatering of mineral processing tailings (MPT) is crucial for its environmental disposal. To reduce the number of laboratory experiments, this study proposes a novel and hybrid machine learning (ML) method for the prediction of the flocculation-dewatering performance. The proposed ML method utilizes principle component analysis (PCA) for the dimension-reduction of the input space. Then, ML prediction is performed using the combination of particle swarm optimisation (PSO) and adaptive neuro-fuzzy inference system (ANFIS). Monte Carlo simulations are used for the converged results. An experimental dataset of 102 data instances is prepared. 17 variables are chosen as inputs and the initial settling rate (ISR) is chosen as the output. Along with the raw dataset, two new datasets are prepared based on the cumulative sum of variance, namely PCA99 with 9 variables and PCA95 with 7 variables. The results show that Monte Carlo simulations need to be performed for over 100 times to reach the converged results. Based on the statistic indicators, it is found that the ML prediction on PCA99 and PCA95 is better than that on the raw dataset (average correlation coefficient is 0.85 for the raw dataset, 0.89 for the PCA99 dataset and 0.88 for the PCA95 dataset). Overall speaking, ML prediction has good prediction performance and it can be employed by the mine site to improve the efficiency and cost-effectiveness. This study presents a benchmark study for the prediction of ISR, which, with better consolidation and development, can become important tools for analysing and modelling flocculate-settling experiments.

Qi Chongchong, Ly Hai-Bang, Chen Qiusong, Le Tien-Thinh, Le Vuong Minh, Pham Binh Thai

2019-Nov-25

Flocculation and dewatering, Mineral processing tailings, Monte Carlo simulations, PSO and ANFIS, Polymer, Principal component analysis

General General

Novel knowledge-based treatment planning model for hypofractionated radiotherapy of prostate cancer patients.

In Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) ; h5-index 0.0

PURPOSE : To demonstrate the strength of an innovative knowledge-based model-building method for radiotherapy planning using hypofractionated, multi-target prostate patients.

MATERIAL AND METHODS : An initial RapidPlan model was trained using 48 patients who received 60 Gy to prostate (PTV60) and 44 Gy to pelvic nodes (PTV44) in 20 fractions. To improve the model's goodness-of-fit, an intermediate model was generated using the dose-volume histograms of best-spared organs-at-risk (OARs) of the initial model. Using the intermediate model and manual tweaking, all 48 cases were re-planned. The final model, trained using these re-plans, was validated on 50 additional patients. The validated final model was used to determine any planning advantage of using three arcs instead of two on 16 VMAT cases and tested on 25 additional cases to determine efficacy for single-PTV (PTV60-only) treatment planning.

RESULTS : For model validation, PTV V95% of 99.9% was obtained by both clinical and knowledge-based planning. D1% was lower for model plans: by 1.23 Gy (PTV60, CI = [1.00, 1.45]), and by 2.44 Gy (PTV44, CI = [1.72, 3.16]). OAR sparing was superior for knowledge-based planning: ΔDmean = 3.70 Gy (bladder, CI = [2.83, 4.57]), and 3.22 Gy (rectum, CI = [2.48, 3.95]); ΔD2% = 1.17 Gy (bowel bag, CI = [0.64, 1.69]), and 4.78 Gy (femoral heads, CI = [3.90, 5.66]). Using three arcs instead of two, improvements in OAR sparing and PTV coverage were statistically significant, but of magnitudes < 1 Gy. The model failed at reliable DVH predictions for single PTV plans.

CONCLUSIONS : Our knowledge-based model delivers efficient, consistent plans with excellent PTV coverage and improved OAR sparing compared to clinical plans.

Chatterjee Avishek, Serban Monica, Faria Sergio, Souhami Luis, Cury Fabio, Seuntjens Jan

2019-Dec-06

Automatic planning, Knowledge-based, Machine learning, Prostate radiotherapy

General General

Supervised and unsupervised algorithms for bioinformatics and data science.

In Progress in biophysics and molecular biology ; h5-index 0.0

Bioinformatics refers to an ever evolving huge field of research based on millions of algorithms, designated to several data banks. Such algorithms are either supervised or unsupervised. In this article, a detailed overview of the supervised and unsupervised techniques is presented with the aid of examples. The aim of this article is to provide the readers with the basic understanding of the state of the art models, which are key ingredients of explainable machine learning in the field of bioinformatics.

Sohail Ayesha, Arif Fatima

2019-Dec-06

Algorithms, Evolutionary bioinformatics, Machine learning, Support vector machine learning

Public Health Public Health

Highly precise risk prediction model for new-onset hypertension using artificial intelligence techniques.

In Journal of clinical hypertension (Greenwich, Conn.) ; h5-index 0.0

Hypertension is a significant public health issue. The ability to predict the risk of developing hypertension could contribute to disease prevention strategies. This study used machine learning techniques to develop and validate a new risk prediction model for new-onset hypertension. In Japan, Industrial Safety and Health Law requires employers to provide annual health checkups to their employees. We used 2005-2016 health checkup data from 18 258 individuals, at the time of hypertension diagnosis [Year (0)] and in the two previous annual visits [Year (-1) and Year (-2)]. Data were entered into models based on machine learning methods (XGBoost and ensemble) or traditional statistical methods (logistic regression). Data were randomly split into a derivation set (75%, n = 13 694) used for model construction and development, and a validation set (25%, n = 4564) used to test performance of the derived models. The best predictor in the XGBoost model was systolic blood pressure during cardio-ankle vascular index measurement at Year (-1). Area under the receiver operator characteristic curve values in the validation cohort were 0.877, 0.881, and 0.859 for the XGBoost, ensemble, and logistic regression models, respectively. We have developed a highly precise prediction model for future hypertension using machine learning methods in a general normotensive population. This could be used to identify at-risk individuals and facilitate earlier non-pharmacological intervention to prevent the future development of hypertension.

Kanegae Hiroshi, Suzuki Kenji, Fukatani Kyohei, Ito Tetsuya, Harada Nakahiro, Kario Kazuomi

2019-Dec-09

artificial intelligence, hypertension, machine learning, prediction model

General General

Automatic Multi-organ Segmentation in Dual Energy CT (DECT) with Dedicated 3D Fully Convolutional DECT Networks.

In Medical physics ; h5-index 59.0

PURPOSE : Dual-energy computed tomography (DECT) has shown great potential in many clinical applications. By incorporating the information from two different energy spectra, DECT provides higher contrast and reveals more material differences of tissues compared to conventional single energy CT (SECT). Recent researches show that automatic multi-organ segmentation of DECT data can improve DECT clinical applications. However, most segmentation methods are designed for SECT, while DECT has been significantly less in research. Therefore, a novel approach is required that is able to take full advantage of the extra information provided by DECT.

METHODS : In the scope of this work, we proposed four 3D fully convolutional neural network algorithms for the automatic segmentation of DECT data. We incorporated the extra energy information differently and embedded the merge of information in each of the network architectures.

RESULTS : Quantitative evaluation using 45 thorax/abdomen DECT datasets acquired with aclinical dual-source CT system was investigated. The segmentation of six thoracic and abdominal organs (left and right lungs, liver, spleen, and left and right kidneys) were evaluated using a 5-fold cross-validation strategy. In all of the tests, we achieved the best average Dice coefficients of 98 %for the right lung, 98 % for the left lung, 96 % for the liver, 92 % for the spleen, 95 % for the right kidney, 93 % for the left kidney, respectively. The network architectures exploiting dual energy spectra outperform deep learning for SECT.

CONCLUSION : The results of the cross-validation show that our methods are feasible and promising. Successful tests on special clinical cases reveal that our methods have high adaptability in the practical application.

Chen Shuqing, Zhong Xia, Hu Shiyang, Dorn Sabrina, Kachelrieß Marc, Lell Michael, Maier Andreas

2019-Dec-09

DECT, FCN, Multi-organ, U-Net, deep learning

General General

Development of an Alarm Algorithm, With Nanotechnology Multimodal Sensor, to Predict Impending Infusion Failure and Improve Safety of Peripheral Intravenous Catheters in Neonates.

In Advances in neonatal care : official journal of the National Association of Neonatal Nurses ; h5-index 0.0

BACKGROUND : Peripheral intravenous catheters connected to an infusion pump are necessary for the delivery of fluids, nutrition, and medications to hospitalized neonates but are not without complications. These adverse events contribute to hospital-acquired patient harm. An artificial intelligence theory called fuzzy logic may allow the use of appropriate variables to predict infusion failure.

PURPOSE : This innovative study aimed to develop an intravenous infusion nanotechnology monitoring system that would alert the nurse to impending peripheral intravenous infusion failure.

METHODS : An intravenous infusion nanotechnology monitoring system, using predictor variables of pressure, pH, and oxygen saturation used in a fuzzy logic alarm algorithm was developed to alert the nurse to impending peripheral intravenous infusion failure.

FINDINGS : The developed intravenous infusion nanotechnology monitoring system is composed of a peripheral intravenous catheter with nanotechnology multimodal sensor, an intravenous pump, a fuzzy logic algorithm, and alarm. For example, using this system, an elevated in-line pressure, a low pH, and a low venous oxygen level would generate an alarm for possible impending infusion failure.

IMPLICATIONS FOR PRACTICE : With further development, this technology may help nurses predict and prevent adverse outcomes from intravenous infusions. This work shows how nurses can be content experts and innovators of technology that they use to make clinical decisions.

IMPLICATIONS FOR RESEARCH : After regulatory approval, a randomized controlled trial may be performed to investigate whether interventions at the time of an alarm would result in fewer adverse outcomes and improve safety.

Bosque Elena M

2019-Dec-06

Radiology Radiology

Research progress of computer aided diagnosis system for pulmonary nodules in CT images.

In Journal of X-ray science and technology ; h5-index 0.0

BACKGROUND AND OBJECTIVE : Since CAD (Computer Aided Diagnosis) system can make it easier and more efficient to interpret CT (Computer Tomography) images, it has gained much attention and developed rapidly in recent years. This article reviews recent CAD techniques for pulmonary nodule detection and diagnosis in CT Images.

METHODS : CAD systems can be classified into computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems. This review reports recent researches of both systems, including the database, technique, innovation and experimental results of each work. Multi-task CAD systems, which can handle segmentation, false positive reduction, malignancy prediction and other tasks at the same time. The commercial CAD systems are also briefly introduced.

RESULTS : We have found that deep learning based CAD is the mainstream of current research. The reported sensitivity of deep learning based CADe systems ranged between 80.06% and 94.1% with an average 4.3 false-positive (FP) per scan when using LIDC-IDRI dataset, and between 94.4% and 97.9% with an average 4 FP/scan when using LUNA16 dataset, respectively. The overall accuracy of deep learning based CADx systems ranged between 86.84% and 92.3% with an average AUC of 0.956 reported when using LIDC-IDRI dataset.

CONCLUSIONS : We summarized the current tendency and limitations as well as future challenges in this field. The development of CAD needs to meet the rigid clinical requirements, such as high accuracy, strong robustness, high efficiency, fine-grained analysis and classification, and to provide practical clinical functions. This review provides helpful information for both engineering researchers and radiologists to learn the latest development of CAD systems.

Wang Yu, Wu Bo, Zhang Nan, Liu Jiabao, Ren Fei, Zhao Liqin

2019-Dec-02

Pulmonary nodules, computer-aided detection (CADe), computer-aided diagnosis \n(CADx), multi-task CAD

General General

Stumbling prediction based on plantar pressure distribution.

In Work (Reading, Mass.) ; h5-index 0.0

BACKGROUND : Stumbles are common accidents that can result in falls and serious injuries, particularly in the workplace where back and forth movements are involved and in offices where high heels are imperative. Currently, the characteristics of plantar pressure during a stumble and the differences between stumbling and a normal gait remain unclear.

OBJECTIVE : This paper is aimed at providing insights into the feasibility of the data mining technique for interventions in stumble-related occupational safety issues.

METHODS : The characteristics of plantar pressure distribution during stumbling and normal gait were analyzed by using the power spectrum density (PSD) and the Support Vector Machine (SVM). The PSD, a novel pattern recognition feature, was used to mathematically describe the image signal. The SVM, a powerful data mining technique, was used as the classifier to recognize a stumble. Dynamic plantar pressures were measured from twelve healthy participants as they walked.

RESULTS : The plantar pressures of the stumbling gaits had significantly different patterns compared to the normal ones, from either a qualitative or quantitative perspective. The mean recognition accuracy of the proposed method reached 96.7% .

CONCLUSIONS : This study helps better understand stumbles and provides a theoretical basis for stumble-related occupational injuries. In addition, the stumble is the precursor of a fall and the research on stumble recognition would be of value to predict and provide warnings of falls and to design anti-fall devices for potential victims.

Niu Jianwei, Zheng Yanling, Liu Haixiao, Chen Xiao, Ran Linghua

2019-Dec-03

Gait recognition, SVM, artificial intelligence, power spectrum density

General General

Comparative assessment of the flash-flood potential within small mountain catchments using bivariate statistics and their novel hybrid integration with machine learning models.

In The Science of the total environment ; h5-index 0.0

The present study is carried out in the context of the continuous increase, worldwide, of the number of flash-floods phenomena. Also, there is an evident increase of the size of the damages caused by these hazards. Bâsca Chiojdului River Basin is one of the most affected areas in Romania by flash-flood phenomena. Therefore, Flash-Flood Potential Index (FFPI) was defined and calculated across the Bâsca Chiojdului river basin by using one bivariate statistical method (Statistical Index) and its novel ensemble with the following machine learning models: Logistic Regression, Classification and Regression Trees, Multilayer Perceptron, Random Forest and Support Vector Machine and Decision Tree CART. In a first stage, the areas with torrentiality were digitized based on orthophotomaps and field observations. These regions, together with an equal number of non-torrential pixels, were further divided into training surfaces (70%) and validating surfaces (30%). The next step of the analysis consisted of the selection of flash-flood conditioning factors based on the multicollinearity investigation and predictive ability estimation through Information Gain method. Eight factors, from a total of ten flash-floods predictors, were selected in order to be included in the FFPI calculation process. By applying the models represented by Statistical Index and its ensemble with the machine learning algorithms, the weight of each conditioning factor and of each factor class/category in the FFPI equations was established. Once the weight values were derived, the FFPI values across the Bâsca Chiojdului river basin were calculated by overlaying the flash-flood predictors in GIS environment. According to the results obtained, the central part of Bâsca Chiojdului river basin has the highest susceptibility to flash-flood phenomena. Thus, around 30% of the study site has high and very high values of FFPI. The results validation was carried out by applying the Prediction Rate and Success Rate. The methods revealed the fact that the Multilayer Perceptron - Statistical Index (MLP-SI) ensemble has the highest efficiency among the 3 methods.

Costache Romulus, Hong Haoyuan, Pham Quoc Bao

2019-Oct-08

Bâsca Chiojdului catchment, Ensemble models, Flash-flood potential index, Machine learning models, Statistical index

General General

Transparent Classification with Multilayer Logical Perceptrons and Random Binarization

arxiv preprint

Models with transparent inner structure and high classification performance are required to reduce potential risk and provide trust for users in domains like health care, finance, security, etc. However, existing models are hard to simultaneously satisfy the above two properties. In this paper, we propose a new hierarchical rule-based model for classification tasks, named Concept Rule Sets (CRS), which has both a strong expressive ability and a transparent inner structure. To address the challenge of efficiently learning the non-differentiable CRS model, we propose a novel neural network architecture, Multilayer Logical Perceptron (MLLP), which is a continuous version of CRS. Using MLLP and the Random Binarization (RB) method we proposed, we can search the discrete solution of CRS in continuous space using gradient descent and ensure the discrete CRS acts almost the same as the corresponding continuous MLLP. Experiments on 12 public data sets show that CRS outperforms the state-of-the-art approaches and the complexity of the learned CRS is close to the simple decision tree.

Zhuo Wang, Wei Zhang, Ning Liu, Jianyong Wang

2019-12-10

Surgery Surgery

Screening key lncRNAs with diagnostic and prognostic value for head and neck squamous cell carcinoma based on machine learning and mRNA-lncRNA co-expression network analysis.

In Cancer biomarkers : section A of Disease markers ; h5-index 0.0

BACKGROUND : Head and neck squamous cell carcinoma (HNSCC) is the seventh most common type of cancer around the world. The aim of this study was to seek the long non-coding RNAs (lncRNAs) acting as diagnostic and prognostic biomarker of HNSCC.

METHODS : Base on TCGA dataset, the differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) were identified between HNSCC and normal tissue. The machine learning and survival analysis were performed to estimate the potential diagnostic and prognostic value of lncRNAs for HNSCC. We also build the co-expression network and functional annotation. The expression of selected candidate mRNAs and lncRNAs were validated by Quantitative real time polymerase chain reaction (qRT-PCR).

RESULTS : A total of 3363 DEmRNAs (1822 down-regulated and 1541 up-regulated mRNAs) and 32 DElncRNAs (13 down-regulated and 19 up-regulated lncRNAs) between HNSCC and normal tissue were obtained. A total of 13 lncRNAs (IL12A.AS1, RP11.159F24.6, RP11.863P13.3, LINC00941, FOXCUT, RNF144A.AS1, RP11.218E20.3, HCG22, HAGLROS, LINC01615, RP11.351J23.1, AC024592.9 and MIR9.3HG) were defined as optimal diagnostic lncRNAs biomarkers for HNSCC. The area under curve (AUC) of the support vector machine (SVM) model, decision tree model and random forests model and were 0.983, 0.842 and 0.983, and the specificity and sensitivity of the three model were 95.5% and 96.2%, 77.3% and 97.6% and 93.2% and 97.8%, respectively. Among them, AC024592.9, LINC00941, LINC01615 and MIR9-3HG was not only an optimal diagnostic lncRNAs biomarkers, but also related to survival time. The focal adhesion, ECM-receptor interaction, pathways in cancer and cytokine-cytokine receptor interaction were four significantly enriched pathways in DEmRNAs co-expressed with the identified optimal diagnostic lncRNAs. But for most of the selected DEmRNAs and DElncRNAs, the expression was consistent with our integrated analysis results, including LINC00941, LINC01615, FOXCUT, TGA6 and MMP13.

CONCLUSION : AC024592.9, LINC00941, LINC01615 and MIR9-3HG was not only an optimal diagnostic lncRNAs biomarkers, but also were a prognostic lncRNAs biomarkers.

Hu Ying, Guo Geyang, Li Junjun, Chen Jie, Tan Pingqing

2019-Nov-22

Head and neck squamous cell carcinoma, diagnostic, machine learning, prognostic

General General

Impact of Automatic Query Generation and Quality Recognition Using Deep Learning to Curate Evidence From Biomedical Literature: Empirical Study.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : The quality of health care is continuously improving and is expected to improve further because of the advancement of machine learning and knowledge-based techniques along with innovation and availability of wearable sensors. With these advancements, health care professionals are now becoming more interested and involved in seeking scientific research evidence from external sources for decision making relevant to medical diagnosis, treatments, and prognosis. Not much work has been done to develop methods for unobtrusive and seamless curation of data from the biomedical literature.

OBJECTIVE : This study aimed to design a framework that can enable bringing quality publications intelligently to the users' desk to assist medical practitioners in answering clinical questions and fulfilling their informational needs.

METHODS : The proposed framework consists of methods for efficient biomedical literature curation, including the automatic construction of a well-built question, the recognition of evidence quality by proposing extended quality recognition model (E-QRM), and the ranking and summarization of the extracted evidence.

RESULTS : Unlike previous works, the proposed framework systematically integrates the echelons of biomedical literature curation by including methods for searching queries, content quality assessments, and ranking and summarization. Using an ensemble approach, our high-impact classifier E-QRM obtained significantly improved accuracy than the existing quality recognition model (1723/1894, 90.97% vs 1462/1894, 77.21%).

CONCLUSIONS : Our proposed methods and evaluation demonstrate the validity and rigorousness of the results, which can be used in different applications, including evidence-based medicine, precision medicine, and medical education.

Afzal Muhammad, Hussain Maqbool, Malik Khalid Mahmood, Lee Sungyoung

2019-Dec-09

biomedical research, clinical decision support systems, data curation, deep learning, evidence-based medicine, machine learning, precision medicine

oncology Oncology

Prognostic nomogram for bladder cancer with brain metastases: a National Cancer Database analysis.

In Journal of translational medicine ; h5-index 0.0

BACKGROUND : This study aimed to establish and validate a nomogram for predicting brain metastasis in patients with bladder cancer (BCa) and assess various treatment modalities using a primary cohort comprising 234 patients with clinicopathologically-confirmed BCa from 2004 to 2015 in the National Cancer Database.

METHODS : Machine learning method and Cox model were used for nomogram construction. For BCa patients with brain metastasis, surgery of the primary site, chemotherapy, radiation therapy, palliative care, brain confinement of metastatic sites, and the Charlson/Deyo Score were predictive features identified for building the nomogram.

RESULTS : For the original 169 patients considered in the model, the areas under the receiver operating characteristic curve (AUC) were 0.823 (95% CI 0.758-0.889, P < 0.001) and 0.854 (95% CI 0.785-0.924, P < 0.001) for 0.5- and 1-year overall survival respectively. In the validation cohort, the nomogram displayed similar AUCs of 0.838 (95% CI 0.738-0.937, P < 0.001) and 0.809 (95% CI 0.680-0.939, P < 0.001), respectively. The high and low risk groups had median survivals of 1.91 and 5.09 months for the training cohort and 1.68 and 8.05 months for the validation set, respectively (both P < 0.0001).

CONCLUSIONS : Our prognostic nomogram provides a useful tool for overall survival prediction as well as assessing the risk and optimal treatment for BCa patients with brain metastasis.

Yao Zhixian, Zheng Zhong, Ke Wu, Wang Renjie, Mu Xingyu, Sun Feng, Wang Xiang, Garg Shivank, Shi Wenyin, He Yinyan, Liu Zhihong

2019-Dec-09

Bladder cancer, Brain metastasis, Machine learning, Nomogram, Overall survival

Radiology Radiology

Multi-Institutional Validation of Deep Learning for Pretreatment Identification of Extranodal Extension in Head and Neck Squamous Cell Carcinoma.

In Journal of clinical oncology : official journal of the American Society of Clinical Oncology ; h5-index 0.0

PURPOSE : Extranodal extension (ENE) is a well-established poor prognosticator and an indication for adjuvant treatment escalation in patients with head and neck squamous cell carcinoma (HNSCC). Identification of ENE on pretreatment imaging represents a diagnostic challenge that limits its clinical utility. We previously developed a deep learning algorithm that identifies ENE on pretreatment computed tomography (CT) imaging in patients with HNSCC. We sought to validate our algorithm performance for patients from a diverse set of institutions and compare its diagnostic ability to that of expert diagnosticians.

METHODS : We obtained preoperative, contrast-enhanced CT scans and corresponding pathology results from two external data sets of patients with HNSCC: an external institution and The Cancer Genome Atlas (TCGA) HNSCC imaging data. Lymph nodes were segmented and annotated as ENE-positive or ENE-negative on the basis of pathologic confirmation. Deep learning algorithm performance was evaluated and compared directly to two board-certified neuroradiologists.

RESULTS : A total of 200 lymph nodes were examined in the external validation data sets. For lymph nodes from the external institution, the algorithm achieved an area under the receiver operating characteristic curve (AUC) of 0.84 (83.1% accuracy), outperforming radiologists' AUCs of 0.70 and 0.71 (P = .02 and P = .01). Similarly, for lymph nodes from the TCGA, the algorithm achieved an AUC of 0.90 (88.6% accuracy), outperforming radiologist AUCs of 0.60 and 0.82 (P < .0001 and P = .16). Radiologist diagnostic accuracy improved when receiving deep learning assistance.

CONCLUSION : Deep learning successfully identified ENE on pretreatment imaging across multiple institutions, exceeding the diagnostic ability of radiologists with specialized head and neck experience. Our findings suggest that deep learning has utility in the identification of ENE in patients with HNSCC and has the potential to be integrated into clinical decision making.

Kann Benjamin H, Hicks Daniel F, Payabvash Sam, Mahajan Amit, Du Justin, Gupta Vishal, Park Henry S, Yu James B, Yarbrough Wendell G, Burtness Barbara A, Husain Zain A, Aneja Sanjay

2019-Dec-09

Surgery Surgery

Perspectives on the current developments with neuromodulation for the treatment of epilepsy.

In Expert review of neurotherapeutics ; h5-index 0.0

Introduction: As deep brain stimulation revolutionized the treatment of movement disorders in the late 80s, neuromodulation in the treatment of epilepsy will undoubtedly undergo transformative changes in the years to come with the exponential growth of technological development moving into mainstream practice; the appearance of companies such as Facebook, Google, Neuralink within the realm of brain-computer interfaces points to this trend.Areas covered: This perspective piece will talk about the history of brain stimulation in epilepsy, current-approved treatments, technical developments and the future of neurostimulation.Expert opinion: Further understanding of the brain alongside machine learning and innovative technology will be the future of neuromodulation for the treatment of epilepsy. All of these innovations and advances should pave the way toward overcoming the vexing underutilization of surgery in the therapeutic armamentarium against medically refractory seizures, given the implicit advantage of a neuromodulatory rather than neurodestructive approach.

Kwon Churl-Su, Jetté Nathalie, Ghatan Saadi

2019-Dec-09

Epilepsy, artificial intelligence, deep brain stimulation (DBS), neuromodulation, responsive neurostimulation (RNS), stimulation

General General

Comprehensive Exploration of Target-specific Ligands Using a Graph Convolution Neural Network.

In Molecular informatics ; h5-index 0.0

Machine learning approaches are widely used to evaluate ligand activities of chemical compounds toward potential target proteins. Especially, exploration of highly selective ligands is important for the development of new drugs with higher safety. One difficulty in constructing well-performing model predicting such a ligand activity is the absence of data on true negative ligand-protein interactions. In other words, in many cases we can access to plenty of information on ligands that bind to specific protein, but less or almost no information showing that compounds don't bind to proteins of interest. In this paper, we suggested an approach to comprehensively explore candidates for ligands specifically targeting toward proteins without using information on the true negative interaction. The approach consists of 4 steps: 1) constructing a model that distinguishes ligands for the target proteins of interest from those targeting proteins that cause off-target effects, by using graph convolution neural network (GCNN); 2) extracting feature vectors after convolution/pooling processes and mapping their principal components in two dimensions; 3) specifying regions with higher density for two ligand groups through kernel density estimation; and 4) investigating the distribution of compounds for exploration on the density map using the same classifier and decomposer. If compounds for exploration are located in higher-density regions of ligand compounds, these compounds can be regarded as having relatively high binding affinity to the major target or off-target proteins compared with other compounds. We applied the approach to the exploration of ligands for β-site amyloid precursor protein [APP]-cleaving enzyme 1 (BACE1), a major target for Alzheimer Disease (AD), with less off-target effect toward cathepsin D. We demonstrated that the density region of BACE1 and cathepsin D ligands are well-divided, and a group of natural compounds as a target for exploration of new drug candidates also has significantly different distribution on the density map.

Miyazaki Yu, Ono Naoaki, Huang Ming, Altaf-Ul-Amin Md, Kanaya Shigehiko

2019-Dec-09

BACE1, GCNN, cathepsin D, ligand selectivity, mapping of principal components

Public Health Public Health

Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients.

In NPJ digital medicine ; h5-index 0.0

Patients admitted to the intensive care unit frequently have anemia and impaired renal function, but often lack historical blood results to contextualize the acuteness of these findings. Using data available within two hours of ICU admission, we developed machine learning models that accurately (AUC 0.86-0.89) classify an individual patient's baseline hemoglobin and creatinine levels. Compared to assuming the baseline to be the same as the admission lab value, machine learning performed significantly better at classifying acute kidney injury regardless of initial creatinine value, and significantly better at predicting baseline hemoglobin value in patients with admission hemoglobin of <10 g/dl.

Dauvin Antonin, Donado Carolina, Bachtiger Patrik, Huang Ke-Chun, Sauer Christopher Martin, Ramazzotti Daniele, Bonvini Matteo, Celi Leo Anthony, Douglas Molly J

2019

Acute kidney injury, Anaemia, Chronic kidney disease, Computational models, Data integration

General General

Bridging the gap between military prolonged field care monitoring and exploration spaceflight: the compensatory reserve.

In NPJ microgravity ; h5-index 0.0

The concept of prolonged field care (PFC), or medical care applied beyond doctrinal planning timelines, is the top priority capability gap across the US Army. PFC is the idea that combat medics must be prepared to provide medical care to serious casualties in the field without the support of robust medical infrastructure or resources in the event of delayed medical evacuation. With limited resources, significant distances to travel before definitive care, and an inability to evacuate in a timely fashion, medical care during exploration spaceflight constitutes the ultimate example PFC. One of the main capability gaps for PFC in both military and spaceflight settings is the need for technologies for individualized monitoring of a patient's physiological status. A monitoring capability known as the compensatory reserve measurement (CRM) meets such a requirement. CRM is a small, portable, wearable technology that uses a machine learning and feature extraction-based algorithm to assess real-time changes in hundreds of specific features of arterial waveforms. Future development and advancement of CRM still faces engineering challenges to develop ruggedized wearable sensors that can measure waveforms for determining CRM from multiple sites on the body and account for less than optimal conditions (sweat, water, dirt, blood, movement, etc.). We show here the utility of a military wearable technology, CRM, which can be translated to space exploration.

Schlotman Taylor E, Lehnhardt Kris R, Abercromby Andrew F, Easter Benjamin D, Downs Meghan E, Akers L T C Kevin S, Convertino Victor A

2019

Biomedical engineering, Physiology, Translational research

Radiology Radiology

Reformed conventional curriculum promoting the professional interest orientation of students of medicine: JENOS.

In GMS journal for medical education ; h5-index 0.0

Introduction: In the last ten years, the medical faculty at Friedrich Schiller University Jena has reformed its traditional curriculum for human medicine. The reformed JENa professional interest-Oriented Studies (JEnaer Neigungs-Orientiertes Studium, JENOS) - with the objective to facilitate career entry through a professional interest-oriented practical approach - emerged due to the stipulation of cost neutrality. Methods: Report on the process sequence of JENOS from the reform idea to implementation: the initial processes, the development and assessment process with accompanying dialogue and dispute of the reform process within the faculty shall be discussed. The 17 objectives of the JENOS reformed traditional curriculum shall be presented and the current level of fulfilment assessed. Results: The structural link of the professional interest-oriented proposals was achieved through the recognition by the "Landesprüfungsamt" (State Examination Board) as elective subjects with 21 semester hours (SH). Feedback and evaluations were conducted using lecturer and student information systems that were implemented in parallel. Eleven of 17 objectives have been achieved, three are still in process and three have not been achieved. Discussion: A professional interest orientation could be achieved through the reform. The weaknesses are found primarily in the links between teaching content. These are currently undergoing a mapping process in order to be optimised. Conclusions: Despite cost neutrality, JENOS is the successful result of reforming the curriculum. The academic reform complied with some requirements for the Master Plan 2020 for Medical Studies in order to be able to implement future changes.

Ehlers Claudia, Wiesener Nadine, Teichgräber Ulf, Guntinas-Lichius Orlando

2019

Ambulatory-oriented medicine (AoM), Canadian Medical Education Directions of Specialist (CanMEDS) rolls, Clinic-oriented medicine (KoM), Flexner model, JENa professional interest-Oriented Studies (JENOS), JUH-specific lecturer and student information system (DOSIS), Master Plan 2020, bottom-up strategy, constructive alignment, costs, curriculum, deep learning, evaluations, identification, incentives, interactivity, learning portfolio, longitudinal curriculum, mapping, medical didactic programmes, mentoring, organisational difficulties, performance-based compensation, practical orientation, professional interest orientation, reduction of the curriculum, reform, reinforcement of ambulatory and general medicine, research-oriented Medicine (FoM), resources, scientific orientation, small group modules, student centered learning

Pathology Pathology

A Novel Clinical Six-Flavoprotein-Gene Signature Predicts Prognosis in Esophageal Squamous Cell Carcinoma.

In BioMed research international ; h5-index 102.0

Flavoproteins and their interacting proteins play important roles in mitochondrial electron transport, fatty acid degradation, and redox regulation. However, their clinical significance and function in esophageal squamous cell carcinoma (ESCC) are little known. Here, using survival analysis and machine learning, we mined 179 patient expression profiles with ESCC in GSE53625 from the Gene Expression Omnibus (GEO) database and constructed a signature consisting of two flavoprotein genes (GPD2 and PYROXD2) and four flavoprotein interacting protein genes (CTTN, GGH, SRC, and SYNJ2BP). Kaplan-Meier analysis revealed the signature was significantly associated with the survival of ESCC patients (mean survival time: 26.77 months in the high-risk group vs. 54.97 months in the low-risk group, P < 0.001, n = 179), and time-dependent ROC analysis demonstrated that the six-gene signature had good predictive ability for six-year survival for ESCC (AUC = 0.86, 95% CI: 0.81-0.90). We then validated its prediction performance in an independent set by RT-PCR (mean survival: 15.73 months in the high-risk group vs. 21.1 months in the low-risk group, P=0.032, n = 121). Furthermore, RNAi-mediated knockdown of genes in the flavoprotein signature led to decreased proliferation and migration of ESCC cells. Taken together, CTTN, GGH, GPD2, PYROXD2, SRC, and SYNJ2BP have an important clinical significance for prognosis of ESCC patients, suggesting they are efficient prognostic markers and potential targets for ESCC therapy.

Peng Liu, Guo Jin-Cheng, Long Lin, Pan Feng, Zhao Jian-Mei, Xu Li-Yan, Li En-Min

2019

General General

Application of hybrid artificial intelligence model to predict coal strength alteration during CO2 geological sequestration in coal seams.

In The Science of the total environment ; h5-index 0.0

CO2 geological sequestration in coal seams has gradually become one of the effective means to deal with the global greenhouse effect. However, the injection of CO2 into the coal seam can have an important impact on the physical and chemical properties of coal, which in turn affects the CO2 sequestration performance in coal seams and causes a large number of environmental problems. In order to better evaluate the strength alteration of coal in CO2 geological sequestration, a hybrid artificial intelligence model integrating back propagation neural network (BPNN), genetic algorithm (GA) and adaptive boosting algorithm (AdaBoost) is proposed. A total of 112 data samples for unconfined compressive strength (UCS) are retrieved from the reported studies to train and verify the proposed model. The input variables for the predictive model include coal rank, CO2 interaction time, CO2 interaction temperature and CO2 saturation pressure, and the corresponding output variable is the measured UCS. The predictive model performance is evaluated by correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The predictive results denote that the GA-BPNN-AdaBoost predictive model is an efficient and accurate method to predict coal strength alteration induced by CO2 adsorption. The simultaneous optimization of BPNN by GA and AdaBoost algorithm can greatly improve the prediction accuracy and generalization ability of the model. At the same time, the mean impact value (MIV) is used to investigate the relative importance of each input variable. The relative importance scores of coal rank, CO2 interaction time, CO2 interaction temperature and CO2 saturation pressure are 0.5475, 0.2822, 0.0373, 0.1330, respectively. The research results in this paper can provide important guiding significance for CO2 geological sequestration in coal seams.

Yan Hao, Zhang Jixiong, Zhou Nan, Li Meng

2019-Nov-06

Adaptive boosting algorithm, Back propagation neural network, Coal strength alteration, Genetic algorithm, Predictive model

General General

Medication Regimen Extraction From Clinical Conversations

arxiv preprint

Extracting relevant information from clinical conversations and providing it to doctors and patients might help in addressing doctor burnout and patient forgetfulness. In this paper, we focus on extracting the Medication Regimen (dosage and frequency for medications) discussed in a clinical conversation. We frame the problem as a Question Answering (QA) task and perform comparative analysis over: a QA approach, a new combined QA and Information Extraction approach and other baselines. We use a small corpus of 6,692 annotated doctor-patient conversations for the task. Clinical conversation corpora are costly to create, difficult to handle (because of data privacy concerns), and thus `scarce'. We address this data scarcity challenge through data augmentation methods, using publicly available embeddings and pretrain part of the network on a related task of summarization to improve the model's performance. Compared to the baseline, our best-performing models improve the dosage and frequency extractions' ROUGE-1 F1 scores from 54.28 and 37.13 to 89.57 and 45.94, respectively. Using our best-performing model, we present the first fully automated system that can extract Medication Regimen (MR) tags from spontaneous doctor-patient conversations with about ~71% accuracy.

Sai P. Selvaraj, Sandeep Konam

2019-12-10

Surgery Surgery

Design of task-specific optical systems using broadband diffractive neural networks.

In Light, science & applications ; h5-index 0.0

Deep learning has been transformative in many fields, motivating the emergence of various optical computing architectures. Diffractive optical network is a recently introduced optical computing framework that merges wave optics with deep-learning methods to design optical neural networks. Diffraction-based all-optical object recognition systems, designed through this framework and fabricated by 3D printing, have been reported to recognize hand-written digits and fashion products, demonstrating all-optical inference and generalization to sub-classes of data. These previous diffractive approaches employed monochromatic coherent light as the illumination source. Here, we report a broadband diffractive optical neural network design that simultaneously processes a continuum of wavelengths generated by a temporally incoherent broadband source to all-optically perform a specific task learned using deep learning. We experimentally validated the success of this broadband diffractive neural network architecture by designing, fabricating and testing seven different multi-layer, diffractive optical systems that transform the optical wavefront generated by a broadband THz pulse to realize (1) a series of tuneable, single-passband and dual-passband spectral filters and (2) spatially controlled wavelength de-multiplexing. Merging the native or engineered dispersion of various material systems with a deep-learning-based design strategy, broadband diffractive neural networks help us engineer the light-matter interaction in 3D, diverging from intuitive and analytical design methods to create task-specific optical components that can all-optically perform deterministic tasks or statistical inference for optical machine learning.

Luo Yi, Mengu Deniz, Yardimci Nezih T, Rivenson Yair, Veli Muhammed, Jarrahi Mona, Ozcan Aydogan

2019

Applied optics, Optical techniques, Other photonics

General General

Automated Breast Cancer Diagnosis Based on Machine Learning Algorithms.

In Journal of healthcare engineering ; h5-index 0.0

There have been several empirical studies addressing breast cancer using machine learning and soft computing techniques. Many claim that their algorithms are faster, easier, or more accurate than others are. This study is based on genetic programming and machine learning algorithms that aim to construct a system to accurately differentiate between benign and malignant breast tumors. The aim of this study was to optimize the learning algorithm. In this context, we applied the genetic programming technique to select the best features and perfect parameter values of the machine learning classifiers. The performance of the proposed method was based on sensitivity, specificity, precision, accuracy, and the roc curves. The present study proves that genetic programming can automatically find the best model by combining feature preprocessing methods and classifier algorithms.

Dhahri Habib, Al Maghayreh Eslam, Mahmood Awais, Elkilani Wail, Faisal Nagi Mohammed

2019

General General

Adversarial Deep Learning in EEG Biometrics.

In IEEE signal processing letters ; h5-index 0.0

Deep learning methods for person identification based on electroencephalographic (EEG) brain activity encounters the problem of exploiting the temporally correlated structures or recording session specific variability within EEG. Furthermore, recent methods have mostly trained and evaluated based on single session EEG data. We address this problem from an invariant representation learning perspective. We propose an adversarial inference approach to extend such deep learning models to learn session-invariant person-discriminative representations that can provide robustness in terms of longitudinal usability. Using adversarial learning within a deep convolutional network, we empirically assess and show improvements with our approach based on longitudinally collected EEG data for person identification from half-second EEG epochs.

Özdenizci Ozan, Wang Ye, Koike-Akino Toshiaki, Erdoğmuş Deniz

2019-May

EEG, adversarial learning, biometrics, convolutional networks, invariant representation, person identification

General General

Theranostic markers for personalized therapy of spider phobia: Methods of a bicentric external cross-validation machine learning approach.

In International journal of methods in psychiatric research ; h5-index 0.0

OBJECTIVES : Embedded in the Collaborative Research Center "Fear, Anxiety, Anxiety Disorders" (CRC-TRR58), this bicentric clinical study aims at identifying biobehavioral markers of treatment (non-)response by applying machine learning methodology with an external cross-validation protocol. We hypothesize that a priori prediction of treatment (non-)response is possible in a second, independent sample based on multimodal markers.

METHODS : One-session virtual reality exposure treatment (VRET) with patients with spider phobia was conducted on two sites. Clinical, neuroimaging, and genetic data were assessed at baseline, post-treatment and after 6 months. The primary and secondary outcomes defining treatment response are as follows: 30% reduction regarding the individual score in the Spider Phobia Questionnaire and 50% reduction regarding the individual distance in the behavioral avoidance test.

RESULTS : N = 204 patients have been included (n = 100 in Würzburg, n = 104 in Münster). Sample characteristics for both sites are comparable.

DISCUSSION : This study will offer cross-validated theranostic markers for predicting the individual success of exposure-based therapy. Findings will support clinical decision-making on personalized therapy, bridge the gap between basic and clinical research, and bring stratified therapy into reach. The study is registered at ClinicalTrials.gov (ID: NCT03208400).

Schwarzmeier Hanna, Leehr Elisabeth Johanna, Böhnlein Joscha, Seeger Fabian Reinhard, Roesmann Kati, Gathmann Bettina, Herrmann Martin J, Siminski Niklas, Junghöfer Markus, Straube Thomas, Grotegerd Dominik, Dannlowski Udo

2019-Dec-08

machine learning, spider phobia, theranostic markers

General General

A Preliminary Precision Treatment Rule for Remission of Suicide Ideation.

In Suicide & life-threatening behavior ; h5-index 0.0

OBJECTIVE : There is growing interest in the development of composite precision treatment rules (PTRs) to guide the selection of the treatments most likely to be helpful for individual patients. We present here the results of an effort to develop a preliminary PTR for Collaborative Assessment and Management of Suicidality (CAMS) relative to enhanced-care as usual based on secondary analysis of the Operation Worth Living (OWL) randomized controlled trial. The outcome of interest is eliminating suicide ideation (SI) within 3 months of initiating treatment.

METHOD : A state-of-the-art ensemble machine learning method was used to develop the PTR among the n = 148 U.S. Soldiers (predominately male and White, age range 18-48) OWL patients.

RESULTS : We estimated that CAMS was the better treatment for 77.8% of patients and that treatment assignment according to the PTR would result in a 13.6% (95% CI: 0.9%-26.3%) increase in 3-month SI remission compared to random treatment assignment.

CONCLUSIONS : Although promising, results are limited by the small sample size, restrictive baseline assessment, and inability to evaluate effects on suicidal behaviors or disaggregate based on history of suicidal behaviors. Replication is needed in larger samples with comprehensive baseline assessments, longer-term follow-ups, and more extensive outcomes.

Kessler Ronald C, Chalker Samantha A, Luedtke Alex R, Sadikova Ekaterina, Jobes David A

2019-Dec-09

General General

Pathway Tools version 23.0 update: software for pathway/genome informatics and systems biology.

In Briefings in bioinformatics ; h5-index 0.0

MOTIVATION : Biological systems function through dynamic interactions among genes and their products, regulatory circuits and metabolic networks. Our development of the Pathway Tools software was motivated by the need to construct biological knowledge resources that combine these many types of data, and that enable users to find and comprehend data of interest as quickly as possible through query and visualization tools. Further, we sought to support the development of metabolic flux models from pathway databases, and to use pathway information to leverage the interpretation of high-throughput data sets.

RESULTS : In the past 4 years we have enhanced the already extensive Pathway Tools software in several respects. It can now support metabolic-model execution through the Web, it provides a more accurate gap filler for metabolic models; it supports development of models for organism communities distributed across a spatial grid; and model results may be visualized graphically. Pathway Tools supports several new omics-data analysis tools including the Omics Dashboard, multi-pathway diagrams called pathway collages, a pathway-covering algorithm for metabolomics data analysis and an algorithm for generating mechanistic explanations of multi-omics data. We have also improved the core pathway/genome databases management capabilities of the software, providing new multi-organism search tools for organism communities, improved graphics rendering, faster performance and re-designed gene and metabolite pages.

AVAILABILITY : The software is free for academic use; a fee is required for commercial use. See http://pathwaytools.com.

CONTACT : pkarp@ai.sri.com.

SUPPLEMENTARY INFORMATION : Supplementary data are available at Briefings in Bioinformatics online.

Karp Peter D, Midford Peter E, Billington Richard, Kothari Anamika, Krummenacker Markus, Latendresse Mario, Ong Wai Kit, Subhraveti Pallavi, Caspi Ron, Fulcher Carol, Keseler Ingrid M, Paley Suzanne M

2019-Dec-08

Computational genomics, metabolic models, metabolic pathways, systems biology

oncology Oncology

Deep learning of pharmacogenomics resources: moving towards precision oncology.

In Briefings in bioinformatics ; h5-index 0.0

The recent accumulation of cancer genomic data provides an opportunity to understand how a tumor's genomic characteristics can affect its responses to drugs. This field, called pharmacogenomics, is a key area in the development of precision oncology. Deep learning (DL) methodology has emerged as a powerful technique to characterize and learn from rapidly accumulating pharmacogenomics data. We introduce the fundamentals and typical model architectures of DL. We review the use of DL in classification of cancers and cancer subtypes (diagnosis and treatment stratification of patients), prediction of drug response and drug synergy for individual tumors (treatment prioritization for a patient), drug repositioning and discovery and the study of mechanism/mode of action of treatments. For each topic, we summarize current genomics and pharmacogenomics data resources such as pan-cancer genomics data for cancer cell lines (CCLs) and tumors, and systematic pharmacologic screens of CCLs. By revisiting the published literature, including our in-house analyses, we demonstrate the unprecedented capability of DL enabled by rapid accumulation of data resources to decipher complex drug response patterns, thus potentially improving cancer medicine. Overall, this review provides an in-depth summary of state-of-the-art DL methods and up-to-date pharmacogenomics resources and future opportunities and challenges to realize the goal of precision oncology.

Chiu Yu-Chiao, Chen Hung-I Harry, Gorthi Aparna, Mostavi Milad, Zheng Siyuan, Huang Yufei, Chen Yidong

2019-Dec-08

cancer, deep learning, drug discovery, pharmacogenomics, precision oncology

General General

Study on miR-384-5p activates TGF-β signaling pathway to promote neuronal damage in abutment nucleus of rats based on deep learning.

In Artificial intelligence in medicine ; h5-index 34.0

BACKGROUND : Any ailment in our organs can be visualized by using different modality signals and images. Hospitals are encountering a massive influx of large multimodality patient data to be analysed accurately and with context understanding. The deep learning techniques, like convolution neural networks (CNN), long short-term memory (LSTM), autoencoders, deep generative models and deep belief networks have already been applied to efficiently analyse possible large collections of data. Application of these methods to medical signals and images can aid the clinicians in clinical decision making.

PURPOSE : The aim of this study was to explore its potential application mechanism to the abalone basal ganglia neurons in rats based on deep learning.

PATIENTS AND METHODS : Firstly, in the GEO database, we obtained data on rat anesthesia, performing differential analysis, co-expression analysis, and enrichment analysis, and then we received the relevant module genes. Besides, the potential regulation of multi-factors on the module was calculated by hypergeometric test, and a series of ncRNA and TF were identified. Finally, we screened the target genes of anesthetized rats to gain insight into the potential role of anesthesia in rat basal lateral nucleus neurons.

RESULTS : A total of 535 differentially expressed genes in rats were obtained, involving Mafb and Ryr2. These genes are clustered into 17 anesthesia-related expression disorder modules. At the same time, the biological processes favored by the module are regulation of neuron apoptotic process and transforming growth factor beta2 production. Pivot analysis found that 39 ncRNAs and 4 TFs drive anesthesia-related disorders. Finally, the mechanism of action was analyzed and predicted. The module was regulated by Acvr1. We believe that miR-384-5p in anesthetized rats can activate the TGF-beta signaling pathway. Further, it promotes anesthesia and causes exposure to the basal ganglia neuron damage of the amygdala.

CONCLUSION : In this study, the imbalance module was used to explore the multi-factor-mediated anesthesia application mechanism, which provided new methods and ideas for subsequent research. The results suggest that miR-384-5p can promote anesthesia damage to the abalone basal ganglia neurons in rats through a variety of biological processes and signaling pathways. This result lays a solid theoretical foundation for biologists to explore the application mechanism of anesthesiology further.

Wang Zhen, Du Xiaoyan, Yang Yang, Zhang Guoqing

2019-Nov

Anesthesia, Basal lateral nucleus neurons, Dysfunction module, Multifactorial

Public Health Public Health

Classifying cancer pathology reports with hierarchical self-attention networks.

In Artificial intelligence in medicine ; h5-index 34.0

We introduce a deep learning architecture, hierarchical self-attention networks (HiSANs), designed for classifying pathology reports and show how its unique architecture leads to a new state-of-the-art in accuracy, faster training, and clear interpretability. We evaluate performance on a corpus of 374,899 pathology reports obtained from the National Cancer Institute's (NCI) Surveillance, Epidemiology, and End Results (SEER) program. Each pathology report is associated with five clinical classification tasks - site, laterality, behavior, histology, and grade. We compare the performance of the HiSAN against other machine learning and deep learning approaches commonly used on medical text data - Naive Bayes, logistic regression, convolutional neural networks, and hierarchical attention networks (the previous state-of-the-art). We show that HiSANs are superior to other machine learning and deep learning text classifiers in both accuracy and macro F-score across all five classification tasks. Compared to the previous state-of-the-art, hierarchical attention networks, HiSANs not only are an order of magnitude faster to train, but also achieve about 1% better relative accuracy and 5% better relative macro F-score.

Gao Shang, Qiu John X, Alawad Mohammed, Hinkle Jacob D, Schaefferkoetter Noah, Yoon Hong-Jun, Christian Blair, Fearn Paul A, Penberthy Lynne, Wu Xiao-Cheng, Coyle Linda, Tourassi Georgia, Ramanathan Arvind

2019-Nov

Cancer pathology reports, Clinical reports, Deep learning, Natural language processing, Text classification

General General

Compositional model based on factorial evolution for realizing multi-task learning in bacterial virulent protein prediction.

In Artificial intelligence in medicine ; h5-index 34.0

The ability of multitask learning promulgated its sovereignty in the machine learning field with the diversified application including but not limited to bioinformatics and pattern recognition. Bioinformatics provides a wide range of applications for Multitask Learning (MTL) methods. Identification of Bacterial virulent protein is one such application that helps in understanding the virulence mechanism for the design of drug and vaccine. However, the limiting factor in a reliable prediction model is the scarcity of the experimentally verified training data. To deal with, casting the problem in a Multitask Learning scenario, could be beneficial. Reusability of auxiliary data from related multiple domains in the prediction of target domain with limited labeled data is the primary objective of multitask learning model. Due to the amalgamation of multiple related data, it is possible that the probability distribution between the features tends to vary. Therefore, to deal with change amongst the feature distribution, this paper proposes a composite model for multitask learning framework which is based on two principles: discovering the shared parameters for identifying the relationships between tasks and common underlying representation of features amongst the related tasks. Through multi-kernel and factorial evolution, the proposed framework able to discover the shared kernel parameters and latent feature representation that is common amongst the tasks. To examine the benefits of the proposed model, an extensive experiment is performed on the freely available dataset at VirulentPred web server. Based on the results, we found that multitask learning model performs better than the conventional single task model. Additionally, our findings state that if the distribution between the tasks is high, then training the multiple models yield slightly better prediction. However, if the data distribution difference is low, multitask learning significantly outperforms the individual learning.

Singh Deepak, Singh Pradeep, Singh Sisodia Dilip

2019-Nov

Multi-kernel learning, Multifactorial evolutionary algorithm, Multitask learning, Virulent protein

General General

A feature selection and multi-model fusion-based approach of predicting air quality.

In ISA transactions ; h5-index 0.0

With the rapid development of China's industrialization, the air pollution is becoming more and more serious. It is vital for us to predict the air quality for determining the further prevention measures of avoiding the brought disasters. In this paper, we are going to propose an approach of predicting the air quality based on the multiple data features through fusing the multiple machine learning models. The approach takes the meteorological data and air quality data for the past six days as one batch of input (the whole data set is for 46 days) and employs a multi-model fusion to provide an improved 24-hour prediction of PM2.5 pollutant concentration all over Beijing. During the above process, two focal feature groups are composed. The first focal feature group contains the historical meteorological data, while the second group includes the statistical information, the date information and the polynomial variations. Besides the two groups, we complement one million more data items by employing the time sliding means. Among the supplementary data, we select the most critical 500 features with Light Gradient Boosting Machine (LightGBM) model and send the features as the input to Gradient Boosting Decision Tree (GBDT) and LightGBM models. Meanwhile, we screen the most critical 300 features with eXtreme Gradient Boosting (XGBoost) model and send them as the input to the three prediction models. Referring to each of the models, we respectively gain the optimal parameters through grid search methods and then fuse the models' contribution with the linear weighting. The experiments indicate that the proposed approach based on the weighting fusion is better than that provided by a single modeling scheme, and the loss value is 0.4158 under the SMAPE index.

Zhang Ying, Zhang Rongrong, Ma Qunfei, Wang Yanhao, Wang Qingqing, Huang Zihao, Huang Linyan

2019-Dec-02

Air quality prediction, Feature selection, Machine learning, Model fusion

General General

Transparent Classification with Multilayer Logical Perceptrons and Random Binarization

arxiv preprint

Models with transparent inner structure and high classification performance are required to reduce potential risk and provide trust for users in domains like health care, finance, security, etc. However, existing models are hard to simultaneously satisfy the above two properties. In this paper, we propose a new hierarchical rule-based model for classification tasks, named Concept Rule Sets (CRS), which has both a strong expressive ability and a transparent inner structure. To address the challenge of efficiently learning the non-differentiable CRS model, we propose a novel neural network architecture, Multilayer Logical Perceptron (MLLP), which is a continuous version of CRS. Using MLLP and the Random Binarization (RB) method we proposed, we can search the discrete solution of CRS in continuous space using gradient descent and ensure the discrete CRS acts almost the same as the corresponding continuous MLLP. Experiments on 12 public data sets show that CRS outperforms the state-of-the-art approaches and the complexity of the learned CRS is close to the simple decision tree.

Zhuo Wang, Wei Zhang, Ning Liu, Jianyong Wang

2019-12-10

General General

Methods for algorithmic diagnosis of metabolic syndrome.

In Artificial intelligence in medicine ; h5-index 34.0

Metabolic Syndrome (MetS) is associated with the risk of developing chronic disease (atherosclerotic cardiovascular disease, type 2 diabetes, cancers and chronic kidney disease) and has an important role in early prevention. Previous research showed that an artificial neural network (ANN) is a suitable tool for algorithmic MetS diagnostics, that includes solely non-invasive, low-cost and easily-obtainabled (NI&LC&EO) diagnostic methods. This paper considers using four well-known machine learning methods (linear regression, artificial neural network, decision tree and random forest) for MetS predictions and provides their comparison, in order to induce and facilitate development of appropriate medical software by using these methods. Training, validation and testing are conducted on the large dataset that includes 3000 persons. Input vectors are very simple and contain the following parameters: gender, age, body mass index, waist-to-height ratio, systolic and diastolic blood pressures, while the output is MetS diagnosis in true/false form, made in accordance with International Diabetes Federation (IDF). Comparison leads to the conclusion that random forest achieves the highest specificity (SPC=0.9436), sensitivity (SNS=0.9154), positive (PPV=0.9379) and negative (NPV=0.9150) predictive values. Algorithmic diagnosis of MetS could be beneficial in everyday clinical practice since it can easily identify high risk patients.

Vrbaški Dunja, Vrbaški Milan, Kupusinac Aleksandar, Ivanović Darko, Stokić Edita, Ivetić Dragan, Doroslovački Ksenija

2019-Nov

Artificial neural network, Decision tree, Linear regression, Metabolic syndrome, Random forest

General General

Cosine similarity measures of bipolar neutrosophic set for diagnosis of bipolar disorder diseases.

In Artificial intelligence in medicine ; h5-index 34.0

Similarity plays a significant implicit or explicit role in various fields. In some real applications in decision making, similarity may bring counterintuitive outcomes from the decision maker's standpoint. Therefore, in this research, we propose some novel similarity measures for bipolar and interval-valued bipolar neutrosophic set such as the cosine similarity measures and weighted cosine similarity measures. The propositions of these similarity measures are examined, and two multi-attribute decision making techniques are presented based on proposed measures. For verifying the feasibility of proposed measures, two numerical examples are presented in comparison with the related methods for demonstrating the practicality of the proposed method. Finally, we applied the proposed measures of similarity for diagnosing bipolar disorder diseases.

Abdel-Basset Mohamed, Mohamed Mai, Elhoseny Mohamed, Son Le Hoang, Chiclana Francisco, Zaied Abd El-Nasser H

2019-Nov

Bipolar, Bipolar disorder diseases, Cosine similarity measure, Multi-attribute decision making

General General

Automated classification of histopathology images using transfer learning.

In Artificial intelligence in medicine ; h5-index 34.0

Early and accurate diagnosis of diseases can often save lives. Diagnosis of diseases from tissue samples is done manually by pathologists. Diagnostics process is usually time consuming and expensive. Hence, automated analysis of tissue samples from histopathology images has critical importance for early diagnosis and treatment. The computer aided systems can improve the quality of diagnoses and give pathologists a second opinion for critical cases. In this study, a deep learning based transfer learning approach has been proposed to classify histopathology images automatically. Two well-known and current pre-trained convolutional neural network (CNN) models, ResNet-50 and DenseNet-161, have been trained and tested using color and grayscale images. The DenseNet-161 tested on grayscale images and obtained the best classification accuracy of 97.89%. Additionally, ResNet-50 pre-trained model was tested on the color images of the Kimia Path24 dataset and achieved the highest classification accuracy of 98.87%. According to the obtained results, it may be said that the proposed pre-trained models can be used for fast and accurate classification of histopathology images and assist pathologists in their daily clinical tasks.

Talo Muhammed

2019-Nov

CNN, Deep learning, Histopathology, Medical image classification, Transfer learning

Public Health Public Health

A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset.

In Artificial intelligence in medicine ; h5-index 34.0

BACKGROUND AND OBJECTIVE : Cerebral stroke has become a significant global public health issue in recent years. The ideal solution to this concern is to prevent in advance by controlling related metabolic factors. However, it is difficult for medical staff to decide whether special precautions are needed for a potential patient only based on the monitoring of physiological indicators unless they are obviously abnormal. This paper will develop a hybrid machine learning approach to predict cerebral stroke for clinical diagnosis based on the physiological data with incompleteness and class imbalance.

METHODS : Two steps are involved in the whole process. Firstly, random forest regression is adopted to impute missing values before classification. Secondly, an automated hyperparameter optimization(AutoHPO) based on deep neural network(DNN) is applied to stroke prediction on an imbalanced dataset.

RESULTS : The medical dataset contains 43,400 records of potential patients which includes 783 occurrences of stroke. The false negative rate from our prediction approach is only 19.1%, which has reduced by an average of 51.5% in comparison to other traditional approaches. The false positive rate, accuracy and sensitivity predicted by the proposed approach are respectively 33.1, 71.6, and 67.4%.

CONCLUSION : The approach proposed in this paper has effectively reduced the false negative rate with a relatively high overall accuracy, which means a successful decrease in the misdiagnosis rate for stroke prediction. The results are more reliable and valid as the reference in stroke prognosis, and also can be acquired conveniently at a low cost.

Liu Tianyu, Fan Wenhui, Wu Cheng

2019-Nov

AutoHPO, Class imbalance, Clinical decision, Hybrid machine learning, Stroke prediction

General General

Analysis of Self-Assembly Pathways with Unsupervised Machine Learning Algorithms.

In The journal of physical chemistry. B ; h5-index 0.0

Colloidal and nanoparticle systems display a rich and exciting phase behavior including the self-assembly of highly complex crystal structures. Nucleation and growth pathways towards crystallization have been studied both computationally and experimentally, but the mechanisms for the formation of the pre-critical nucleus and consequent crystal growth are yet to be fully understood. Recent advances in the application of machine learning algorithms applied to many-particle systems have led to significant breakthroughs in the ability for high-throughput analysis of phase transitions and the identification of crystal structures. We build upon these techniques to identify and analyze pathways for nucleation and growth in supercooled liquids of colloidal systems modeled with isotropic pair potentials. Our study involves the development of unsupervised machine learning models trained on spherical harmonics based descriptors. These models allow us to determine clusters of local environments that are present prior to and during crystallization. We analyze these environments to identify prevalent motifs and local order within the supercooled liquid prior to formation of the critical nucleus.

Adorf Carl Simon, Moore Timothy C, Melle Yannah J U, Glotzer Sharon C

2019-Dec-08

General General

Deep neural network for automatic characterization of lesions on 68Ga-PSMA-11 PET/CT.

In European journal of nuclear medicine and molecular imaging ; h5-index 66.0

PURPOSE : This study proposes an automated prostate cancer (PC) lesion characterization method based on the deep neural network to determine tumor burden on 68Ga-PSMA-11 PET/CT to potentially facilitate the optimization of PSMA-directed radionuclide therapy.

METHODS : We collected 68Ga-PSMA-11 PET/CT images from 193 patients with metastatic PC at three medical centers. For proof-of-concept, we focused on the detection of pelvis bone and lymph node lesions. A deep neural network (triple-combining 2.5D U-Net) was developed for the automated characterization of these lesions. The proposed method simultaneously extracts features from axial, coronal, and sagittal planes, which mimics the workflow of physicians and reduces computational and memory requirements.

RESULTS : Among all the labeled lesions, the network achieved 99% precision, 99% recall, and an F1 score of 99% on bone lesion detection and 94%, precision 89% recall, and an F1 score of 92% on lymph node lesion detection. The segmentation accuracy is lower than the detection. The performance of the network was correlated with the amount of training data.

CONCLUSION : We developed a deep neural network to characterize automatically the PC lesions on 68Ga-PSMA-11 PET/CT. The preliminary test within the pelvic area confirms the potential of deep learning methods. Increasing the amount of training data should further enhance the performance of the proposed method and may ultimately allow whole-body assessments.

Zhao Yu, Gafita Andrei, Vollnberg Bernd, Tetteh Giles, Haupt Fabian, Afshar-Oromieh Ali, Menze Bjoern, Eiber Matthias, Rominger Axel, Shi Kuangyu

2019-Dec-07

Deep learning, Lesion detection, PET/CT, PSMA, Prostate cancer

General General

Prediction of marbling score and carcass traits in Korean Hanwoo beef cattle using machine learning methods and synthetic minority oversampling technique.

In Meat science ; h5-index 0.0

Pricing of Hanwoo beef in the Korean market is primarily based on meat quality, and particularly on marbling score. The ability to accurately predict marbling score early in the life of an animal is extremely valuable for producers to meet the requirements of their target market, and for genetic selection. A total of 3989 Korean Hanwoo cattle (2108 with 50 k SNP genotypes) and 45 phenotypic features were available for this study. Four machine learning (ML) algorithms were applied to predict six carcass traits and compared against linear regression prediction models. In most scenarios, SMO was the best performing algorithm. The most and least accurately predicted traits were carcass weight and marbling score with correlation of 0.95 and 0.64 respectively. Additionally, the value of using a synthetic minority over-sampling technique (SMOTE) was evaluated and results showed a slight improvement in the prediction error of marbling score. Machine Learning approaches can be useful tools to predict important carcass traits in beef cattle.

Shahinfar Saleh, Al-Mamun Hawlader A, Park Byoungho, Kim Sidong, Gondro Cedric

2019-Nov-12

Carcass traits, GWAS, Genetic algorithm, Hanwoo Beef Cattle, Machine learning, Marbling score, SMOTE

General General

Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning.

In Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology ; h5-index 0.0

BACKGROUND : Deep learning-based auto-segmented contours (DC) aim to alleviate labour intensive contouring of organs at risk (OAR) and clinical target volumes (CTV). Most previous DC validation studies have a limited number of expert observers for comparison and/or use a validation dataset related to the training dataset. We determine if DC models are comparable to Radiation Oncologist (RO) inter-observer variability on an independent dataset.

METHODS : Expert contours (EC) were created by multiple ROs for central nervous system (CNS), head and neck (H&N), and prostate radiotherapy (RT) OARs and CTVs. DCs were generated using deep learning-based auto-segmentation software trained by a single RO on publicly available data. Contours were compared using Dice Similarity Coefficient (DSC) and 95% Hausdorff distance (HD).

RESULTS : Sixty planning CT scans had 2-4 ECs, for a total of 60 CNS, 53 H&N, and 50 prostate RT contour sets. The mean DC and EC contouring times were 0.4 vs 7.7 min for CNS, 0.6 vs 26.6 min for H&N, and 0.4 vs 21.3 min for prostate RT contours. There were minimal differences in DSC and 95% HD involving DCs for OAR comparisons, but more noticeable differences for CTV comparisons.

CONCLUSIONS : The accuracy of DCs trained by a single RO is comparable to expert inter-observer variability for the RT planning contours in this study. Use of deep learning-based auto-segmentation in clinical practice will likely lead to significant benefits to RT planning workflow and resources.

Wong Jordan, Fong Allan, McVicar Nevin, Smith Sally, Giambattista Joshua, Wells Derek, Kolbeck Carter, Giambattista Jonathan, Gondara Lovedeep, Alexander Abraham

2019-Dec-05

Machine learning, Radiotherapy

General General

Brain tumor segmentation approach based on the extreme learning machine and significantly fast and robust fuzzy C-means clustering algorithms running on Raspberry Pi hardware.

In Medical hypotheses ; h5-index 0.0

Automatic decision support systems have gained importance in health sector in recent years. In parallel with recent developments in the fields of artificial intelligence and image processing, embedded systems are also used in decision support systems for tumor diagnosis. Extreme learning machine (ELM), is a recently developed, quick and efficient algorithm which can quickly and flawlessly diagnose tumors using machine learning techniques. Similarly, significantly fast and robust fuzzy C-means clustering algorithm (FRFCM) is a novel and fast algorithm which can display a high performance. In the present study, a brain tumor segmentation approach is proposed based on extreme learning machine and significantly fast and robust fuzzy C-means clustering algorithms (BTS-ELM-FRFCM) running on Raspberry Pi (PRI) hardware. The present study mainly aims to introduce a new segmentation system hardware containing new algorithms and offering a high level of accuracy the health sector. PRI's are useful mobile devices due to their cost-effectiveness and satisfying hardware. 3200 training images were used to train ELM in the present study. 20 pieces of MRI images were used for testing process. Figure of merid (FOM), Jaccard similarity coefficient (JSC) and Dice indexes were used in order to evaluate the performance of the proposed approach. In addition, the proposed method was compared with brain tumor segmentation based on support vector machine (BTS-SVM), brain tumor segmentation based on fuzzy C-means (BTS-FCM) and brain tumor segmentation based on self-organizing maps and k-means (BTS-SOM). The statistical analysis on FOM, JSC and Dice results obtained using four different approaches indicated that BTS-ELM-FRFCM displayed the highest performance. Thus, it can be concluded that the embedded system designed in the present study can perform brain tumor segmentation with a high accuracy rate.

Şişik Fatih, Sert Eser

2019-Nov-18

Brain tumor segmentation, Extreme learning machine, Magnetic resonance imaging, Raspberry Pi, Segmentation, Significantly fast and robust fuzzy C-means clustering

General General

Recent advances on constraint-based models by integrating machine learning.

In Current opinion in biotechnology ; h5-index 0.0

Research that meaningfully integrates constraint-based modeling with machine learning is at its infancy but holds much promise. Here, we consider where machine learning has been implemented within the constraint-based modeling reconstruction framework and highlight the need to develop approaches that can identify meaningful features from large-scale data and connect them to biological mechanisms to establish causality to connect genotype to phenotype. We motivate the construction of iterative integrative schemes where machine learning can fine-tune the input constraints in a constraint-based model or contrarily, constraint-based model simulation results are analyzed by machine learning and reconciled with experimental data. This can iteratively refine a constraint-based model until there is consistency between experimental data, machine learning results, and constraint-based model simulations.

Rana Pratip, Berry Carter, Ghosh Preetam, Fong Stephen S

2019-Dec-05

General General

Publisher Correction: Deep Machine Learning Techniques for the Detection and Classification of Sperm Whale Bioacoustics.

In Scientific reports ; h5-index 158.0

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Bermant Peter C, Bronstein Michael M, Wood Robert J, Gero Shane, Gruber David F

2019-Dec-06

General General

A Machine Learning Approach to Identify a Circulating MicroRNA Signature for Alzheimer Disease.

In The journal of applied laboratory medicine ; h5-index 0.0

BACKGROUND : Accurate diagnosis of Alzheimer disease (AD) involving less invasive molecular procedures and at reasonable cost is an unmet medical need. We identified a serum miRNA signature for AD that is less invasive than a measure in cerebrospinal fluid.

METHODS : From the Oxford Project to Investigate Memory and Aging (OPTIMA) study, 96 serum samples were profiled by a multiplex (>500 analytes) microRNA (miRNA) reverse transcription quantitative PCR analysis, including 51 controls, 32 samples from patients with AD, and 13 samples from patients with mild cognitive impairment (MCI). Clinical diagnosis of a subset of AD and the controls was confirmed by postmortem (PM) histologic examination of brain tissue. In a machine learning approach, the AD and control samples were split 70:30 as the training and test cohorts. A multivariate random forest statistical analysis was applied to construct and test a miRNA signature for AD identification. In addition, the MCI participants were included in the test cohort to assess whether the signature can identify early AD patients.

RESULTS : A 12-miRNA signature for AD identification was constructed in the training cohort, demonstrating 76.0% accuracy in the independent test cohort with 90.0% sensitivity and 66.7% specificity. The signature, however, was not able to identify MCI participants. With a subset of AD and control participants with PM-confirmed diagnosis status, a separate 12-miRNA signature was constructed. Although sample size was limited, the PM-confirmed signature demonstrated improved accuracy of 85.7%, largely owing to improved specificity of 80.0% with comparable sensitivity of 88.9%.

CONCLUSION : Although additional and more diverse cohorts are needed for further clinical validation of the robustness, the miRNA signature appears to be a promising blood test to diagnose AD.

Zhao Xuemei, Kang John, Svetnik Vladimir, Warden Donald, Wilcock Gordon, David Smith A, Savage Mary J, Laterza Omar F

2019-Dec-06

General General

A Statistical-Learning Model for Unplanned 7-Day Readmission in Pediatrics.

In Hospital pediatrics ; h5-index 0.0

OBJECTIVES : The rate of pediatric 7-day unplanned readmissions is often seen as a measure of quality of care, with high rates indicative of the need for improvement of quality of care. In this study, we used machine learning on electronic health records to study predictors of pediatric 7-day readmissions. We ranked predictors by clinical significance, as determined by the magnitude of the least absolute shrinkage and selection operator regression coefficients.

METHODS : Data consisting of 50 241 inpatient and observation encounters at a single tertiary pediatric hospital were retrieved; 50% of these patients' data were used for building a least absolute shrinkage and selection operator regression model, whereas the other half of the data were used for evaluating model performance. The categories of variables included were demographics, social determinants of health, severity of illness and acuity, resource use, diagnoses, medications, psychosocial factors, and other variables such as primary care no show.

RESULTS : Previous hospitalizations and readmissions, medications, multiple comorbidities, longer current and previous lengths of stay, certain diagnoses, and previous emergency department use were the most significant predictors modifying a patient's risk of 7-day pediatric readmission. The model achieved an area under the curve of 0.778 (95% confidence interval 0.763-0.793).

CONCLUSIONS : Predictors such as medications, previous and current health care resource use, history of readmissions, severity of illness and acuity, and certain psychosocial factors modified the risk of unplanned 7-day readmissions. These predictors are mostly unmodifiable, indicating that intervention plans on high-risk patients may be developed through discussions with patients and parents to identify underlying modifiable causal factors of readmissions.

Ehwerhemuepha Louis, Pugh Karen, Grant Alex, Taraman Sharief, Chang Anthony, Rakovski Cyril, Feaster William

2019-Dec-06

General General

6-Hydroxythiobinupharidine Inhibits Migration of LM8 Osteosarcoma Cells by Decreasing Expression of LIM Domain Kinase 1.

In Anticancer research ; h5-index 0.0

BACKGROUND/AIM : Osteosarcoma is the most malignant type of bone tumor. Patients with osteosarcoma metastases have a poorer prognosis than those without metastases. Thus, the prognosis of osteosarcoma patients with metastases must be improved.

MATERIALS AND METHODS : The present study investigated the inhibitory effects of 6-hydroxythiobinupharidine isolated from Nuphar pumilum on migration of LM8 murine osteosarcoma cells by a migration assay and also examined the expression of proteins related to actin dynamics by western blot. The present study also developed an automatic cell counting system using machine learning to count migrated cells by Fiji and Trainable Weka Segmentation.

RESULTS : 6-Hydroxythiobinupharidine inhibited migration of LM8 osteosarcoma cells in a dose-dependent manner, and decreased protein expression of Lin11, Isl-1, and Mec-3 domain kinase 1 (LIMK1) and the levels of phosphorylated Cofilin.

CONCLUSION : 6-Hydroxythiobinupharidine suppressed migration of LM8 osteosarcoma cells by decreasing expression of LIMK1. 6-Hydroxythiobinupharidine could be potentially used as an anti-metastatic compound.

Yoshizawa Masato, Nakamura Seikou, Sugiyama Yuki, Tamai Shiori, Ishida Yukiko, Sueyoshi Mari, Toda Yuki, Hosogi Shigekuni, Yano Yoshitaka, Ashihara Eishi

2019-Dec

6-hydroxythiobinupharidine, Cofilin, LIMK, Osteosarcoma, machine learning, metastasis

General General

Emerging role of eHealth in the identification of very early inflammatory rheumatic diseases.

In Best practice & research. Clinical rheumatology ; h5-index 0.0

Digital health or eHealth technologies, notably pervasive computing, robotics, big-data, wearable devices, machine learning, and artificial intelligence (AI), have opened unprecedented opportunities as to how the diseases are diagnosed and managed with active patient engagement. Patient-related data have provided insights (real world data) into understanding the disease processes. Advanced analytics have refined these insights further to draw dynamic algorithms aiding clinicians in making more accurate diagnosis with the help of machine learning. AI is another tool, which, although is still in the evolution stage, has the potential to help identify early signs even before the clinical features are apparent. The evolving digital developments pose challenges on allowing access to health-related data for further research but, at the same time, protecting each patient's privacy. This review focuses on the recent technological advances and their applications and highlights the immense potential to enable early diagnosis of rheumatological diseases.

Kataria Suchitra, Ravindran Vinod

2019-Aug

Artificial intelligence, Big data, Data analytics, Digital health, Machine learning, Robotics, Wearable devices

Public Health Public Health

Screening PubMed abstracts: is class imbalance always a challenge to machine learning?

In Systematic reviews ; h5-index 0.0

BACKGROUND : The growing number of medical literature and textual data in online repositories led to an exponential increase in the workload of researchers involved in citation screening for systematic reviews. This work aims to combine machine learning techniques and data preprocessing for class imbalance to identify the outperforming strategy to screen articles in PubMed for inclusion in systematic reviews.

METHODS : We trained four binary text classifiers (support vector machines, k-nearest neighbor, random forest, and elastic-net regularized generalized linear models) in combination with four techniques for class imbalance: random undersampling and oversampling with 50:50 and 35:65 positive to negative class ratios and none as a benchmark. We used textual data of 14 systematic reviews as case studies. Difference between cross-validated area under the receiver operating characteristic curve (AUC-ROC) for machine learning techniques with and without preprocessing (delta AUC) was estimated within each systematic review, separately for each classifier. Meta-analytic fixed-effect models were used to pool delta AUCs separately by classifier and strategy.

RESULTS : Cross-validated AUC-ROC for machine learning techniques (excluding k-nearest neighbor) without preprocessing was prevalently above 90%. Except for k-nearest neighbor, machine learning techniques achieved the best improvement in conjunction with random oversampling 50:50 and random undersampling 35:65.

CONCLUSIONS : Resampling techniques slightly improved the performance of the investigated machine learning techniques. From a computational perspective, random undersampling 35:65 may be preferred.

Lanera Corrado, Berchialla Paola, Sharma Abhinav, Minto Clara, Gregori Dario, Baldi Ileana

2019-Dec-06

Classification, Indexed search engine, Machine learning, Text mining, Unbalanced data, systematic review

General General

A computer-aided diagnosing system in the evaluation of thyroid nodules-experience in a specialized thyroid center.

In World journal of surgical oncology ; h5-index 0.0

BACKGROUND : The evaluation of thyroid nodules with ultrasonography has created a large burden for radiologists. Artificial intelligence technology has been rapidly developed in recent years to reduce the cost of labor and improve the differentiation of thyroid malignancies. This study aimed to investigate the diagnostic performance of a novel computer-aided diagnosing system (CADs: S-detect) for the ultrasound (US) interpretation of thyroid nodule subtypes in a specialized thyroid center.

METHODS : Our study prospectively included 180 thyroid nodules that underwent ultrasound interpretation. The CADs and radiologist assessed all nodules. The ultrasonographic features of different subtypes were analyzed, and the diagnostic performances of the CADs and radiologist were compared.

RESULTS : There were seven subtypes of thyroid nodules, among which papillary thyroid cancer (PTC) accounted for 50.6% and follicular thyroid carcinoma (FTC) accounted for 2.2%. Among all thyroid nodules, the CADs presented a higher sensitivity and lower specificity than the radiologist (90.5% vs 81.1%; 41.2% vs 83.5%); the radiologist had a higher accuracy than the CADs (82.2% vs 67.2%) for diagnosing malignant thyroid nodules. The accuracy of the CADs was not as good as that of the radiologist in diagnosing PTCs (70.9% vs 82.1%). The CADs and radiologist presented accuracies of 43.8% and 60.9% in identifying FTCs, respectively.

CONCLUSIONS : The ultrasound CADs presented a higher sensitivity for identifying malignant thyroid nodules than experienced radiologists. The CADs was not as good as experienced radiologists in a specialized thyroid center in identifying PTCs. Radiologists maintained a higher specificity than the CADs for FTC detection.

Xia Shujun, Yao Jiejie, Zhou Wei, Dong Yijie, Xu Shangyan, Zhou Jianqiao, Zhan Weiwei

2019-Dec-06

CADs, Experienced radiologists, Thyroid nodule

oncology Oncology

An artificial intelligence framework for automatic segmentation and volumetry of vestibular schwannomas from contrast-enhanced T1-weighted and high-resolution T2-weighted MRI.

In Journal of neurosurgery ; h5-index 64.0

OBJECTIVE : Automatic segmentation of vestibular schwannomas (VSs) from MRI could significantly improve clinical workflow and assist in patient management. Accurate tumor segmentation and volumetric measurements provide the best indicators to detect subtle VS growth, but current techniques are labor intensive and dedicated software is not readily available within the clinical setting. The authors aim to develop a novel artificial intelligence (AI) framework to be embedded in the clinical routine for automatic delineation and volumetry of VS.

METHODS : Imaging data (contrast-enhanced T1-weighted [ceT1] and high-resolution T2-weighted [hrT2] MR images) from all patients meeting the study's inclusion/exclusion criteria who had a single sporadic VS treated with Gamma Knife stereotactic radiosurgery were used to create a model. The authors developed a novel AI framework based on a 2.5D convolutional neural network (CNN) to exploit the different in-plane and through-plane resolutions encountered in standard clinical imaging protocols. They used a computational attention module to enable the CNN to focus on the small VS target and propose a supervision on the attention map for more accurate segmentation. The manually segmented target tumor volume (also tested for interobserver variability) was used as the ground truth for training and evaluation of the CNN. We quantitatively measured the Dice score, average symmetric surface distance (ASSD), and relative volume error (RVE) of the automatic segmentation results in comparison to manual segmentations to assess the model's accuracy.

RESULTS : Imaging data from all eligible patients (n = 243) were randomly split into 3 nonoverlapping groups for training (n = 177), hyperparameter tuning (n = 20), and testing (n = 46). Dice, ASSD, and RVE scores were measured on the testing set for the respective input data types as follows: ceT1 93.43%, 0.203 mm, 6.96%; hrT2 88.25%, 0.416 mm, 9.77%; combined ceT1/hrT2 93.68%, 0.199 mm, 7.03%. Given a margin of 5% for the Dice score, the automated method was shown to achieve statistically equivalent performance in comparison to an annotator using ceT1 images alone (p = 4e-13) and combined ceT1/hrT2 images (p = 7e-18) as inputs.

CONCLUSIONS : The authors developed a robust AI framework for automatically delineating and calculating VS tumor volume and have achieved excellent results, equivalent to those achieved by an independent human annotator. This promising AI technology has the potential to improve the management of patients with VS and potentially other brain tumors.

Shapey Jonathan, Wang Guotai, Dorent Reuben, Dimitriadis Alexis, Li Wenqi, Paddick Ian, Kitchen Neil, Bisdas Sotirios, Saeed Shakeel R, Ourselin Sebastien, Bradford Robert, Vercauteren Tom

2019-Dec-06

AI = artificial intelligence, ASSD = average symmetric surface distance, CNN = convolutional neural network, DL = deep learning, GK = Gamma Knife, HDL = hardness-weighted Dice loss, MRI, RVE = relative volume error, SRS = stereotactic radiosurgery, SpvA = supervised attention module, VS = vestibular schwannoma, artificial intelligence, ceT1 = contrast-enhanced T1-weighted, convolutional neural network, hrT2 = high-resolution T2-weighted, oncology, segmentation, tumor, vestibular schwannoma

General General

Automated detection of focal cortical dysplasia using a deep convolutional neural network.

In Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society ; h5-index 0.0

Focal cortical dysplasia (FCD) is one of the commonest epileptogenic lesions, and is related to malformations of the cortical development. The findings on magnetic resonance (MR) images are important for the diagnosis and surgical planning of FCD. In this paper, an automated detection technique for FCD is proposed using MR images and deep learning. The input MR image is first preprocessed to correct the bias field, normalize intensities, align with a standard atlas, and strip the non-brain tissues. All cortical patches are then extracted on each axial slice, and these patches are classified into FCD and non-FCD using a deep convolutional neural network (CNN) with five convolutional layers, a max pooling layer, and two fully-connected layers. Finally, the false and missed classifications are corrected in the post-processing stage. The technique is evaluated using images of 10 patients with FCD and 20 controls. The proposed CNN shows a superior performance in classifying cortical image patches compared with multiple CNN architectures. For the system-level evaluation, nine of the ten FCD images are successfully detected, and 85% of the non-FCD images are correctly identified. Overall, this CNN based technique could learn optimal cortical (texture and symmetric) features automatically, and improve the FCD detection.

Wang Huiquan, Ahmed S Nizam, Mandal Mrinal

2019-Nov-13

Computer-aided detection, Convolutional neural network, Deep learning, Focal cortical dysplasia, Magnetic resonance imaging

Public Health Public Health

Mapping Environmental Suitability of Scrub Typhus in Nepal Using MaxEnt and Random Forest Models.

In International journal of environmental research and public health ; h5-index 73.0

Being a globally emerging mite-borne zoonotic disease, scrub typhus is a serious public health concern in Nepal. Mapping environmental suitability and quantifying the human population under risk of the disease is important for prevention and control efforts. In this study, we model and map the environmental suitability of scrub typhus using the ecological niche approach, machine learning modeling techniques, and report locations of scrub typhus along with several climatic, topographic, Normalized Difference Vegetation Index (NDVI), and proximity explanatory variables and estimated population under the risk of disease at a national level. Both MaxEnt and RF technique results reveal robust predictive power with test The area under curve (AUC) and true skill statistics (TSS) of above 0.8 and 0.6, respectively. Spatial prediction reveals that environmentally suitable areas of scrub typhus are widely distributed across the country particularly in the low-land Tarai and less elevated river valleys. We found that areas close to agricultural land with gentle slopes have higher suitability of scrub typhus occurrence. Despite several speculations on the association between scrub typhus and proximity to earthquake epicenters, we did not find a significant role of proximity to earthquake epicenters in the distribution of scrub typhus in Nepal. About 43% of the population living in highly suitable areas for scrub typhus are at higher risk of infection, followed by 29% living in suitable areas of moderate-risk, and about 22% living in moderately suitable areas of lower risk. These findings could be useful in selecting priority areas for surveillance and control strategies effectively.

Acharya Bipin Kumar, Chen Wei, Ruan Zengliang, Pant Gobind Prasad, Yang Yin, Shah Lalan Prasad, Cao Chunxiang, Xu Zhiwei, Dhimal Meghnath, Lin Hualiang

2019-Dec-02

Nepal, machine learning, scrub typhus, suitability mapping

General General

Immunotherapy for hepatocellular carcinoma.

In Cancer letters ; h5-index 85.0

Despite significant research efforts, only a few treatment approaches have been developed for hepatocellular carcinoma (HCC). In recent years, immune checkpoint inhibitors (anti-PD-1, anti-PD-L1, and anti-CTLA-4 antibodies) have exhibited potential therapeutic effects for advanced HCC. With the development of gene-editing technologies, gene-sequencing technologies, big data strategies, and artificial intelligence algorithms, engineered immune cell infusion and personalized cancer vaccine therapy have emerged as important directions for anti-HCC treatment. Combining different immunotherapies or combining immunotherapies with conventional therapeutic approaches may provide synergistic effects and facilitate the development of personalized medicine. In this study, we provide an overview of the liver immunoanatomy, the potential immune mechanisms of HCC, and current (pre)clinical developments in this field.

Zongyi Yin, Xiaowu Li

2019-Dec-04

CAR-T therapy, PD-1, cancer vaccine, immune checkpoint inhibitor, targeting drug

General General

Recognition of big data mixed Raman Spectra based on deep learning with smartphone as Raman Analyzer.

In Electrophoresis ; h5-index 0.0

Raman spectral detection has emerged as a powerful analytical technique due to the advantages of fast acquisition, non-invasion and low cost. The on-site application is highly dependent on Raman automatic analysis algorithm. However, current Raman algorithm research mainly focuses on small sample Raman spectroscopy identification with defects of low accuracy and detection rate. It is also difficult to realize rapid Raman spectroscopy measurement under big data. In this paper, rapid recognition of mixtures in complex environments was realized by establishing a fast Raman analysis model based on deep learning through data training, self-learning, and parameter optimization. The cloud network architecture was proposed to apply deep learning to real-time detection using Smartphone-based Raman devices. This research solves the technical problems about mixture recognition under big data and thus could be used as a new method for fast and field RS detection in complex environments. This article is protected by copyright. All rights reserved.

Liang Jie, Mu Taotao

2019-Dec-07

Cloud platform, Deep learning, Mixture recognition, Raman spectrometer, Recognition algorithm

General General

Machine learning-based prediction of response to growth hormone treatment in Turner syndrome: the LG Growth Study.

In Journal of pediatric endocrinology & metabolism : JPEM ; h5-index 0.0

Background Growth hormone (GH) treatment has become a common practice in Turner syndrome (TS). However, there are only a few studies on the response to GH treatment in TS. The aim of this study is to predict the responsiveness to GH treatment and to suggest a prediction model of height outcome in TS. Methods The clinical parameters of 105 TS patients registered in the LG Growth Study (LGS) were retrospectively reviewed. The prognostic factors for the good responders were identified, and the prediction of height response was investigated by the random forest (RF) method, and also, multiple regression models were applied. Results In the RF method, the most important predictive variable for the increment of height standard deviation score (SDS) during the first year of GH treatment was chronologic age (CA) at start of GH treatment. The RF method also showed that the increment of height SDS during the first year was the most important predictor in the increment of height SDS after 3 years of treatment. In a prediction model by multiple regression, younger CA was the significant predictor of height SDS gain during the first year (32.4% of the variability). After 3 years of treatment, mid-parental height (MPH) and the increment of height SDS during the first year were identified as significant predictors (76.6% of the variability). Conclusions Both the machine learning approach and the multiple regression model revealed that younger CA at the start of GH treatment was the most important factor related to height response in patients with TS.

Jung Mo Kyung, Yu Jeesuk, Lee Ji-Eun, Kim Se Young, Kim Hae Soon, Yoo Eun-Gyong

2019-Dec-07

Turner syndrome, growth hormone, height outcome

General General

One network to solve all ROIs: Deep learning CT for any ROI using differentiated backprojection.

In Medical physics ; h5-index 59.0

PURPOSE : Computed tomography for the reconstruction of region of interest (ROI) has advantages in reducing the x-ray dose and the use of a small detector. However, standard analytic reconstruction methods such as filtered back projection (FBP) suffer from severe cupping artifacts, and existing model-based iterative reconstruction methods require extensive computations. Recently, we proposed a deep neural network to learn the cupping artifacts, but the network was not generalized well for different ROIs due to the singularities in the corrupted images. Therefore, there is an increasing demand for a neural network that works well for any ROI size.

METHOD : Two types of neural networks are designed. The first type learns ROI size-specific cupping artifacts from FBP images, whereas the second type network is for the inversion of the truncated Hilbert transform from the truncated differentiated backprojection (DBP) data. Their generalizabilities for different ROI sizes, pixel sizes, detector pitch and starting angles for a short scan are then investigated.

RESULTS : Experimental results show that the new type of neural networks significantly outperform existing iterative methods for all ROI sizes despite significantly lower runtime complexity. In addition, performance improvement is consistent across different acquisition scenarios.

CONCLUSIONS : Since the proposed method consistently surpasses existing methods, it can be used as a general CT reconstruction engine for many practical applications without compromising possible detector truncation.

Han Yoseob, Ye Jong Chul

2019-Dec

Hilbert transform, deep learning, differentiated backprojection, interior tomography, region of interest (ROI) reconstruction

General General

A two-dimensional feasibility study of deep learning-based feature detection and characterization directly from CT sinograms.

In Medical physics ; h5-index 59.0

Machine Learning, especially deep learning, has been used in typical x-ray computed tomography (CT) applications, including image reconstruction, image enhancement, image domain feature detection and image domain feature characterization. To our knowledge, this is the first study on machine learning for feature detection and analysis directly based on CT projection data. Specifically, we present neural network methods for blood vessel detection and characterization in the sinogram domain avoiding any partial volume, beam hardening, or motion artifacts introduced during reconstruction. First, we estimate sinogram domain vessel maps using a residual encoder-decoder convolutional neural network (REDCNN). Next, we estimate the vessel centerline and we extract the vessel-only sinogram from the original sinogram, eliminating any background information. Finally, we use a fully connected neural network to estimate the vessel lumen cross-sectional area from the vessel-only sinogram. We trained and tested the proposed methods using CatSim simulations, real CT measurements of vessel phantoms, and clinical data from the NIH CT image database. We achieved encouraging initial results showing the feasibility of CT analysis in the sinogram domain. In principle, sinogram domain analysis should be possible for many other and more complicated clinical CT analysis tasks. Further studies are needed for this sinogram domain analysis approach to become practical for clinical applications.

De Man Quinten, Haneda Eri, Claus Bernhard, Fitzgerald Paul, De Man Bruno, Qian Guhan, Shan Hongming, Min James, Sabuncu Mert, Wang Ge

2019-Dec

computed tomography, deep learning, machine learning, sinogram

General General

Application of A KNN-based Similarity Method to Biopharmaceutical Manufacturing.

In Biotechnology progress ; h5-index 0.0

Machine learning based similarity analysis is commonly found in many artificial intelligence applications like the one utilized in e-commerce and digital marketing. In this paper, a kNN-based (k-nearest neighbors) similarity method is proposed for rapid biopharmaceutical process diagnosis and process performance monitoring. Our proposed application measures the spatial distance between batches, identifies the most similar historical batches and ranks them in order of similarity. The proposed method considers the similarity in both multivariate and univariate feature space, and measures batch deviations to a benchmarking batch. The feasibility and effectiveness of the proposed method are tested on a drug manufacturing process at Biogen. This article is protected by copyright. All rights reserved.

Ren Jun, Zhou Roland, Farrow Michael, Peiris Ramila, Alosi Tim, Guenard Rob, Romero-Torres Saly

2019-Dec-07

Batch similarity, KNN, Process evaluation, Quality investigation

General General

Vocal pattern detection of depression among older adults.

In International journal of mental health nursing ; h5-index 0.0

Depression is a serious problem for many older adults but is too often undetected by the person, family or providers. Although vocal patterns have been successfully used to detect and predict depression in adults aged 18 to 65 years, no studies to date have included older adults. The study purpose was to determine whether vocal patterns associated with clinical depression in younger people also signify depression in older adults. An observational, repeated measures design was used to enroll 46 volunteer older adults who completed a semi-structured interview composed the 9-item Patient Health Questionnaire or PHQ-9 depression scale and selected speech measures. Recorded interviews were analysed by machine learning algorithms to evaluate whether vocal patterns may predict presence of depression in older adults. In this study, using the PHQ-9 and a supervised machine learning algorithm accurately predicted high and low depression scores between 86% and 92% of the time. Change in raw PHQ-9 scores between interview cycles was predicted within 1.17 points. These results provide strong and promising evidence that vocal patterns can be used effectively to detect clinical depression in adults who are 65 years and older.

Smith Marianne, Dietrich Bryce Jensen, Bai Er-Wei, Bockholt Henry Jeremy

2019-Dec-06

depression, geriatric psychiatry, health serves for the aged, machine learning, phonetics

General General

Plant miRNA-lncRNA Interaction Prediction with the Ensemble of CNN and IndRNN.

In Interdisciplinary sciences, computational life sciences ; h5-index 0.0

Non-coding RNA (ncRNA) plays an important role in regulating biological activities of animals and plants, and the representative ones are microRNA (miRNA) and long non-coding RNA (lncRNA). Recent research has found that predicting the interaction between miRNA and lncRNA is the primary task for elucidating their functional mechanisms. Due to the small scale of data, a large amount of noise, and the limitations of human factors, the prediction accuracy and reliability of traditional feature-based classification methods are often affected. Besides, the structure of plant ncRNA is complex. This paper proposes an ensemble deep-learning model based on convolutional neural network (CNN) and independently recurrent neural network (IndRNN) for predicting the interaction between miRNA and lncRNA of plants, namely, CIRNN. The model uses CNN to explore the functional features of gene sequences automatically, leverages IndRNN to obtain the representation of sequence features, and learns the dependencies among sequences; thus, it overcomes the inaccuracy caused by human factors in traditional feature engineering. The experiment results show that the proposed model is superior to shallow machine-learning and existing deep-learning models when dealing with large-scale data, especially for the long sequence.

Zhang Peng, Meng Jun, Luan Yushi, Liu Chanjuan

2019-Dec-06

CNN, Ensemble learning, IndRNN, Interaction, Prediction, miRNA–lncRNA

General General

The Reification of Diagnosis in Psychiatry.

In Neurotoxicity research ; h5-index 0.0

Invited commentary on: The neuro-immune and neurotoxic fingerprint of major neuro-cognitive psychosis or deficit schizophrenia: a supervised machine learning study, by Maes et Al.

Stoyanov Drozdstoy

2019-Dec-06

bio-markers, classification, machine learning, mental disorders

General General

Diagnostic and Gradation Model of Osteoporosis Based on Improved Deep U-Net Network.

In Journal of medical systems ; h5-index 48.0

The measurement of bone mineral density for osteoporosis has always been the focus of researchers because it plays an important role in bone disease diagnosis. However, because of X-ray image noise and the large difference between the bone shapes of patients under the condition of low contrast, existing osteoporosis diagnosis algorithms are difficult to obtain satisfactory results. This paper presents an improved osteoporosis diagnosis algorithm based on U-NET network. Firstly, the bone in the original image are marked and used to construct the data set. And then, by normalizing the input of each layer, it can be ensured that the input data distribution of each layer is stable, so that the purpose of accelerated training can be achieved. Finally, the energy function is calculated by combining the value of the softmax prediction class for each pixel on the final feature map with the Cross entropy loss function and all the segmented images are extracted to obtain the diagnostic result. As the experimental results show that the improved U-net can accurately solve the influence of image interference in the process of bone mineral density measurement. The recognition rate of U-net automatic diagnosis method is above 81%, and the diagnosis effect is better than other comparison methods.

Liu Jian, Wang Jian, Ruan Weiwei, Lin Chengshan, Chen Daguo

2019-Dec-07

Bone mineral density, Deep learning, Medical diagnosis, Osteoporosis, U-net model, X-ray image

Public Health Public Health

Predicting Hepatitis B Virus Infection Based on Health Examination Data of Community Population.

In International journal of environmental research and public health ; h5-index 73.0

Despite a decline in the prevalence of hepatitis B in China, the disease burden remains high. Large populations unaware of infection risk often fail to meet the ideal treatment window, resulting in poor prognosis. The purpose of this study was to develop and evaluate models identifying high-risk populations who should be tested for hepatitis B surface antigen. Data came from a large community-based health screening, including 97,173 individuals, with an average age of 54.94. A total of 33 indicators were collected as model predictors, including demographic characteristics, routine blood indicators, and liver function. Borderline-Synthetic minority oversampling technique (SMOTE) was conducted to preprocess the data and then four predictive models, namely, the extreme gradient boosting (XGBoost), random forest (RF), decision tree (DT), and logistic regression (LR) algorithms, were developed. The positive rate of hepatitis B surface antigen (HBsAg) was 8.27%. The area under the receiver operating characteristic curves for XGBoost, RF, DT, and LR models were 0.779, 0.752, 0.619, and 0.742, respectively. The Borderline-SMOTE XGBoost combined model outperformed the other models, which correctly predicted 13,637/19,435 cases (sensitivity 70.8%, specificity 70.1%), and the variable importance plot of XGBoost model indicated that age was of high importance. The prediction model can be used to accurately identify populations at high risk of hepatitis B infection that should adopt timely appropriate medical treatment measures.

Wang Ying, Du Zhicheng, Lawrence Wayne R, Huang Yun, Deng Yu, Hao Yuantao

2019-Dec-02

hepatitis B virus, machine learning, prediction

Radiology Radiology

Deep learning: definition and perspectives for thoracic imaging.

In European radiology ; h5-index 62.0

Relevance and penetration of machine learning in clinical practice is a recent phenomenon with multiple applications being currently under development. Deep learning-and especially convolutional neural networks (CNNs)-is a subset of machine learning, which has recently entered the field of thoracic imaging. The structure of neural networks, organized in multiple layers, allows them to address complex tasks. For several clinical situations, CNNs have demonstrated superior performance as compared with classical machine learning algorithms and in some cases achieved comparable or better performance than clinical experts. Chest radiography, a high-volume procedure, is a natural application domain because of the large amount of stored images and reports facilitating the training of deep learning algorithms. Several algorithms for automated reporting have been developed. The training of deep learning algorithm CT images is more complex due to the dimension, variability, and complexity of the 3D signal. The role of these methods is likely to increase in clinical practice as a complement of the radiologist's expertise. The objective of this review is to provide definitions for understanding the methods and their potential applications for thoracic imaging. KEY POINTS: • Deep learning outperforms other machine learning techniques for number of tasks in radiology. • Convolutional neural network is the most popular deep learning architecture in medical imaging. • Numerous deep learning algorithms are being currently developed; some of them may become part of clinical routine in the near future.

Chassagnon Guillaume, Vakalopolou Maria, Paragios Nikos, Revel Marie-Pierre

2019-Dec-06

Deep learning, Lung, Machine learning, Thorax

Radiology Radiology

A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images.

In European radiology ; h5-index 62.0

OBJECTIVE : To develop a deep learning-based artificial intelligence (AI) scheme for predicting the likelihood of the ground-glass nodule (GGN) detected on CT images being invasive adenocarcinoma (IA) and also compare the accuracy of this AI scheme with that of two radiologists.

METHODS : First, we retrospectively collected 828 histopathologically confirmed GGNs of 644 patients from two centers. Among them, 209 GGNs are confirmed IA and 619 are non-IA, including 409 adenocarcinomas in situ and 210 minimally invasive adenocarcinomas. Second, we applied a series of pre-preprocessing techniques, such as image resampling, rescaling and cropping, and data augmentation, to process original CT images and generate new training and testing images. Third, we built an AI scheme based on a deep convolutional neural network by using a residual learning architecture and batch normalization technique. Finally, we conducted an observer study and compared the prediction performance of the AI scheme with that of two radiologists using an independent dataset with 102 GGNs.

RESULTS : The new AI scheme yielded an area under the receiver operating characteristic curve (AUC) of 0.92 ± 0.03 in classifying between IA and non-IA GGNs, which is equivalent to the senior radiologist's performance (AUC 0.92 ± 0.03) and higher than the score of the junior radiologist (AUC 0.90 ± 0.03). The Kappa value of two sets of subjective prediction scores generated by two radiologists is 0.6.

CONCLUSIONS : The study result demonstrates using an AI scheme to improve the performance in predicting IA, which can help improve the development of a more effective personalized cancer treatment paradigm.

KEY POINTS : • The feasibility of using a deep learning method to predict the likelihood of the ground-glass nodule being invasive adenocarcinoma. • Residual learning-based CNN model improves the performance in classifying between IA and non-IA nodules. • Artificial intelligence (AI) scheme yields higher performance than radiologists in predicting invasive adenocarcinoma.

Gong Jing, Liu Jiyu, Hao Wen, Nie Shengdong, Zheng Bin, Wang Shengping, Peng Weijun

2019-Dec-06

Carcinoma, Computer-assisted image interpretation, Lung neoplasms, Multiple pulmonary nodules, X-Ray computed tomography scanners

General General

Multi-Class Classification of Sleep Apnea/Hypopnea Events Based on Long Short-Term Memory Using a Photoplethysmography Signal.

In Journal of medical systems ; h5-index 48.0

In this study, we proposed a new method for multi-class classification of sleep apnea/hypopnea events based on a long short-term memory (LSTM) using photoplethysmography (PPG) signals. The three-layer LSTM model was used with batch-normalization and dropout to classify the multi-class events including normal, apnea, and hypopnea. The PPG signals, which were measured by the nocturnal polysomnography with 7 h from 82 patients suffered from sleep apnea, were used to model training and evaluation. The performance of the proposed method was evaluated on the training set from 63 patients and test set from 13 patients. The results of the LSTM model showed the following high performances: the positive predictive value of 94.16% for normal, 81.38% for apnea, and 97.92% for hypopnea; sensitivity of 86.03% for normal, 91.24% for apnea, and 99.38% for hypopnea events. The proposed method had especially higher performance of hypopnea classification which had been a drawback of previous studies. Furthermore, it can be applied to a system that can classify sleep apnea/hypopnea and normal events automatically without expert's intervention at home.

Kang Chang-Hoon, Erdenebayar Urtnasan, Park Jong-Uk, Lee Kyoung-Joung

2019-Dec-06

Deep learning, Long short-term memory (LSTM), Multi-class classification, Sleep apnea and hypopnea

Ophthalmology Ophthalmology

Detecting glaucoma based on spectral domain optical coherence tomography imaging of peripapillary retinal nerve fiber layer: a comparison study between hand-crafted features and deep learning model.

In Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie ; h5-index 0.0

PURPOSE : To develop a deep learning (DL) model for automated detection of glaucoma and to compare diagnostic capability against hand-craft features (HCFs) based on spectral domain optical coherence tomography (SD-OCT) peripapillary retinal nerve fiber layer (pRNFL) images.

METHODS : A DL model with pre-trained convolutional neural network (CNN) based was trained using a retrospective training set of 1501 pRNFL OCT images, which included 690 images from 153 glaucoma patients and 811 images from 394 normal subjects. The DL model was further tested in an independent test set of 50 images from 50 glaucoma patients and 52 images from 52 normal subjects. A customized software was used to extract and measure HCFs including pRNFL thickness in average and four different sectors. Area under the receiver operator characteristics (AROC) curves was calculated to compare the diagnostic capability between DL model and hand-crafted pRNFL parameters.

RESULTS : In this study, the DL model achieved an AROC of 0.99 [CI: 0.97 to 1.00] which was significantly larger than the AROC values of all other HCFs (AROCs 0.661 with 95% CI 0.549 to 0.772 for temporal sector, AROCs 0.696 with 95% CI 0.549 to 0.799 for nasal sector, AROCs 0.913 with 95% CI 0.855 to 0.970 for superior sector, AROCs 0.938 with 95% CI 0.894 to 0.982 for inferior sector, and AROCs 0.895 with 95% CI 0.832 to 0.957 for average).

CONCLUSION : Our study demonstrated that DL models based on pre-trained CNN are capable of identifying glaucoma with high sensitivity and specificity based on SD-OCT pRNFL images.

Zheng Ce, Xie Xiaolin, Huang Longtao, Chen Binyao, Yang Jianling, Lu Jiewei, Qiao Tong, Fan Zhun, Zhang Mingzhi

2019-Dec-07

Deep learning, Glaucoma, Peripapillary retinal nerve fiber layer, Spectral domain optical coherence tomography

General General

The clinical feasibility of deep learning-based classification of amyloid PET images in visually equivocal cases.

In European journal of nuclear medicine and molecular imaging ; h5-index 66.0

PURPOSE : Although most deep learning (DL) studies have reported excellent classification accuracy, these studies usually target typical Alzheimer's disease (AD) and normal cognition (NC) for which conventional visual assessment performs well. A clinically relevant issue is the selection of high-risk subjects who need active surveillance among equivocal cases. We validated the clinical feasibility of DL compared with visual rating or quantitative measurement for assessing the diagnosis and prognosis of subjects with equivocal amyloid scans.

METHODS : 18F-florbetaben scans of 430 cases (85 NC, 233 mild cognitive impairment, and 112 AD) were assessed through visual rating-based, quantification-based, and DL-based methods. DL was trained using 280 two-dimensional PET images (80%) and tested by randomly assigning the remaining (70 cases, 20%) cases and a clinical validation set of 54 equivocal cases. In the equivocal cases, we assessed the agreement among the visual rating, quantification, and DL and compared the clinical outcome according to each modality-based amyloid status.

RESULTS : The visual reading was positive in 175 cases, equivocal in 54 cases, and negative in 201 cases. The composite SUVR cutoff value was 1.32 (AUC 0.99). The subject-level performance of DL using the test set was 100%. Among the 54 equivocal cases, 37 cases were classified as positive (Eq(deep+)) by DL, 40 cases were classified by a second-round visual assessment, and 40 cases were classified by quantification. The DL- and quantification-based classifications showed good agreement (83%, κ = 0.59). The composite SUVRs differed between Eq(deep+) (1.47 [0.13]) and Eq(deep-) (1.29 [0.10]; P < 0.001). DL, but not the visual rating, showed a significant difference in the Mini-Mental Status Examination score change during the follow-up between Eq(deep+) (- 4.21 [0.57]) and Eq(deep-) (- 1.74 [0.76]; P = 0.023) (mean duration, 1.76 years).

CONCLUSIONS : In visually equivocal scans, DL was more related to quantification than to visual assessment, and the negative cases selected by DL showed no decline in cognitive outcome. DL is useful for clinical diagnosis and prognosis assessment in subjects with visually equivocal amyloid scans.

Son Hye Joo, Oh Jungsu S, Oh Minyoung, Kim Soo Jong, Lee Jae-Hong, Roh Jee Hoon, Kim Jae Seung

2019-Dec-06

18F-florbetaben PET, Alzheimer’s disease, Amyloid, Deep learning, Equivocal scan

Radiology Radiology

Non-invasive tumor decoding and phenotyping of cerebral gliomas utilizing multiparametric 18F-FET PET-MRI and MR Fingerprinting.

In European journal of nuclear medicine and molecular imaging ; h5-index 66.0

OBJECTIVES : The introduction of the 2016 WHO classification of CNS tumors has made the combined molecular and histopathological characterization of tumors a pivotal part of glioma patient management. Recent publications on radiogenomics-based prediction of the mutational status have demonstrated the predictive potential of imaging-based, non-invasive tissue characterization algorithms. Hence, the aim of this study was to assess the potential of multiparametric 18F-FET PET-MRI including MR fingerprinting accelerated with machine learning and radiomic algorithms to predict tumor grading and mutational status of patients with cerebral gliomas.

MATERIALS AND METHODS : 42 patients with suspected primary brain tumor without prior surgical or systemic treatment or biopsy underwent an 18F-FET PET-MRI examination. To differentiate the mutational status and the WHO grade of the cerebral tumors, support vector machine and random forest were trained with the radiomics signature of the multiparametric PET-MRI data including MR fingerprinting. Surgical sampling served as a gold standard for histopathological reference and assessment of mutational status.

RESULTS : The 5-fold cross-validated area under the curve in predicting the ATRX mutation was 85.1%, MGMT mutation was 75.7%, IDH1 was 88.7%, and 1p19q was 97.8%. The area under the curve of differentiating low-grade glioma vs. high-grade glioma was 85.2%.

CONCLUSION : 18F-FET PET-MRI and MR fingerprinting enable high-quality imaging-based tumor decoding and phenotyping for differentiation of low-grade vs. high-grade gliomas and for prediction of the mutational status of ATRX, IDH1, and 1p19q. These initial results underline the potential of 18F-FET PET-MRI to serve as an alternative to invasive tissue characterization.

Haubold Johannes, Demircioglu Aydin, Gratz Marcel, Glas Martin, Wrede Karsten, Sure Ulrich, Antoch Gerald, Keyvani Kathy, Nittka Mathias, Kannengiesser Stephan, Gulani Vikas, Griswold Mark, Herrmann Ken, Forsting Michael, Nensa Felix, Umutlu Lale

2019-Dec-06

1p19q, ATRX, FET PET, Glioma, IDH1, MR fingerprinting, PET-MRI, Radiomics

General General

[Artificial intelligence in the diagnosis of breast cancer : Yesterday, today and tomorrow].

In Der Radiologe ; h5-index 0.0

BACKGROUND : Artificial intelligence (AI) is increasingly applied in the field of breast imaging.

OBJECTIVES : What are the main areas where AI is applied in breast imaging and what AI and computer-aided diagnosis (CAD) systems are already available?

MATERIALS AND METHODS : Basic literature and vendor-supplied information are screened for relevant information, which is then pooled, structured and discussed from the perspective of breast imaging.

RESULTS : Original CAD systems in mammography date almost 25 years back. They are much more widely applied in the United States than in Europe. The initial CAD systems exhibited limited diagnostic abilities and disproportionally high rates of false positive results. Since 2012, deep learning mechanisms have been applied and expand the application possibilities of AI.

CONCLUSION : To date there is no algorithm that has beyond doubt been proven to outperform double reporting by two certified breast radiologists. AI could, however, in the foreseeable future, take over the following tasks: preselection of abnormal examinations to substantially reduce workload of the radiologists by either excluding normal findings from human review or by replacing the double reader in screening. Furthermore, the establishment of radio-patho-genomic correlations and their translation into clinical practice is hardly conceivable without AI.

Bennani-Baiti B, Baltzer P A T

2019-Dec-06

Breast neoplasms, Computer-aided diagnosis, Early detection of cancer, Mammography, Screening

Radiology Radiology

[A primer on machine learning].

In Der Radiologe ; h5-index 0.0

BACKGROUND : The methods of machine learning and artificial intelligence are slowly but surely being introduced in everyday medical practice. In the future, they will support us in diagnosis and therapy and thus improve treatment for the benefit of the individual patient. It is therefore important to deal with this topic and to develop a basic understanding of it.

OBJECTIVES : This article gives an overview of the exciting and dynamic field of machine learning and serves as an introduction to some methods primarily from the realm of supervised learning. In addition to definitions and simple examples, limitations are discussed.

CONCLUSIONS : The basic principles behind the methods are simple. Nevertheless, due to their high dimensional nature, the factors influencing the results are often difficult or impossible to understand by humans. In order to build confidence in the new technologies and to guarantee their safe application, we need explainable algorithms and prospective effectiveness studies.

Kleesiek Jens, Murray Jacob M, Strack Christian, Kaissis Georgios, Braren Rickmer

2019-Dec-06

Artificial neural networks, Deep learning, Digital literacy, Machine learning, New technologies

General General

Genome optimization for improvement of maize breeding.

In TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik ; h5-index 0.0

We propose a new model to improve maize breeding that incorporates doubled haploid production, genomic selection, and genome optimization. Breeding 4.0 has been considered the next era of plant breeding. It is clear that the Breeding 4.0 era for maize will feature the integration of multi-disciplinary technologies including genomics and phenomics, gene editing and synthetic biology, and Big Data and artificial intelligence. The breeding approach of passively selecting ideal genotypes from designated genetic pools must soon evolve to virtual design of optimized genomes by pyramiding superior alleles using computational simulation. An optimized genome expressing optimal phenotypes, which may never actually be created, can function as a blueprint for breeding programs to use minimal materials and hybridizations to achieve maximum genetic gain. We propose a new breeding pipeline, "genomic design breeding," that incorporates doubled haploid production, genomic selection, and genome optimization and is facilitated by different scales of trait predictions and decision-making models. Successful implementation of the proposed model will facilitate the evolution of maize breeding from "art" to "science" and eventually to "intelligence," in the Breeding 4.0 era.

Jiang Shuqin, Cheng Qian, Yan Jun, Fu Ran, Wang Xiangfeng

2019-Dec-06

General General

A Deep Learning Framework for Design and Analysis of Surgical Bioprosthetic Heart Valves.

In Scientific reports ; h5-index 158.0

Bioprosthetic heart valves (BHVs) are commonly used as heart valve replacements but they are prone to fatigue failure; estimating their remaining life directly from medical images is difficult. Analyzing the valve performance can provide better guidance for personalized valve design. However, such analyses are often computationally intensive. In this work, we introduce the concept of deep learning (DL) based finite element analysis (DLFEA) to learn the deformation biomechanics of bioprosthetic aortic valves directly from simulations. The proposed DL framework can eliminate the time-consuming biomechanics simulations, while predicting valve deformations with the same fidelity. We present statistical results that demonstrate the high performance of the DLFEA framework and the applicability of the framework to predict bioprosthetic aortic valve deformations. With further development, such a tool can provide fast decision support for designing surgical bioprosthetic aortic valves. Ultimately, this framework could be extended to other BHVs and improve patient care.

Balu Aditya, Nallagonda Sahiti, Xu Fei, Krishnamurthy Adarsh, Hsu Ming-Chen, Sarkar Soumik

2019-Dec-06

General General

Ensemble modelling framework for groundwater level prediction in urban areas of India.

In The Science of the total environment ; h5-index 0.0

India is facing the worst water crisis in its history and major Indian cities which accommodate about 50% of its population will be among highly groundwater stressed cities by 2020. In past few decades, the urban groundwater resources declined significantly due to over exploitation, urbanization, population growth and climate change. To understand the role of these variables on groundwater level fluctuation, we developed a machine learning based modelling approach considering singular spectrum analysis (SSA), mutual information theory (MI), genetic algorithm (GA), artificial neural network (ANN) and support vector machine (SVM). The developed approach was used to predict the groundwater levels in Bengaluru, a densely populated city with declining groundwater water resources. The input data which consist of groundwater levels, rainfall, temperature, NOI, SOI, NIÑO3 and monthly population growth rate were pre-processed using mutual information theory, genetic algorithm and lag analysis. Later, the optimized input sets were used in ANN and SVM to predict monthly groundwater level fluctuations. The results suggest that the machine learning based approach with data pre-processing predict groundwater levels accurately (R > 85%). It is also evident from the results that the pre-processing techniques enhance the prediction accuracy and results were improved for 66% of the monitored wells. Analysis of various input parameters suggest, inclusion of population growth rate is positively correlated with decrease in groundwater levels. The developed approach in this study for urban groundwater prediction can be useful particularly in cities where lack of pipeline/sewage/drainage lines leakage data hinders physical based modelling.

Yadav Basant, Gupta Pankaj Kumar, Patidar Nitesh, Himanshu Sushil Kumar

2019-Nov-24

Artificial neural network, Genetic algorithm, Machine learning, Mutual information, Support vector machine, Urbanization

General General

A feasibility study of deep learning for predicting hemodynamics of human thoracic aorta.

In Journal of biomechanics ; h5-index 0.0

Numerical analysis methods including finite element analysis (FEA), computational fluid dynamics (CFD), and fluid-structure interaction (FSI) analysis have been used to study the biomechanics of human tissues and organs, as well as tissue-medical device interactions, and treatment strategies. However, for patient-specific computational analysis, complex procedures are usually required to set-up the models, and long computing time is needed to perform the simulation, preventing fast feedback to clinicians in time-sensitive clinical applications. In this study, by using machine learning techniques, we developed deep neural networks (DNNs) to directly estimate the steady-state distributions of pressure and flow velocity inside the thoracic aorta. After training on hemodynamic data from CFD simulations, the DNNs take as input a shape of the aorta and directly output the hemodynamic distributions in one second. The trained DNNs are capable of predicting the velocity magnitude field with an average error of 1.9608% and the pressure field with an average error of 1.4269%. This study demonstrates the feasibility and great potential of using DNNs as a fast and accurate surrogate model for hemodynamic analysis of large blood vessels.

Liang Liang, Mao Wenbin, Sun Wei

2019-Nov-26

Computational fluid dynamics, Deep neural network, Hemodynamic analysis, Machine learning

General General

Predicting structural class for protein sequences of 40% identity based on features of primary and secondary structure using Random Forest algorithm.

In Computational biology and chemistry ; h5-index 0.0

At present, tertiary structure discovery growth rate is lagging far behind discovery of primary structure. The prediction of protein structural class using Machine Learning techniques can help reduce this gap. The Structural Classification of Protein - Extended (SCOPe 2.07) is latest and largest dataset available at present. The protein sequences with less than 40% identity to each other are used for predicting α, β, α/β and α + β SCOPe classes. The sensitive features are extracted from primary and secondary structure representations of Proteins. Features are extracted experimentally from secondary structure with respect to its frequency, pitch and spatial arrangements. Primary structure based features contain species information for a protein sequence. The species parameters are further validated with uniref100 dataset using TaxId. As it is known, protein tertiary structure is manifestation of function. Functional differences are observed in species. Hence, the species are expected to have strong correlations with structural class, which is discovered in current work. It enhances prediction accuracy by 7%-10%. The subset of SCOPe 2.07 is trained using 65 dimensional feature vector using Random Forest classifier. The test result for the rest of the set gives consistent accuracy of better than 95%. The accuracy achieved on benchmark datasets ASTRAL 1.73, 25PDB and FC699 is better than 86%, 91% and 97% respectively, which is best reported to our knowledge.

Apurva Mehta, Mazumdar Himanshu

2019-Nov-15

Evolutionary, Low identity, Protein secondary structure, Protein structural class, Random Forest, Structural classification of proteins (SCOP)

General General

Characterization of Young and Old Adult Brains: An EEG Functional Connectivity Analysis.

In Neuroscience ; h5-index 0.0

Brain connectivity studies have reported that functional networks change with older age. We aim to (1) investigate whether electroencephalography (EEG) data can be used to distinguish between individual functional networks of young and old adults; and (2) identify the functional connections that contribute to this classification. Two eyes-open resting-state EEG recording sessions with 64 electrodes for each of 22 younger adults (19-37 years) and 22 older adults (63-85 years) were conducted. For each session, imaginary coherence matrices in delta, theta, alpha, beta and gamma bands were computed. A range of machine learning classification methods were utilized to distinguish younger and older adult brains. A support vector machine (SVM) classifier was 93% accurate in classifying the brains by age group. We report decreased functional connectivity with older age in delta, theta, alpha and gamma bands, and increased connectivity with older age in beta band. Most connections involving frontal, temporal, and parietal electrodes, and more than half of connections involving occipital electrodes, showed decreased connectivity with older age. Slightly less than half of the connections involving central electrodes showed increased connectivity with older age. Functional connections showing decreased strength with older age were not significantly different in electrode-to-electrode distance than those that increased with older age. Most of the connections used by the classifier to distinguish participants by age group belonged to the alpha band. Findings suggest a decrease in connectivity in key networks and frequency bands associated with attention and awareness, and an increase in connectivity of the sensorimotor functional networks with aging during a resting state.

Moezzi Bahar, Pratti Latha Madhuri, Hordacre Brenton, Graetz Lynton, Berryman Carolyn, Lavrencic Louise M, Ridding Michael C, Keage Hannah A D, McDonnell Mark D, Goldsworthy Mitchell R

2019-Dec-01

aging, electroencephalography, functional connectivity, machine learning, resting-state

General General

Will artificial intelligence eventually replace psychiatrists?

In The British journal of psychiatry : the journal of mental science ; h5-index 0.0

The dystopian scenario of an 'artificial intelligence takeover' imagines artificial intelligence (AI) becoming the dominant form of intelligence on Earth, rendering humans redundant. As a society we have become increasingly familiar with AI and robots replacing humans in many tasks, certain jobs and even some areas of medicine, but surely this is not the fate of psychiatry?Here a computational neuroscientist (Janaina Mourão-Miranda) and psychiatrist (Justin Taylor Baker) suggest that psychiatry as a profession is relatively safe, whereas psychiatrists Christian Brown and Giles William Story predict that robots will be taking over the asylum.

Brown Christian, Story Giles William, Mourão-Miranda Janaina, Baker Justin Taylor

2019-Dec-06

Artificial intelligence, biomarkers, information technologies, silicon valley, technology

Radiology Radiology

Man or machine? Prospective comparison of the version 2018 EASL, LI-RADS criteria and a radiomics model to diagnose hepatocellular carcinoma.

In Cancer imaging : the official publication of the International Cancer Imaging Society ; h5-index 0.0

BACKGROUND : The Liver Imaging Reporting and Data System (LI-RADS) and European Association for the Study of the Liver (EASL) criteria are widely used for diagnosing hepatocellular carcinoma (HCC). Radiomics allows further quantitative tumor heterogeneity profiling. This study aimed to compare the diagnostic accuracies of the version 2018 (v2018) EASL, LI-RADS criteria and radiomics models for HCC in high-risk patients.

METHODS : Ethical approval by the institutional review board and informed consent were obtained for this study. From July 2015 to September 2018, consecutive high-risk patients were enrolled in our tertiary care hospital and underwent gadoxetic acid-enhanced magnetic resonance (MR) imaging and subsequent hepatic surgery. We constructed a multi-sequence-based three-dimensional whole-tumor radiomics signature by least absolute shrinkage and selection operator model and multivariate logistic regression analysis. The diagnostic accuracies of the radiomics signature was validated in an independent cohort and compared with the EASL and LI-RADS criteria reviewed by two independent radiologists.

RESULTS : Two hundred twenty-nine pathologically confirmed nodules (173 HCCs, mean size: 5.74 ± 3.17 cm) in 211 patients were included. Among them, 201 patients (95%) were infected with hepatitis B virus (HBV). The sensitivity and specificity were 73 and 71% for the radiomics signature, 91 and 71% for the EASL criteria, and 86 and 82% for the LI-RADS criteria, respectively. The areas under the receiver operating characteristic curves (AUCs) of the radiomics signature (0.810), LI-RADS (0.841) and EASL criteria (0.811) were comparable.

CONCLUSIONS : In HBV-predominant high-risk patients, the multi-sequence-based MR radiomics signature, v2018 EASL and LI-RADS criteria demonstrated comparable overall accuracies for HCC.

Jiang Hanyu, Liu Xijiao, Chen Jie, Wei Yi, Lee Jeong Min, Cao Likun, Wu Yuanan, Duan Ting, Li Xin, Ma Ling, Song Bin

2019-Dec-05

Carcinoma, Diagnosis, Gadolinium ethoxybenzyl DTPA, Guideline, Hepatocellular, Machine learning

Public Health Public Health

Epidemiological pathology of Aβ deposition in the ageing brain in CFAS: addition of multiple Aβ-derived measures does not improve dementia assessment using logistic regression and machine learning approaches.

In Acta neuropathologica communications ; h5-index 48.0

Aβ-amyloid deposition is a key feature of Alzheimer's disease, but Consortium to Establish a Registry for Alzheimer's Disease (CERAD) assessment, based on neuritic plaque density, shows a limited relationships to dementia. Thal phase is based on a neuroanatomical hierarchy of Aβ-deposition, and in combination with Braak neurofibrillary tangle staging also allows derivation of primary age-related tauopathy (PART). We sought to determine whether Thal Aβ phase predicts dementia better than CERAD in a population-representative cohort (n = 186) derived from the Cognitive Function and Ageing Study (CFAS). Cerebral amyloid angiopathy (CAA) was quantitied as the number of neuroanatomical areas involved and cases meeting criteria for PART were defined to determine if they are a distinct pathological group within the ageing population. Agreement with the Thal scheme was excellent. In univariate analysis Thal phase performed less well as a predictor of dementia than CERAD, Braak or CAA. Logistic regression, decision tree and linear discriminant analysis were performed for multivariable analysis, with similar results. Thal phase did not provide a better explanation of dementia than CERAD, and there was no additional benefit to including more than one assessment of Aβ in the model. Number of areas involved by CAA was highly correlated with assessment based on a severity score (p < 0.001). The presence of capillary involvement (CAA type I) was associated with higher Thal phase and Braak stage (p < 0.001). CAA was not associated with microinfarcts (p = 0.1). Cases satisfying pathological criteria for PART were present at a frequency of 10.2% but were not older and did not have a higher likelihood of dementia than a comparison group of individuals with similar Braak stage but with more Aβ. They also did not have higher hippocampal-tau stage, although PART was weakly associated with increased presence of thorn-shaped astrocytes (p = 0.048), suggesting common age-related mechanisms. Thal phase is highly applicable in a population-representative setting and allows definition of pathological subgroups, such as PART. Thal phase, plaque density, and extent and type of CAA measure different aspects of Aβ pathology, but addition of more than one Aβ measure does not improve dementia prediction, probably because these variables are highly correlated. Machine learning predictions reveal the importance of combining neuropathological measurements for the assessment of dementia.

Wharton S B, Wang D, Parikh C, Matthews F E, Brayne C, Ince P G

2019-Dec-05

Cardiology Cardiology

Intravascular ultrasound-based machine learning for predicting fractional flow reserve in intermediate coronary artery lesions.

In Atherosclerosis ; h5-index 71.0

BACKGROUND AND AIMS : Intravascular ultrasound (IVUS)-derived morphological criteria are poor predictors of the functional significance of intermediate coronary stenosis. IVUS-based supervised machine learning (ML) algorithms were developed to identify lesions with a fractional flow reserve (FFR) ≤0.80 (vs. >0.80).

METHODS : A total of 1328 patients with 1328 non-left main coronary lesions were randomized into training and test sets in a 4:1 ratio. Masked IVUS images were generated by an automatic segmentation model, and 99 computed IVUS features and six clinical variables (age, gender, body surface area, vessel type, involved segment, and involvement of the proximal left anterior descending artery) were used for ML training with 5-fold cross-validation. Diagnostic performances of the binary classifiers (L2 penalized logistic regression, artificial neural network, random forest, AdaBoost, CatBoost, and support vector machine) for detecting ischemia-producing lesions were evaluated using the non-overlapping test samples.

RESULTS : In the classification of test set lesions into those with an FFR ≤0.80 vs. >0.80, the overall diagnostic accuracies for predicting an FFR ≤0.80 were 82% with L2 penalized logistic regression, 80% with artificial neural network, 83% with random forest, 83% with AdaBoost, 81% with CatBoost, and 81% with support vector machine (AUCs: 0.84-0.87). With exclusion of the 28 lesions with borderline FFR of 0.75-0.80, the overall accuracies for the test set were 86% with L2 penalized logistic regression, 85% with an artificial neural network, 87% with random forest, 87% with AdaBoost, 85% with CatBoost, and 85% with support vector machine.

CONCLUSIONS : The IVUS-based ML algorithms showed good diagnostic performance for identifying ischemia-producing lesions, and may reduce the need for pressure wires.

Lee June-Goo, Ko Jiyuon, Hae Hyeonyong, Kang Soo-Jin, Kang Do-Yoon, Lee Pil Hyung, Ahn Jung-Min, Park Duk-Woo, Lee Seung-Whan, Kim Young-Hak, Lee Cheol Whan, Park Seong-Wook, Park Seung-Jung

2019-Nov-02

Artificial intelligence, Fractional flow reserve, Intravascular ultrasound, Machine learning

General General

Improving reference prioritisation with PICO recognition.

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

BACKGROUND : Machine learning can assist with multiple tasks during systematic reviews to facilitate the rapid retrieval of relevant references during screening and to identify and extract information relevant to the study characteristics, which include the PICO elements of patient/population, intervention, comparator, and outcomes. The latter requires techniques for identifying and categorising fragments of text, known as named entity recognition.

METHODS : A publicly available corpus of PICO annotations on biomedical abstracts is used to train a named entity recognition model, which is implemented as a recurrent neural network. This model is then applied to a separate collection of abstracts for references from systematic reviews within biomedical and health domains. The occurrences of words tagged in the context of specific PICO contexts are used as additional features for a relevancy classification model. Simulations of the machine learning-assisted screening are used to evaluate the work saved by the relevancy model with and without the PICO features. Chi-squared and statistical significance of positive predicted values are used to identify words that are more indicative of relevancy within PICO contexts.

RESULTS : Inclusion of PICO features improves the performance metric on 15 of the 20 collections, with substantial gains on certain systematic reviews. Examples of words whose PICO context are more precise can explain this increase.

CONCLUSIONS : Words within PICO tagged segments in abstracts are predictive features for determining inclusion. Combining PICO annotation model into the relevancy classification pipeline is a promising approach. The annotations may be useful on their own to aid users in pinpointing necessary information for data extraction, or to facilitate semantic search.

Brockmeier Austin J, Ju Meizhi, Przybyła Piotr, Ananiadou Sophia

2019-Dec-05

Active learning, Evidence-based medicine, Logistic regression, Machine learning, Systematic review, Text mining

General General

Functional Neuroimaging in the New Era of Big Data.

In Genomics, proteomics & bioinformatics ; h5-index 0.0

The field of functional neuroimaging has substantially advanced as a big data science in the past decade, thanks to international collaborative projects and community efforts. Here we conducted a literature review on functional neuroimaging, with focus on three general challenges in big data tasks: data collection and sharing, data infrastructure construction, and data analysis methods. The review covers a wide range of literature types including perspectives, database descriptions, methodology developments, and technical details. We show how each of the challenges was proposed and addressed, and how these solutions formed the three core foundations for the functional neuroimaging as a big data science and helped to build the current data-rich and data-driven community. Furthermore, based on our reviews of recent literatures on the incoming challenges and oppurtunies towards future scientific discoveries, we envisioned that the functional neuroimaging community needs advancing from the current foundations to better data integration infrastructure, methodology development toward improved learning capability, and multi-discipline translational research framework for this new era of big data.

Li Xiang, Guo Ning, Li Quanzheng

2019-Dec-03

Big data, Health informatics, Machine learning, Neuroimaging, fMRI

General General

Machine Learning to Detect Alzheimer's Disease from Circulating Non-coding RNAs.

In Genomics, proteomics & bioinformatics ; h5-index 0.0

Blood-borne small non-coding (sncRNAs) are among the prominent candidates for blood-based diagnostic tests. Often, high-throughput approaches are applied to discover biomarker signatures. These have to be validated in larger cohorts and evaluated by adequate statistical learning approaches. Previously, we published high-throughput sequencing based microRNA (miRNA) signatures in Alzheimer's disease (AD) patients in the United States (US) and Germany. Here, we determined abundance levels of 21 known circulating miRNAs in 465 individuals encompassing AD patients and controls by RT-qPCR. We computed models to assess the relation between miRNA expression and phenotypes, gender, age, or disease severity (Mini-Mental State Examination; MMSE). Of the 21 miRNAs, expression levels of 20 miRNAs were consistently de-regulated in the US and German cohorts. 18 miRNAs were significantly correlated with neurodegeneration (Benjamini-Hochberg adjusted P < 0.05) with highest significance for miR-532-5p (Benjamini-Hochberg adjusted P = 4.8×10-30). Machine learning models reached an area under the curve (AUC) value of 87.6% in differentiating AD patients from controls. Further, ten miRNAs were significantly correlated with MMSE, in particular miR-26a/26b-5p (adjusted P = 0.0002). Interestingly, the miRNAs with lower abundance in AD were enriched in monocytes and T-helper cells, while those up-regulated in AD were enriched in serum, exosomes, cytotoxic t-cells, and B-cells. Our study represents the next important step in translational research for a miRNA-based AD test.

Ludwig Nicole, Fehlmann Tobias, Kern Fabian, Gogol Manfred, Maetzler Walter, Deutscher Stephanie, Gurlit Simone, Schulte Claudia, von Thaler Anna-Katharina, Deuschle Christian, Metzger Florian, Berg Daniela, Suenkel Ulrike, Keller Verena, Backes Christina, Lenhof Hans-Peter, Meese Eckart, Keller Andreas

2019-Dec-03

Alzheimer’s disease, Biomarker, Gene regulation, Neurodegeneration, Non-coding RNAs, miRNAs

Surgery Surgery

Filtering maxRatio results with machine learning models increases quantitative PCR accuracy over the fit point method.

In Journal of microbiological methods ; h5-index 0.0

With qPCR reaching thousands of reactions per run, assay validation needs automation. We applied support vector machine to qPCR analysis and we could identify reactions with 100% accuracy, dispensing them from further validation. We achieved a greatly reduced workload that could improve high-throughput qPCR analysis.

Marongiu Luigi, Shain Eric, Shain Kevin, Allgayer Heike

2019-Dec-03

Assay accuracy, High throughput, SVM, Validation, maxRatio, qPCR

Radiology Radiology

A Deep Convolutional Neural Network with Performance Comparable to Radiologists for Differentiating Between Spinal Schwannoma and Meningioma.

In Spine ; h5-index 57.0

STUDY DESIGN : Retrospective analysis of magnetic resonance imaging (MRI) OBJECTIVE.: To evaluate the performance of our convolutional neural network (CNN) in differentiating between spinal schwannoma and meningioma on MRI. We compared the performance of the CNN and that of two expert radiologists.

SUMMARY OF BACKGROUND DATA : Preoperative discrimination between spinal schwannomas and meningiomas is crucial because different surgical procedures are required for their treatment. A deep-learning approach based on CNNs is gaining interest in the medical imaging field.

METHODS : We retrospectively reviewed data from patients with spinal schwannoma and meningioma who had undergone MRI and tumor resection. There were 50 patients with schwannoma and 34 patients with meningioma. Sagittal T2-weighted magnetic resonance imaging (T2WI) and sagittal contrast-enhanced T1-weighted magnetic resonance imaging (T1WI) were used for the CNN training and validation. The deep learning framework Tensorflow were used to construct the CNN architecture. To evaluate the performance of the CNN, we plotted the receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC). We calculated and compared the sensitivity, specificity, and accuracy of the diagnosis by the CNN and two board-certified radiologists.

RESULTS : . The AUC of ROC curves of the CNN based on T2WI and contrast-enhanced T1WI were 0.876 and 0.870, respectively. The sensitivity of the CNN based on T2WI was 78%; 100% for radiologist 1; and 95% for radiologist 2. The specificity was 82%, 26%, and 42%, respectively. The accuracy was 80%, 69%, and 73%, respectively. By contrast, the sensitivity of the CNN based on contrast-enhanced T1WI was 85%; 100% for radiologist 1; and 96% for radiologist 2. The specificity was 75%, 56, and 58%, respectively. The accuracy was 81, 82, and 81%, respectively.

CONCLUSIONS : We have successfully differentiated spinal schwannomas and meningiomas using the CNN with high diagnostic accuracy comparable to that of experienced radiologists.

LEVEL OF EVIDENCE : 4.

Maki Satoshi, Furuya Takeo, Horikoshi Takuro, Yokota Hajime, Mori Yasukuni, Ota Joji, Kawasaki Yohei, Miyamoto Takuya, Norimoto Masaki, Okimatsu Sho, Shiga Yasuhiro, Inage Kazuhide, Orita Sumihisa, Takahashi Hiroshi, Suyari Hiroki, Uno Takashi, Ohtori Seiji

2019-Dec-05

General General

Machine Learning and Scaling Laws for Prediction of Accurate Adsorption Energy.

In The journal of physical chemistry. A ; h5-index 0.0

Finding the ``ideal" catalyst is a matter of great interest in the communities of chemists and material scientists, partly because of its wide spectrum of industrial applications. Information regarding a physical parameter termed ``adsorption energy", which dictates the degrees of adhesion of an adsorbate on a substrate is a primary requirement in selecting the catalyst for catalytic reactions. Both experiments and \textit{in-silico} modelling are extensively being used in estimating the adsorption energies, both of which are \textit{Edisonian} approach and demands plenty of resources and are time-consuming. In this paper, employing a data-mining approach, we predict the adsorption energies of mono-atomic and di-atomic gases on the surfaces of many transition metals (TMs) in no times. With less than a set of 10 simple atomic features, our predictions of the adsorption energies are within a root-mean-squared-error (RMSE) of 0.4 eV with the quantum many-body perturbation theory estimates, a computationally expensive method with a good experimental agreement. Based on the important features obtained from machine learning models, we construct a set of mathematical equation using compressed sensing technique to calculate adsorption energy. We also shows that the RMSE can be further minimized up to 0.10 eV by using the pre-computed adsorption energies obtained with conventional exchange and correlation (XC) functional by a new set of scaling relations.

Nayak Sanjay, Bhattacharjee Satadeep, Choi Jung-Hae, Lee Seung-Cheol

2019-Dec-06

General General

DeepScaffold: A Comprehensive Tool for Scaffold-based De Novo Drug Discovery Using Deep Learning.

In Journal of chemical information and modeling ; h5-index 0.0

The ultimate goal of drug design is to find novel compounds with desirable pharmacological properties. Designing molecules retaining particular scaffolds as their core structures is an efficient way to obtain potential drug candidates. We propose a scaffold-based molecular generative model for drug discovery, which performs molecule generation based on a wide spectrum of scaffold definitions, including Bemis-Murko (BM) scaffolds, cyclic skeletons, and scaffolds with specifications on side-chain properties. The model can generalize the learned chemical rules of adding atoms and bonds to a given scaffold. The generated compounds were evaluated by molecular docking in DRD2 targets, and the results demonstrated that this approach can be effectively applied to solve several drug design problems, including the generation of compounds containing a given scaffold and de novo drug design of potential drug candidates with specific docking scores.

Li Yibo, Hu Jianxing, Wang Yanxing, Zhang Liang-Ren, Zhou Jielong, Liu Zhen-Ming

2019-Dec-06

General General

Predicting Breast Cancer by Paper Spray Ion Mobility Spectrometry Mass Spectrometry and Machine Learning.

In Analytical chemistry ; h5-index 0.0

Paper spray ionization has been used as a fast sampling/ionization method for the direct mass spectrometric analysis of biological samples at ambient conditions. Here, we demonstrated that by utilizing paper spray ionization mass spectrometry (PSI-MS) coupled with field asymmetric waveform ion mobility spectrometry (FAIMS), predictive metabolic and lipidomic profiles of routine breast core needle biopsies could be obtained effectively. By the combination of machine learning and pathological examination reports, we developed a classification model, which has an overall accuracy of 87.5% for an in-stantaneous differentiation between cancerous and non-cancerous breast tissues utilizing metabolic and lipidomic profiles. Our results suggested that PSI-FAIMS-MS is a powerful approach for rapid breast cancer diagnosis based on altered meta-bolic and lipidomic profiles.

Huang Ying-Chen, Chung Hsin-Hsiang, Dutkiewicz Ewelina, Chen Chih-Lin, Hsieh Hua-Yi, Chen Bo-Rong, Wang Ming-Yang, Hsu Cheng-Chih

2019-Dec-06

General General

The power and potential of integrated diagnostics in acute myeloid leukaemia.

In British journal of haematology ; h5-index 64.0

The field of acute myeloid leukaemia (AML) diagnostics, initially based solely on morphological assessment, has integrated more and more disciplines. Today, state-of-the-art AML diagnostics relies on cytomorphology, cytochemistry, immunophenotyping, cytogenetics and molecular genetics. Only the integration of all of these methods allows for a comprehensive and complementary characterisation of each case, which is prerequisite for optimal AML diagnosis and management. Here, we will review why multidisciplinary diagnostics is mandatory today and will gain even more importance in the future, especially in the context of precision medicine. We will discuss ideas and strategies that are likely to shape and improve multidisciplinary diagnostics in AML and may even overcome some of today's gold standards. This includes recent technical advances that provide genome-wide molecular insights. The enormous amount of data obtained by these latter techniques represents a great challenge, but also a unique chance. We will reflect on how this increase in knowledge can be incorporated into the routine to pave the way for personalised medicine in AML.

Haferlach Torsten, Schmidts Ines

2019-Dec-06

AML diagnosis and management, acute myeloid leukaemia, artificial intelligence, multidisciplinary diagnostics, precision medicine

oncology Oncology

Technical Note: A Feasibility Study on Deep Learning-Based Radiotherapy Dose Calculation.

In Medical physics ; h5-index 59.0

PURPOSE : Various dose calculation algorithms are available for radiation therapy for cancer patients. However, these algorithms are faced with the tradeoff between efficiency and accuracy. The fast algorithms are generally less accurate, while the accurate dose engines are often time consuming. In this work, we try to resolve this dilemma by exploring deep learning (DL) for dose calculation.

METHODS : We developed a new radiotherapy dose calculation engine based on a modified Hierarchically Densely Connected U-net (HD U-net) model and tested its feasibility with prostate intensity-modulated radiation therapy (IMRT) cases. Mapping from an IMRT fluence map domain to a 3D dose domain requires a deep neural network of complicated architecture and a huge training dataset. To solve this problem, we first project the fluence maps to the dose domain using a broad beam ray-tracing algorithm, and then we use the HD U-net to map the ray-tracing dose distribution into an accurate dose distribution calculated using a collapsed cone convolution/superposition (CS) algorithm. The model is trained on 70 patients with 5-fold cross validation, and tested on a separate 8 patients.

RESULTS : It takes about one second to compute a 3D dose distribution for a typical 7-field prostate IMRT plan, which can be further reduced to achieve real-time dose calculation by optimizing the network. The average Gamma passing rate between DL and CS dose distributions for the 8 test patients are 98.5% (±1.6%) at 1mm/1% and 99.9% (±0.1%) at 2mm/2%. For comparison of various clinical evaluation criteria (dose-volume points) for IMRT plans between two dose distributions, the average difference for dose criteria is less than 0.25 Gy while for volume criteria is less than 0.16%, showing that the DL dose distributions are clinically identical to the CS dose distributions.

CONCLUSIONS : We have shown the feasibility of using DL for calculating radiotherapy dose distribution with high accuracy and efficiency.

Xing Yixun, Nguyen Dan, Lu Weiguo, Yang Ming, Jiang Steve

2019-Dec-06

Deep learning, Dose calculation, Radiotherapy

General General

SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells.

In Bioinformatics (Oxford, England) ; h5-index 0.0

MOTIVATION : Spatial transcriptomics technology is increasingly being applied because it enables the measurement of spatial gene expression in an intact tissue along with imaging morphology of the same tissue. However, current analysis methods for spatial transcriptomics data do not use image pixel information, thus missing the quantitative links between gene expression and tissue morphology.

RESULTS : We developed a user-friendly deep learning software, SpaCell, to integrate millions of pixel intensity values with thousands of gene expression measurements from spatially-barcoded spots in a tissue. We show the integration approach outperforms the use of gene-count data alone or imaging data alone to build deep learning models to identify cell types or predict labels of tissue images with high resolution and accuracy.

AVAILABILITY : The SpaCell package is open source under a MIT license and it is available at https://github.com/BiomedicalMachineLearning/SpaCell.

SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.

Tan Xiao, Su Andrew, Tran Minh, Nguyen Quan

2019-Dec-06

Internal Medicine Internal Medicine

Deep Learning for Automated Measurement of Hemorrhage and Perihematomal Edema in Supratentorial Intracerebral Hemorrhage.

In Stroke ; h5-index 83.0

Background and Purpose- Volumes of hemorrhage and perihematomal edema (PHE) are well-established biomarkers of primary and secondary injury, respectively, in spontaneous intracerebral hemorrhage. An automated imaging pipeline capable of accurately and rapidly quantifying these biomarkers would facilitate large cohort studies evaluating underlying mechanisms of injury. Methods- Regions of hemorrhage and PHE were manually delineated on computed tomography scans of patients enrolled in 2 intracerebral hemorrhage studies. Manual ground-truth masks from the first cohort were used to train a fully convolutional neural network to segment images into hemorrhage and PHE. The primary outcome was automated-versus-human concordance in hemorrhage and PHE volumes. The secondary outcome was voxel-by-voxel overlap of segmentations, quantified by the Dice similarity coefficient (DSC). Algorithm performance was validated on 84 scans from the second study. Results- Two hundred twenty-four scans from 124 patients with supratentorial intracerebral hemorrhage were used for algorithm derivation. Median volumes were 18 mL (interquartile range, 8-43) for hemorrhage and 12 mL (interquartile range, 5-30) for PHE. Concordance was excellent (0.96) for automated quantification of hemorrhage and good (0.81) for PHE, with DSC of 0.90 (interquartile range, 0.85-0.93) and 0.54 (0.39-0.65), respectively. External validation confirmed algorithm accuracy for hemorrhage (concordance 0.98, DSC 0.90) and PHE (concordance 0.90, DSC 0.55). This was comparable with the consistency observed between 2 human raters (DSC 0.90 for hemorrhage, 0.57 for PHE). Conclusions- We have developed a deep learning-based imaging algorithm capable of accurately measuring hemorrhage and PHE volumes. Rapid and consistent automated biomarker quantification may accelerate powerful and precise studies of disease biology in large cohorts of intracerebral hemorrhage patients.

Dhar Rajat, Falcone Guido J, Chen Yasheng, Hamzehloo Ali, Kirsch Elayna P, Noche Rommell B, Roth Kilian, Acosta Julian, Ruiz Andres, Phuah Chia-Ling, Woo Daniel, Gill Thomas M, Sheth Kevin N, Lee Jin-Moo

2019-Dec-06

biology, biomarkers, brain edema, cerebral hemorrhage, deep learning

General General

Support system of cystoscopic diagnosis for bladder cancer based on artificial intelligence.

In Journal of endourology ; h5-index 0.0

Introduction Non-muscle-invasive bladder cancer has a relatively high postoperative recurrence rate despite the implementation of conventional treatment methods. Cystoscopy is essential for diagnosing and monitoring bladder cancer, but lesions are overlooked while using white-light imaging. Using cystoscopy, tumors with a small diameter; flat tumors, such as carcinoma in situ; and the extent of flat lesions associated with the elevated lesions are difficult to identify. In addition, the accuracy of diagnosis and treatment using cystoscopy varies according to the skill and experience of physicians. Therefore, to improve the quality of bladder cancer diagnosis, we aimed to support the cystoscopic diagnosis of bladder cancer using artificial intelligence (AI). Materials/Methods: A total of 2,102 cystoscopic images, consisting of 1,671 images of normal tissue and 431 images of tumor lesions, were used to create a dataset with an 8:2 ratio of training and test images. We constructed a tumor classifier based on a convolutional neural network (CNN). The performance of the trained classifier was evaluated using test data. True positive rate and false positive rate were plotted when the threshold was changed as the receiver operating characteristic (ROC) curve. Results In the test data (tumor image: 87, normal image: 335), 78 images were true positive, 315 true negative, 20 false positive, and 9 false negative. The area under the ROC curve was 0.98, with a maximum Youden-index of 0.837, sensitivity of 89.7%, and specificity of 94.0%. Conclusion By objectively evaluating the cystoscopic image with CNN, it was possible to classify the image, including tumor lesions and normality. The objective evaluation of cystoscopic images using AI is expected to contribute to improvement in the accuracy of the diagnosis and treatment of bladder cancer.

Ikeda Atsushi, Nosato Hirokazu, Kochi Yuta, Kojima Takahiro, Kawai Koji, Sakanashi Hidenori, Murakawa Masahiro, Nishiyama Hiroyuki

2019-Dec-06

General General

PelagiCam: a novel underwater imaging system with computer vision for semi-automated monitoring of mobile marine fauna at offshore structures.

In Environmental monitoring and assessment ; h5-index 0.0

Engineered structures in the open ocean are becoming more frequent with the expansion of the marine renewable energy industry and offshore marine aquaculture. Floating engineered structures function as artificial patch reefs providing novel and relatively stable habitat structure not otherwise available in the pelagic water column. The enhanced physical structure can increase local biodiversity and benefit fisheries yet can also facilitate the spread of invasive species. Clear evidence of any ecological consequences will inform the design and placement of structures to either minimise negative impacts or enhance ecosystem restoration. The development of rapid, cost-effective and reliable remote underwater monitoring methods is crucial to supporting evidence-based decision-making by planning authorities and developers when assessing environmental risks and benefits of offshore structures. A novel, un-baited midwater video system, PelagiCam, with motion-detection software (MotionMeerkat) for semi-automated monitoring of mobile marine fauna, was developed and tested on the UK's largest offshore rope-cultured mussel farm in Lyme Bay, southwest England. PelagiCam recorded Atlantic horse mackerel (Trachurus trachurus), garfish (Belone belone) and two species of jellyfish (Chrysaora hysoscella and Rhizostoma pulmo) in open water close to the floating farm structure. The software successfully distinguished video frames where fishes were present versus absent. The PelagiCam system provides a cost-effective remote monitoring tool to streamline biological data acquisition in impact assessments of offshore floating structures. With the rise of sophisticated artificial intelligence for object recognition, the integration of computer vision techniques should receive more attention in marine ecology and has great potential to revolutionise marine biological monitoring.

Sheehan Emma V, Bridger Danielle, Nancollas Sarah J, Pittman Simon J

2019-Dec-05

Monitoring, aquaculture, ecosystem function, motion detection, mussel farm, pelagic, video analysis

Surgery Surgery

Inter-foetus Membrane Segmentation for TTTS Using Adversarial Networks.

In Annals of biomedical engineering ; h5-index 52.0

Twin-to-Twin Transfusion Syndrome is commonly treated with minimally invasive laser surgery in fetoscopy. The inter-foetal membrane is used as a reference to find abnormal anastomoses. Membrane identification is a challenging task due to small field of view of the camera, presence of amniotic liquid, foetus movement, illumination changes and noise. This paper aims at providing automatic and fast membrane segmentation in fetoscopic images. We implemented an adversarial network consisting of two Fully-Convolutional Neural Networks. The former (the segmentor) is a segmentation network inspired by U-Net and integrated with residual blocks, whereas the latter acts as critic and is made only of the encoding path of the segmentor. A dataset of 900 images acquired in 6 surgical cases was collected and labelled to validate the proposed approach. The adversarial networks achieved a median Dice similarity coefficient of 91.91% with Inter-Quartile Range (IQR) of 4.63%, overcoming approaches based on U-Net (82.98%-IQR: 14.41%) and U-Net with residual blocks (86.13%-IQR: 13.63%). Results proved that the proposed architecture could be a valuable and robust solution to assist surgeons in providing membrane identification while performing fetoscopic surgery.

Casella Alessandro, Moccia Sara, Frontoni Emanuele, Paladini Dario, De Momi Elena, Mattos Leonardo S

2019-Dec-05

Adversarial networks, Deep learning, Fetoscopy, Intraoperative-image segmentation

Internal Medicine Internal Medicine

Machine learning distilled metabolite biomarkers for early stage renal injury.

In Metabolomics : Official journal of the Metabolomic Society ; h5-index 0.0

INTRODUCTION : With chronic kidney disease (CKD), kidney becomes damaged overtime and fails to clean blood. Around 15% of US adults have CKD and nine in ten adults with CKD do not know they have it.

OBJECTIVE : Early prediction and accurate monitoring of CKD may improve care and decrease the frequent progression to end-stage renal disease. There is an urgent demand to discover specific biomarkers that allow for monitoring of early-stage CKD, and response to treatment.

METHOD : To discover such biomarkers, shotgun high throughput was applied to the detection of serum metabolites biomarker discovery for early stages of CKD from 703 participants. Ultra performance liquid chromatography coupled with high-definition mass spectrometry (UPLC-HDMS)-based metabolomics was used for the determination of 703 fasting serum samples from five stages of CKD patients and age-matched healthy controls.

RESULTS AND CONCLUSION : We discovered a set of metabolite biomarkers using a series of classic and neural network based machine learning techniques. This set of metabolites can separate early CKD stage patents from normal subjects with high accuracy. Our study illustrates the power of machine learning methods in metabolite biomarker study.

Guo Yan, Yu Hui, Chen Danqian, Zhao Ying-Yong

2019-Dec-05

Chronic kidney disease, Deep learning, Glomerular filtration rate, Machine learning, Metabolite

oncology Oncology

Radiomics of 18F-FDG PET/CT images predicts clinical benefit of advanced NSCLC patients to checkpoint blockade immunotherapy.

In European journal of nuclear medicine and molecular imaging ; h5-index 66.0

INTRODUCTION : Immunotherapy has improved outcomes for patients with non-small cell lung cancer (NSCLC), yet durable clinical benefit (DCB) is experienced in only a fraction of patients. Here, we test the hypothesis that radiomics features from baseline pretreatment 18F-FDG PET/CT scans can predict clinical outcomes of NSCLC patients treated with checkpoint blockade immunotherapy.

METHODS : This study included 194 patients with histologically confirmed stage IIIB-IV NSCLC with pretreatment PET/CT images. Radiomics features were extracted from PET, CT, and PET+CT fusion images based on minimum Kullback-Leibler divergence (KLD) criteria. The radiomics features from 99 retrospective patients were used to train a multiparametric radiomics signature (mpRS) to predict DCB using an improved least absolute shrinkage and selection operator (LASSO) method, which was subsequently validated in both retrospective (N = 47) and prospective test cohorts (N = 48). Using these cohorts, the mpRS was also used to predict progression-free survival (PFS) and overall survival (OS) by training nomogram models using multivariable Cox regression analyses with additional clinical characteristics incorporated.

RESULTS : The mpRS could predict patients who will receive DCB, with areas under receiver operating characteristic curves (AUCs) of 0.86 (95%CI 0.79-0.94), 0.83 (95%CI 0.71-0.94), and 0.81 (95%CI 0.68-0.92) in the training, retrospective test, and prospective test cohorts, respectively. In the same three cohorts, respectively, nomogram models achieved C-indices of 0.74 (95%CI 0.68-0.80), 0.74 (95%CI 0.66-0.82), and 0.77 (95%CI 0.69-0.84) to predict PFS and C-indices of 0.83 (95%CI 0.77-0.88), 0.83 (95%CI 0.71-0.94), and 0.80 (95%CI 0.69-0.91) to predict OS.

CONCLUSION : PET/CT-based signature can be used prior to initiation of immunotherapy to identify NSCLC patients most likely to benefit from immunotherapy. As such, these data may be leveraged to improve more precise and individualized decision support in the treatment of patients with advanced NSCLC.

Mu Wei, Tunali Ilke, Gray Jhanelle E, Qi Jin, Schabath Matthew B, Gillies Robert J

2019-Dec-05

Immunotherapy, Machine learning, Non-small cell lung cancer (NSCLC), PET/CT, Radiomics

General General

Binary logistic regression modeling with TensorFlow™.

In Annals of translational medicine ; h5-index 0.0

Logistic regression model is one of the most widely used modeling techniques in clinical medicine, owing to the widely available statistical packages for its implementation, and the ease of interpretation. However, logistic model training requires strict assumptions (such as additive and linearity) to be met and these assumptions may not hold true in real world. Thus, clinical investigators need to master some advanced model training methods that can predict more accurately. TensorFlow™ is a popular tool in training machine learning models such as supervised, unsupervised and reinforcement learning methods. Thus, it is important to learn TensorFlow™ in the era of big data. Since most clinical investigators are familiar with the logistic regression model, this article provides a step-by-step tutorial on how to train a logistic regression model in TensorFlow™, with the primary purpose to illustrate how the TensorFlow™ works. We first need to construct a graph with tensors and operations, then the graph is run in a session. Finally, we display the graph and summary statistics in the TensorBoard, which shows the changes of the accuracy and loss value across the training iterations.

Zhang Zhongheng, Mo Lei, Huang Chen, Xu Ping

2019-Oct

Logistic regression, TensorFlow, gradient descent

General General

Machine Learning For Tuning, Selection, And Ensemble Of Multiple Risk Scores For Predicting Type 2 Diabetes.

In Risk management and healthcare policy ; h5-index 0.0

Background : This study proposes the use of machine learning algorithms to improve the accuracy of type 2 diabetes predictions using non-invasive risk score systems.

Methods : We evaluated and compared the prediction accuracies of existing non-invasive risk score systems using the data from the REACTION study (Risk Evaluation of Cancers in Chinese Diabetic Individuals: A Longitudinal Study). Two simple risk scores were established on the bases of logistic regression. Machine learning techniques (ensemble methods) were used to improve prediction accuracies by combining the individual score systems.

Results : Existing score systems from Western populations performed worse than the scores from Eastern populations in general. The two newly established score systems performed better than most existing scores systems but a little worse than the Chinese score system. Using ensemble methods with model selection algorithms yielded better prediction accuracy than all the simple score systems.

Conclusion : Our proposed machine learning methods can be used to improve the accuracy of screening the undiagnosed type 2 diabetes and identifying the high-risk patients.

Liu Yujia, Ye Shangyuan, Xiao Xianchao, Sun Chenglin, Wang Gang, Wang Guixia, Zhang Bo

2019

machine learning, prediction, risk score, stacking, type 2 diabetes, voting

General General

Semi-automated open water iceberg detection from Landsat applied to Disko Bay, West Greenland.

In The journal of glaciology ; h5-index 0.0

Changes in Greenland's marine-terminating outlet glaciers have led to changes in the flux of icebergs into Greenland's coastal waters, yet icebergs remain a relatively understudied component of the ice-ocean system. We developed a simple iceberg delineation algorithm for Landsat imagery. A machine learning-based cloud mask incorporated into the algorithm enables us to extract iceberg size distributions from open water even in partially cloudy scenes. We applied the algorithm to the Landsat archive covering Disko Bay, West Greenland, to derive a time series of iceberg size distributions from 2000-02 and 2013-15. The time series captures a change in iceberg size distributions, which we interpret as a result of changes in the calving regime of the parent glacier, Sermeq Kujalleq (Jakobshavn Isbræ). The change in calving style associated with the disintegration and disappearance of Sermeq Kujalleq's floating ice tongue resulted in the production of more small icebergs. The increased number of small icebergs resulted in increasingly negative power law slopes fit to iceberg size distributions in Disko Bay, suggesting that iceberg size distribution time series provide useful insights into changes in calving dynamics.

Scheick Jessica, Enderlin Ellyn M, Hamilton Gordon

2019-Jun

calving, ice-ocean interactions, icebergs, remote sensing

General General

Molecular profiling of single neurons of known identity in two ganglia from the crab Cancer borealis.

In Proceedings of the National Academy of Sciences of the United States of America ; h5-index 0.0

Understanding circuit organization depends on identification of cell types. Recent advances in transcriptional profiling methods have enabled classification of cell types by their gene expression. While exceptionally powerful and high throughput, the ground-truth validation of these methods is difficult: If cell type is unknown, how does one assess whether a given analysis accurately captures neuronal identity? To shed light on the capabilities and limitations of solely using transcriptional profiling for cell-type classification, we performed 2 forms of transcriptional profiling-RNA-seq and quantitative RT-PCR, in single, unambiguously identified neurons from 2 small crustacean neuronal networks: The stomatogastric and cardiac ganglia. We then combined our knowledge of cell type with unbiased clustering analyses and supervised machine learning to determine how accurately functionally defined neuron types can be classified by expression profile alone. The results demonstrate that expression profile is able to capture neuronal identity most accurately when combined with multimodal information that allows for post hoc grouping, so analysis can proceed from a supervised perspective. Solely unsupervised clustering can lead to misidentification and an inability to distinguish between 2 or more cell types. Therefore, this study supports the general utility of cell identification by transcriptional profiling, but adds a caution: It is difficult or impossible to know under what conditions transcriptional profiling alone is capable of assigning cell identity. Only by combining multiple modalities of information such as physiology, morphology, or innervation target can neuronal identity be unambiguously determined.

Northcutt Adam J, Kick Daniel R, Otopalik Adriane G, Goetz Benjamin M, Harris Rayna M, Santin Joseph M, Hofmann Hans A, Marder Eve, Schulz David J

2019-Dec-05

RNA-seq, expression profiling, qPCR, stomatogastric

General General

The pathogenesis of systemic lupus erythematosus: Harnessing big data to understand the molecular basis of lupus.

In Journal of autoimmunity ; h5-index 65.0

Systemic lupus erythematosus (SLE) is a chronic, systemic autoimmune disease that causes damage to multiple organ systems. Despite decades of research and available murine models that capture some aspects of the human disease, new treatments for SLE lag behind other autoimmune diseases such as Rheumatoid Arthritis and Crohn's disease. Big data genomic assays have transformed our understanding of SLE by providing important insights into the molecular heterogeneity of this multigenic disease. Gene wide association studies have demonstrated more than 100 risk loci, supporting a model of multiple genetic hits increasing SLE risk in a non-linear fashion, and providing evidence of ancestral diversity in susceptibility loci. Epigenetic studies to determine the role of methylation, acetylation and non-coding RNAs have provided new understanding of the modulation of gene expression in SLE patients and identified new drug targets and biomarkers for SLE. Gene expression profiling has led to a greater understanding of the role of myeloid cells in the pathogenesis of SLE, confirmed roles for T and B cells in SLE, promoted clinical trials based on the prominent interferon signature found in SLE patients, and identified candidate biomarkers and cellular signatures to further drug development and drug repurposing. Gene expression studies are advancing our understanding of the underlying molecular heterogeneity in SLE and providing hope that patient stratification will expedite new therapies based on personal molecular signatures. Although big data analyses present unique interpretation challenges, both computationally and biologically, advances in machine learning applications may facilitate the ability to predict changes in SLE disease activity and optimize therapeutic strategies.

Catalina Michelle D, Owen Katherine A, Labonte Adam C, Grammer Amrie C, Lipsky Peter E

2019-Dec-02

Autoimmune disease, Epigenetics, Gene expression, Genome wide association study, Machine learning, Systemic lupus erythematosus

General General

The role of artificial intelligence in learning and professional development for healthcare professionals.

In Healthcare management forum ; h5-index 0.0

This article discusses the emerging role of Artificial Intelligence (AI) in the learning and professional development of healthcare professionals. It provides a brief history of AI, current and past applications in healthcare education and training, and discusses why and how health leaders can revolutionize education system practices using AI in healthcare education. It also discusses potential implications of AI on human educators like clinical educators and provides recommendations for health leaders to support the application of AI in the learning and professional development of healthcare professionals.

Randhawa Gurprit K, Jackson Mary

2020-Jan

Radiology Radiology

[Artificial intelligence and radiomics in MRI-based prostate diagnostics].

In Der Radiologe ; h5-index 0.0

CLINICAL/METHODICAL ISSUE : In view of the diagnostic complexity and the large number of examinations, modern radiology is challenged to identify clinically significant prostate cancer (PCa) with high sensitivity and specificity. Meanwhile overdiagnosis and overtreatment of clinically nonsignificant carcinomas need to be avoided.

STANDARD RADIOLOGICAL METHODS : Increasingly, international guidelines recommend multiparametric magnetic resonance imaging (mpMRI) as first-line investigation in patients with suspected PCa.

METHODICAL INNOVATIONS : Image interpretation according to the PI-RADS criteria is limited by interobserver variability. Thus, rapid developments in the field of automated image analysis tools, including radiomics and artificial intelligence (AI; machine learning, deep learning), give hope for further improvement in patient care.

PERFORMANCE : AI focuses on the automated detection and classification of PCa, but it also attempts to stratify tumor aggressiveness according to the Gleason score. Recent studies present good to very good results in radiomics or AI-supported mpMRI diagnosis. Nevertheless, these systems are not widely used in clinical practice.

ACHIEVEMENTS AND PRACTICAL RECOMMENDATIONS : In order to apply these innovative technologies, a growing awareness for the need of structured data acquisition, development of robust systems and an increased acceptance of AI as diagnostic support are needed. If AI overcomes these obstacles, it may play a key role in the quantitative and reproducible image-based diagnosis of ever-increasing prostate MRI examination volumes.

Hamm Charlie Alexander, Beetz Nick Lasse, Savic Lynn Jeanette, Penzkofer Tobias

2019-Dec-04

Deep learning, Machine learning, Multiparametric magnetic resonance imaging, Prostate cancer, Quantitative imaging

General General

RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou's five-step rule.

In Database : the journal of biological databases and curation ; h5-index 0.0

By reducing amino acid alphabet, the protein complexity can be significantly simplified, which could improve computational efficiency, decrease information redundancy and reduce chance of overfitting. Although some reduced alphabets have been proposed, different classification rules could produce distinctive results for protein sequence analysis. Thus, it is urgent to construct a systematical frame for reduced alphabets. In this work, we constructed a comprehensive web server called RAACBook for protein sequence analysis and machine learning application by integrating reduction alphabets. The web server contains three parts: (i) 74 types of reduced amino acid alphabet were manually extracted to generate 673 reduced amino acid clusters (RAACs) for dealing with unique protein problems. It is easy for users to select desired RAACs from a multilayer browser tool. (ii) An online tool was developed to analyze primary sequence of protein. The tool could produce K-tuple reduced amino acid composition by defining three correlation parameters (K-tuple, g-gap, λ-correlation). The results are visualized as sequence alignment, mergence of RAA composition, feature distribution and logo of reduced sequence. (iii) The machine learning server is provided to train the model of protein classification based on K-tuple RAAC. The optimal model could be selected according to the evaluation indexes (ROC, AUC, MCC, etc.). In conclusion, RAACBook presents a powerful and user-friendly service in protein sequence analysis and computational proteomics. RAACBook can be freely available at http://bioinfor.imu.edu.cn/raacbook. Database URL: http://bioinfor.imu.edu.cn/raacbook.

Zheng Lei, Huang Shenghui, Mu Nengjiang, Zhang Haoyue, Zhang Jiayu, Chang Yu, Yang Lei, Zuo Yongchun

2019-Jan-01

General General

An attention-based deep learning model for clinical named entity recognition of Chinese electronic medical records.

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

BACKGROUND : Clinical named entity recognition (CNER) is important for medical information mining and establishment of high-quality knowledge map. Due to the different text features from natural language and a large number of professional and uncommon clinical terms in Chinese electronic medical records (EMRs), there are still many difficulties in clinical named entity recognition of Chinese EMRs. It is of great importance to eliminate semantic interference and improve the ability of autonomous learning of internal features of the model under the small training corpus.

METHODS : From the perspective of deep learning, we integrated the attention mechanism into neural network, and proposed an improved clinical named entity recognition method for Chinese electronic medical records called BiLSTM-Att-CRF, which could capture more useful information of the context and avoid the problem of missing information caused by long-distance factors. In addition, medical dictionaries and part-of-speech (POS) features were also introduced to improve the performance of the model.

RESULTS : Based on China Conference on Knowledge Graph and Semantic Computing (CCKS) 2017 and 2018 Chinese EMRs corpus, our BiLSTM-Att-CRF model finally achieved better performance than other widely-used models without additional features(F1-measure of 85.4% in CCKS 2018, F1-measure of 90.29% in CCKS 2017), and achieved the best performance with POS and dictionary features (F1-measure of 86.11% in CCKS 2018, F1-measure of 90.48% in CCKS 2017). In particular, the BiLSTM-Att-CRF model had significant effect on the improvement of Recall.

CONCLUSIONS : Our work preliminarily confirmed the validity of attention mechanism in discovering key information and mining text features, which might provide useful ideas for future research in clinical named entity recognition of Chinese electronic medical records. In the future, we will explore the deeper application of attention mechanism in neural network.

Li Luqi, Zhao Jie, Hou Li, Zhai Yunkai, Shi Jinming, Cui Fangfang

2019-Dec-05

Attention mechanism, Chinese electronic medical records, Named entity recognition

General General

Improving rare disease classification using imperfect knowledge graph.

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

BACKGROUND : Accurately recognizing rare diseases based on symptom description is an important task in patient triage, early risk stratification, and target therapies. However, due to the very nature of rare diseases, the lack of historical data poses a great challenge to machine learning-based approaches. On the other hand, medical knowledge in automatically constructed knowledge graphs (KGs) has the potential to compensate the lack of labeled training examples. This work aims to develop a rare disease classification algorithm that makes effective use of a knowledge graph, even when the graph is imperfect.

METHOD : We develop a text classification algorithm that represents a document as a combination of a "bag of words" and a "bag of knowledge terms," where a "knowledge term" is a term shared between the document and the subgraph of KG relevant to the disease classification task. We use two Chinese disease diagnosis corpora to evaluate the algorithm. The first one, HaoDaiFu, contains 51,374 chief complaints categorized into 805 diseases. The second data set, ChinaRe, contains 86,663 patient descriptions categorized into 44 disease categories.

RESULTS : On the two evaluation data sets, the proposed algorithm delivers robust performance and outperforms a wide range of baselines, including resampling, deep learning, and feature selection approaches. Both classification-based metric (macro-averaged F1 score) and ranking-based metric (mean reciprocal rank) are used in evaluation.

CONCLUSION : Medical knowledge in large-scale knowledge graphs can be effectively leveraged to improve rare diseases classification models, even when the knowledge graph is incomplete.

Li Xuedong, Wang Yue, Wang Dongwu, Yuan Walter, Peng Dezhong, Mei Qiaozhu

2019-Dec-05

Extremely imbalanced data, Knowledge graph, Machine learning, Rare disease diagnosis, Text classification

General General

Applying a deep learning-based sequence labeling approach to detect attributes of medical concepts in clinical text.

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

BACKGROUND : To detect attributes of medical concepts in clinical text, a traditional method often consists of two steps: named entity recognition of attributes and then relation classification between medical concepts and attributes. Here we present a novel solution, in which attribute detection of given concepts is converted into a sequence labeling problem, thus attribute entity recognition and relation classification are done simultaneously within one step.

METHODS : A neural architecture combining bidirectional Long Short-Term Memory networks and Conditional Random fields (Bi-LSTMs-CRF) was adopted to detect various medical concept-attribute pairs in an efficient way. We then compared our deep learning-based sequence labeling approach with traditional two-step systems for three different attribute detection tasks: disease-modifier, medication-signature, and lab test-value.

RESULTS : Our results show that the proposed method achieved higher accuracy than the traditional methods for all three medical concept-attribute detection tasks.

CONCLUSIONS : This study demonstrates the efficacy of our sequence labeling approach using Bi-LSTM-CRFs on the attribute detection task, indicating its potential to speed up practical clinical NLP applications.

Xu Jun, Li Zhiheng, Wei Qiang, Wu Yonghui, Xiang Yang, Lee Hee-Jin, Zhang Yaoyun, Wu Stephen, Xu Hua

2019-Dec-05

Clinical notes, Information extraction, Natural language processing

General General

[Automated Cell Counting Using "Deep Learning" in Donor Corneas from Organ Culture Achieves High Precision and Accuracy].

In Klinische Monatsblatter fur Augenheilkunde ; h5-index 0.0

BACKGROUND : Human corneal grafts from organ culture need to have more than 2000 endothelial cells/mm2 to be suitable for transplantation. Measurement of the endothelial cell density is complicated by invisible cell borders in phase contrast microscopy, as well as by limited areas for counting due to folds in the Descemet membrane of the swollen corneal grafts. To date, no automated counting method for measuring the endothelial cell density exists. The neuronal network U-Net has already proven itself in automated segmentation of specular microscopy images of human corneal endothelium. The aim of this study was the application of the U-Net in the quality control of human corneal grafts.

MATERIAL AND METHODS : Training of the U-Net was performed using 100 manually tagged endothelial cell images of corneal grafts from the Lions eye bank in Baden-Württemberg. Another 100 images were obtained for testing the precision of measurements of the U-Net. These were adjudged manually by a) an experienced investigator and b) a less experienced ophthalmologist. The endothelial cells in identical images were then counted automatically by the trained U-Net. Comparison with the manually counted results was drawn by Pearson correlation.

RESULTS : The correlation coefficient between the U-Net and the experienced investigator as gold standard was 0.9. The correlation coefficient between the less experienced ophthalmologist and the gold standard was only 0.81. Both correlations were statistically highly significant (p < 0.0001).

DISCUSSION : The strong correlation between the U-Net and the gold standard points out that, given medical approval, effective assistance for eye banks is possible in quality control by automated counting. This could improve objectivity and efficiency of work flow.

Heinzelmann Sonja, Daniel Moritz Claudius, Maier Philip Christian, Reinhard Thomas, Böhringer Daniel

2019-Dec

General General

[Artificial Intelligence for the Development of Screening Parameters in the Field of Corneal Biomechanics].

In Klinische Monatsblatter fur Augenheilkunde ; h5-index 0.0

Machine learning and artificial intelligence are mostly important if data analysis by knowledge-based analytical methods is difficult and complex. In such cases, combined analytical and empirical approaches based on AI are also meaningful. The development and validation of several clinical parameters for the Corvis ST are a concrete example of this approach. In this article, the development of three screening parameters is described. It is shown how these developments lead to clinical solutions that can be beneficial for detecting clinical and subclinical keratoconus as well as for glaucoma screening.

Reisdorf Sven

2019-Dec

General General

Predicting HSE band gaps from PBE charge densities via neural network functionals.

In Journal of physics. Condensed matter : an Institute of Physics journal ; h5-index 0.0

Density functional theory (DFT) has become a standard method for ab initio calculations of material properties. However, it has a number of shortcomings, particularly in predicting key properties, such as band gap and optical spectra, which are dependent on excited states. To treat such properties, more accurate approaches such as GW or DFT with hybrid functionals (including HSE, PBE0, and B3LYP, to name a few) can be employed; however, these approaches are unfeasible for many large and/or complex systems due to their high computational cost and large memory requirements. In this work, we investigate the ability to train neural networks of the traditional DFT charge density computed with a standard PBE functional to accurately predict HSE band gaps. We show that a single network PBE charge density functional can predict the HSE band gap of seven different materials -- silicon, gallium arsenide, molybdenum disulfide, germanium, tin phosphate, titanium phosphate, and zirconium phosphate -- under a wide variety of conditions with an RSME of 172.6 meV, which is 34\% better accuracy than standard regression between the PBE and HSE band gaps. This approach, which, in principle, can be used to map PBE charge densities to band gaps or other properties computed with any higher accuracy method, has the potential to decrease computational costs, increase prediction accuracy, and enable accurate high-throughput screening for a wide variety of complex materials systems.

Lentz Levi, Kolpak Alexie

2019-Dec-05

band gap, density functional theory, machine learning

General General

Students' perceptions of debating as a learning strategy: A qualitative study.

In Nurse education in practice ; h5-index 36.0

Debate has been shown to develop critical thinking skills, enhance communication, and encourage teamwork in a range of different disciplines, including nursing. The objective of this study was to explore students' perceptions of the educational value of debate. A semi-structured focus group was conducted with 13 undergraduate Operating Department Practice students following a debate on the opt-out system of organ donation. Transcripts were analysed thematically, identifying three main themes that described the students' perceptions of the debate. These were: (1) openness to diverse viewpoints; (2) developing non-technical skills, and (3) encouraging deep learning. The analysis showed participants perceived debate to be a valuable educational method that enhanced their learning. Engaging in debate encouraged students to critically reflect on their prior beliefs about organ donation-in some cases leading them to reconsider their original position. The findings from this study suggest that debate can be a valuable pedagogical tool to incorporate into healthcare education. Future research should consider the use of debate to develop non-technical skills that have utility in healthcare.

Rodger Daniel, Stewart-Lord Adéle

2019-Nov-23

Debate, Education, Healthcare professionals, Organ donation, Qualitative

General General

How Big Data and High-performance Computing Drive Brain Science.

In Genomics, proteomics & bioinformatics ; h5-index 0.0

Brain science accelerates the study of intelligence and behavior, contributes fundamental insights into human cognition, and offers prospective treatments for brain disease. Faced with the challenges posed by imaging technologies and deep learning computational models, big data and high-performance computing (HPC) play essential roles in studying brain function, brain diseases, and large-scale brain models or connectomes. We review the driving forces behind big data and HPC methods applied to brain science, including deep learning, powerful data analysis capabilities, and computational performance solutions, each of which can be used to improve diagnostic accuracy and research output. This work reinforces predictions that big data and HPC will continue to improve brain science by making ultrahigh-performance analysis possible, by improving data standardization and sharing, and by providing new neuromorphic insights.

Chen Shanyu, He Zhipeng, Han Xinyin, He Xiaoyu, Li Ruilin, Zhu Haidong, Zhao Dan, Dai Chuangchuang, Zhang Yu, Lu Zhonghua, Chi Xuebin, Niu Beifang

2019-Dec-02

Big data, Brain connectomes, Brain science, Deep learning, High-performance computing

General General

i4mC-ROSE, a bioinformatics tool for the identification of DNA N4-methylcytosine sites in the Rosaceae genome.

In International journal of biological macromolecules ; h5-index 0.0

One of the most important epigenetic modifications is N4-methylcytosine, which regulates many biological processes including DNA replication and chromosome stability. Identification of N4-methylcytosine sites is pivotal to understand specific biological functions. Herein, we developed the first bioinformatics tool called i4mC-ROSE for identifying N4-methylcytosine sites in the genomes of Fragaria vesca and Rosa chinensis in the Rosaceae, which utilizes a random forest classifier with six encoding methods that cover various aspects of DNA sequence information. The i4mC-ROSE predictor achieves area under the curve scores of 0.883 and 0.889 for the two genomes during cross-validation. Moreover, the i4mC-ROSE outperforms other classifiers tested in this study when objectively evaluated on the independent datasets. The proposed i4mC-ROSE tool can serve users' demand for the prediction of 4mC sites in the Rosaceae genome. The i4mC-ROSE predictor and utilized datasets are publicly accessible at http://kurata14.bio.kyutech.ac.jp/i4mC-ROSE/.

Hasan Md Mehedi, Manavalan Balachandran, Khatun Mst Shamima, Kurata Hiroyuki

2019-Dec-02

DNA methylation, Linear regression, Machine learning, N4-methylcytosine site, Sequence encoding

Surgery Surgery

Reconstruction error based deep neural networks for coronary heart disease risk prediction.

In PloS one ; h5-index 176.0

Coronary heart disease (CHD) is one of the leading causes of death worldwide; if suffering from CHD and being in its end-stage, the most advanced treatments are required, such as heart surgery and heart transplant. Moreover, it is not easy to diagnose CHD at the earlier stage; hospitals diagnose it based on various types of medical tests. Thus, by predicting high-risk people who are to suffer from CHD, it is significant to reduce the risks of developing CHD. In recent years, some research works have been done using data mining to predict the risk of developing diseases based on medical tests. In this study, we have proposed a reconstruction error (RE) based deep neural networks (DNNs); this approach uses a deep autoencoder (AE) model for estimating RE. Initially, a training dataset is divided into two groups by their RE divergence on the deep AE model that learned from the whole training dataset. Next, two DNN classifiers are trained on each group of datasets separately by combining a RE based new feature with other risk factors to predict the risk of developing CHD. For creating the new feature, we use deep AE model that trained on the only high-risk dataset. We have performed an experiment to prove how the components of our proposed method work together more efficiently. As a result of our experiment, the performance measurements include accuracy, precision, recall, F-measure, and AUC score reached 86.3371%, 91.3716%, 82.9024%, 86.9148%, and 86.6568%, respectively. These results show that the proposed AE-DNNs outperformed regular machine learning-based classifiers for CHD risk prediction.

Amarbayasgalan Tsatsral, Park Kwang Ho, Lee Jong Yun, Ryu Keun Ho

2019

Radiology Radiology

Neuroimaging modality fusion in Alzheimer's classification using convolutional neural networks.

In PloS one ; h5-index 176.0

Automated methods for Alzheimer's disease (AD) classification have the potential for great clinical benefits and may provide insight for combating the disease. Machine learning, and more specifically deep neural networks, have been shown to have great efficacy in this domain. These algorithms often use neurological imaging data such as MRI and FDG PET, but a comprehensive and balanced comparison of the MRI and amyloid PET modalities has not been performed. In order to accurately determine the relative strength of each imaging variant, this work performs a comparison study in the context of Alzheimer's dementia classification using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with identical neural network architectures. Furthermore, this work analyzes the benefits of using both modalities in a fusion setting and discusses how these data types may be leveraged in future AD studies using deep learning.

Punjabi Arjun, Martersteck Adam, Wang Yanran, Parrish Todd B, Katsaggelos Aggelos K

2019

General General

Predicting the replicability of social science lab experiments.

In PloS one ; h5-index 176.0

We measure how accurately replication of experimental results can be predicted by black-box statistical models. With data from four large-scale replication projects in experimental psychology and economics, and techniques from machine learning, we train predictive models and study which variables drive predictable replication. The models predicts binary replication with a cross-validated accuracy rate of 70% (AUC of 0.77) and estimates of relative effect sizes with a Spearman ρ of 0.38. The accuracy level is similar to market-aggregated beliefs of peer scientists [1, 2]. The predictive power is validated in a pre-registered out of sample test of the outcome of [3], where 71% (AUC of 0.73) of replications are predicted correctly and effect size correlations amount to ρ = 0.25. Basic features such as the sample and effect sizes in original papers, and whether reported effects are single-variable main effects or two-variable interactions, are predictive of successful replication. The models presented in this paper are simple tools to produce cheap, prognostic replicability metrics. These models could be useful in institutionalizing the process of evaluation of new findings and guiding resources to those direct replications that are likely to be most informative.

Altmejd Adam, Dreber Anna, Forsell Eskil, Huber Juergen, Imai Taisuke, Johannesson Magnus, Kirchler Michael, Nave Gideon, Camerer Colin

2019

General General

A study of deep learning methods for de-identification of clinical notes in cross-institute settings.

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

BACKGROUND : De-identification is a critical technology to facilitate the use of unstructured clinical text while protecting patient privacy and confidentiality. The clinical natural language processing (NLP) community has invested great efforts in developing methods and corpora for de-identification of clinical notes. These annotated corpora are valuable resources for developing automated systems to de-identify clinical text at local hospitals. However, existing studies often utilized training and test data collected from the same institution. There are few studies to explore automated de-identification under cross-institute settings. The goal of this study is to examine deep learning-based de-identification methods at a cross-institute setting, identify the bottlenecks, and provide potential solutions.

METHODS : We created a de-identification corpus using a total 500 clinical notes from the University of Florida (UF) Health, developed deep learning-based de-identification models using 2014 i2b2/UTHealth corpus, and evaluated the performance using UF corpus. We compared five different word embeddings trained from the general English text, clinical text, and biomedical literature, explored lexical and linguistic features, and compared two strategies to customize the deep learning models using UF notes and resources.

RESULTS : Pre-trained word embeddings using a general English corpus achieved better performance than embeddings from de-identified clinical text and biomedical literature. The performance of deep learning models trained using only i2b2 corpus significantly dropped (strict and relax F1 scores dropped from 0.9547 and 0.9646 to 0.8568 and 0.8958) when applied to another corpus annotated at UF Health. Linguistic features could further improve the performance of de-identification in cross-institute settings. After customizing the models using UF notes and resource, the best model achieved the strict and relaxed F1 scores of 0.9288 and 0.9584, respectively.

CONCLUSIONS : It is necessary to customize de-identification models using local clinical text and other resources when applied in cross-institute settings. Fine-tuning is a potential solution to re-use pre-trained parameters and reduce the training time to customize deep learning-based de-identification models trained using clinical corpus from a different institution.

Yang Xi, Lyu Tianchen, Li Qian, Lee Chih-Yin, Bian Jiang, Hogan William R, Wu Yonghui

2019-Dec-05

Cross institutions, De-identification, Deep learning, EHR, Protected health information

Radiology Radiology

Orbit image analysis machine learning software can be used for the histological quantification of acute ischemic stroke blood clots.

In PloS one ; h5-index 176.0

Our aim was to assess the utility of a novel machine learning software (Orbit Image Analysis) in the histological quantification of acute ischemic stroke (AIS) clots. We analyzed 50 AIS blood clots retrieved using mechanical thrombectomy procedures. Following H&E staining, quantification of clot components was performed by two different methods: a pathologist using a reference standard method (Adobe Photoshop CC) and an experienced researcher using Orbit Image Analysis. Following quantification, the clots were categorized into 3 types: RBC dominant (≥60% RBCs), Mixed and Fibrin dominant (≥60% Fibrin). Correlations between clot composition and Hounsfield Units density on Computed Tomography (CT) were assessed. There was a significant correlation between the components of clots as quantified by the Orbit Image Analysis algorithm and the reference standard approach (ρ = 0.944**, p < 0.001, n = 150). A significant relationship was found between clot composition (RBC-Rich, Mixed, Fibrin-Rich) and the presence of a Hyperdense artery sign using the algorithmic method (X2(2) = 6.712, p = 0.035*) but not using the reference standard method (X2(2) = 3.924, p = 0.141). Orbit Image Analysis machine learning software can be used for the histological quantification of AIS clots, reproducibly generating composition analyses similar to current reference standard methods.

Fitzgerald Seán, Wang Shunli, Dai Daying, Murphree Dennis H, Pandit Abhay, Douglas Andrew, Rizvi Asim, Kadirvel Ramanathan, Gilvarry Michael, McCarthy Ray, Stritt Manuel, Gounis Matthew J, Brinjikji Waleed, Kallmes David F, Doyle Karen M

2019

Public Health Public Health

Trajectories of hepatic and coagulation dysfunctions related to a rapidly fatal outcome among hospitalized patients with dengue fever in Tainan, 2015.

In PLoS neglected tropical diseases ; h5-index 79.0

BACKGROUND : Hepatic dysfunction and coagulopathy are common in acute dengue illness. We analyzed the trajectories of the above parameters in the survivors and fatal patients in the outbreak in Tainan, 2015.

METHODS : A retrospective study was conducted using data from a tertiary hospital between January and December 2015. Multilevel modeling (MLM) was used to identify the changes in aminotransferase (AST), alanine aminotransferase (ALT), activated partial thromboplastin time (aPTT), and platelet counts from Day 0 to Day 7 of the onset of dengue infection. The machine-learning algorithm was used by purity measure assumption to calculate the accuracy of serum transaminases and coagulation variables to discriminate between the fatal and survival groups.

RESULTS : There were 4,069 dengue patients, of which 0.9% died in one week after illness onset (i.e., early mortality). Case fatality rate was the highest for those aged ≥70 years. Both AST and ALT values of the fatal group were significantly higher than those of the survivor group from Day 3 (AST median, 624 U/L vs. 60 U/L, p < 0.001; ALT median, 116 U/L vs. 29 U/L, p = 0.01) of illness onset and peaked on Day 6 (AST median, 9805 U/L vs. 90 U/L, p < 0.001; ALT median, 1504 U/L vs. 49 U/L, p < 0.001). AST ≥ 203 U/L, ALT ≥ 55 U/L, AST2/ALT criteria ≥337.35, or AST/platelet count ratio index (APRI) ≥ 19.18 on Day 3 of dengue infection had a high true positive rate, 90%, 78%, 100%, or 100%, respectively, of early mortality. The platelet counts of the fatal group declined significantly than those of the survivor group since Day 3 of illness onset (median, 19 x103/μl vs. 91 x103/μl, p < 0.01), and aPTT values of the fatal group significantly prolonged longer since Day 5 (median, 68.7 seconds vs. 40.1 seconds, p < 0.001).

CONCLUSIONS : AST, ALT, and platelet counts should be monitored closely from Day 0 to Day 3 of dengue infection, and aPTT be followed up on Day 5 of infection to identify the individuals at risk for early mortality.

Yeh Chun-Yin, Lu Bing-Ze, Liang Wei-Jie, Shu Yu-Chen, Chuang Kun-Ta, Chen Po-Lin, Ko Wen-Chien, Ko Nai-Ying

2019-Dec

Surgery Surgery

A Machine Learning-Based Triage Tool for Children With Acute Infection in a Low Resource Setting.

In Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies ; h5-index 0.0

OBJECTIVES : To deploy machine learning tools (random forests) to develop a model that reliably predicts hospital mortality in children with acute infections residing in low- and middle-income countries, using age and other variables collected at hospital admission.

DESIGN : Post hoc analysis of a single-center, prospective, before-and-after feasibility trial.

SETTING : Rural district hospital in Rwanda, a low-income country in Sub-Sahara Africa.

PATIENTS : Infants and children greater than 28 days and less than 18 years of life hospitalized because of an acute infection.

INTERVENTIONS : None.

MEASUREMENTS AND MAIN RESULTS : Age, vital signs (heart rate, respiratory rate, and temperature) capillary refill time, altered mental state collected at hospital admission, as well as survival status at hospital discharge were extracted from the trial database. This information was collected for 1,579 adult and pediatric patients admitted to a regional referral hospital with an acute infection in rural Rwanda. Nine-hundred forty-nine children were included in this analysis. We predicted survival in study subjects using random forests, a machine learning algorithm. Five prediction models, all including age plus two to five other variables, were tested. Three distinct optimization criteria of the algorithm were then compared. The in-hospital mortality was 1.5% (n = 14). All five models could predict in-hospital mortality with an area under the receiver operating characteristic curve ranging between 0.69 and 0.8. The model including age, respiratory rate, capillary refill time, altered mental state exhibited the highest predictive value area under the receiver operating characteristic curve 0.8 (95% CI, 0.78-0.8) with the lowest possible number of variables.

CONCLUSIONS : A machine learning-based algorithm could reliably predict hospital mortality in a Sub-Sahara African population of 949 children with an acute infection using easily collected information at admission which includes age, respiratory rate, capillary refill time, and altered mental state. Future studies need to evaluate and strengthen this algorithm in larger pediatric populations, both in high- and low-/middle-income countries.

Kwizera Arthur, Kissoon Niranjan, Musa Ndidiamaka, Urayeneza Olivier, Mujyarugamba Pierre, Patterson Andrew J, Harmon Lori, Farmer Joseph C, Dünser Martin W, Meier Jens

2019-Dec

General General

Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection.

In IEEE transactions on pattern analysis and machine intelligence ; h5-index 127.0

In this paper, we propose a novel object detection algorithm named "Deep Regionlets" by integrating deep neural networks and conventional detection schema for accurate generic object detection. Motivated by the advantages of regionlets on modeling object deformation and multiple aspect ratios, we incorporate regionlets into an end-to-end trainable deep learning framework. The deep regionlets framework consists of a region selection network and a deep regionlet learning module. Specifically, given a detection bounding box proposal, the region selection network provides guidance on where to select sub-regions from which features can be learned from. An object proposal typically contains 3-16 sub-regions. The regionlet learning module focuses on local feature selection and transformation to alleviate the effects of appearance variations. To this end, we first realize non-rectangular region selection within the detection framework to accommodate variations in object appearance. Moreover, we design a "gating network" within the regionlet leaning module to enable instance dependent soft feature selection and pooling. The Deep Regionlets framework is trained end-to-end without additional efforts. We present ablation studies and extensive experiments on the PASCAL VOC dataset and the Microsoft COCO dataset. The proposed method outperforms state-of-the-art algorithms, such as RetinaNet and Mask R-CNN, even without additional segmentation labels.

Xu Hongyu, Lv Xutao, Wang Xiaoyu, Ren Zhou, Bodla Navaneeth, Chellappa Rama

2019-Dec-05

General General

ImPLoc: A multi-instance deep learning model for the prediction of protein subcellular localization based on immunohistochemistry images.

In Bioinformatics (Oxford, England) ; h5-index 0.0

MOTIVATION : The tissue atlas of the human protein atlas (HPA) houses immunohistochemistry (IHC) images visualizing the protein distribution from the tissue level down to the cell level, which provide an important resource to study human spatial proteome. Especially, the protein subcellular localization patterns revealed by these images are helpful for understanding protein functions, and the differential localization analysis across normal and cancer tissues lead to new cancer biomarkers. However, computational tools for processing images in this database are highly underdeveloped. The recognition of the localization patterns suffers from the variation in image quality and the difficulty in detecting microscopic targets.

RESULTS : We propose a deep multi-instance multi-label model, ImPLoc, to predict the subcellular locations from IHC images. In this model, we employ a deep CNN-based feature extractor to represent image features, and design a multi-head self-attention encoder to aggregate multiple feature vectors for subsequent prediction. We construct a benchmark dataset of 1186 proteins including 7855 images from HPA and 6 subcellular locations. The experimental results show that ImPLoc achieves significant enhancement on the prediction accuracy compared with the current computational methods. We further apply ImPLoc to a test set of 889 proteins with images from both normal and cancer tissues, and obtain 8 differentially localized proteins with a significance level of 0.05.

AVAILABILITY : https://github.com/yl2019lw/ImPloc.

SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.

Long Wei, Yang Yang, Shen Hong-Bin

2019-Dec-05

General General

AI-powered transmitted light microscopy for functional analysis of live cells.

In Scientific reports ; h5-index 158.0

Transmitted light microscopy can readily visualize the morphology of living cells. Here, we introduce artificial-intelligence-powered transmitted light microscopy (AIM) for subcellular structure identification and labeling-free functional analysis of live cells. AIM provides accurate images of subcellular organelles; allows identification of cellular and functional characteristics (cell type, viability, and maturation stage); and facilitates live cell tracking and multimodality analysis of immune cells in their native form without labeling.

Kim Dongyoung, Min Yoohong, Oh Jung Min, Cho Yoon-Kyoung

2019-Dec-05

General General

Deep learning enables pathologist-like scoring of NASH models.

In Scientific reports ; h5-index 158.0

Non-alcoholic fatty liver disease (NAFLD) and the progressive form of non-alcoholic steatohepatitis (NASH) are diseases of major importance with a high unmet medical need. Efficacy studies on novel compounds to treat NAFLD/NASH using disease models are frequently evaluated using established histological feature scores on ballooning, inflammation, steatosis and fibrosis. These features are assessed by a trained pathologist using microscopy and assigned discrete scores. We demonstrate how to automate these scores with convolutional neural networks (CNNs). Whole slide images of stained liver sections are analyzed using two different scales with four CNNs, each specialized for one of four histopathological features. A continuous value is obtained to quantify the extent of each feature, which can be used directly to provide a high resolution readout. In addition, the continuous values can be mapped to obtain the established discrete pathologist-like scores. The automated deep learning-based scores show good agreement with the trainer - a human pathologist.

Heinemann Fabian, Birk Gerald, Stierstorfer Birgit

2019-Dec-05

General General

A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses.

In Nature communications ; h5-index 260.0

When metallic glasses (MGs) are subjected to mechanical loads, the plastic response of atoms is non-uniform. However, the extent and manner in which atomic environment signatures present in the undeformed structure determine this plastic heterogeneity remain elusive. Here, we demonstrate that novel site environment features that characterize interstice distributions around atoms combined with machine learning (ML) can reliably identify plastic sites in several Cu-Zr compositions. Using only quenched structural information as input, the ML-based plastic probability estimates ("quench-in softness" metric) can identify plastic sites that could activate at high strains, losing predictive power only upon the formation of shear bands. Moreover, we reveal that a quench-in softness model trained on a single composition and quench rate substantially improves upon previous models in generalizing to different compositions and completely different MG systems (Ni62Nb38, Al90Sm10 and Fe80P20). Our work presents a general, data-centric framework that could potentially be used to address the structural origin of any site-specific property in MGs.

Wang Qi, Jain Anubhav

2019-Dec-05

Radiology Radiology

Age Assessment of Youth and Young Adults Using Magnetic Resonance Imaging of the Knee: A Deep Learning Approach.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : Bone age assessment (BAA) is an important tool for diagnosis and in determining the time of treatment in a number of pediatric clinical scenarios, as well as in legal settings where it is used to estimate the chronological age of an individual where valid documents are lacking. Traditional methods for BAA suffer from drawbacks, such as exposing juveniles to radiation, intra- and interrater variability, and the time spent on the assessment. The employment of automated methods such as deep learning and the use of magnetic resonance imaging (MRI) can address these drawbacks and improve the assessment of age.

OBJECTIVE : The aim of this paper is to propose an automated approach for age assessment of youth and young adults in the age range when the length growth ceases and growth zones are closed (14-21 years of age) by employing deep learning using MRI of the knee.

METHODS : This study carried out MRI examinations of the knee of 402 volunteer subjects-221 males (55.0%) and 181 (45.0%) females-aged 14-21 years. The method comprised two convolutional neural network (CNN) models: the first one selected the most informative images of an MRI sequence, concerning age-assessment purposes; these were then used in the second module, which was responsible for the age estimation. Different CNN architectures were tested, both training from scratch and employing transfer learning.

RESULTS : The CNN architecture that provided the best results was GoogLeNet pretrained on the ImageNet database. The proposed method was able to assess the age of male subjects in the range of 14-20.5 years, with a mean absolute error (MAE) of 0.793 years, and of female subjects in the range of 14-19.5 years, with an MAE of 0.988 years. Regarding the classification of minors-with the threshold of 18 years of age-an accuracy of 98.1% for male subjects and 95.0% for female subjects was achieved.

CONCLUSIONS : The proposed method was able to assess the age of youth and young adults from 14 to 20.5 years of age for male subjects and 14 to 19.5 years of age for female subjects in a fully automated manner, without the use of ionizing radiation, addressing the drawbacks of traditional methods.

Dallora Ana Luiza, Berglund Johan Sanmartin, Brogren Martin, Kvist Ola, Diaz Ruiz Sandra, Dübbel André, Anderberg Peter

2019-Dec-05

age assessment, bone age, convolutional neural networks, deep learning, knee, machine learning, magnetic resonance imaging, medical imaging, skeletal maturity, transfer learning

General General

Machine Learning as proposal for a better application of food nanotechnology regulation in European Union.

In Current topics in medicinal chemistry ; h5-index 40.0

Cheminformatic methods are able to design and create predictive models with high rate of accuracy saving time, costs and animal sacrifice. It has been applied on different disciplines including nanotechnology. Given the current gaps of scientific knowledge and the need of efficient application of food law, this paper makes an analysis of principles of European food law for the appropriateness of applying biological activity Machine Learning prediction models to guarantee public safety. For this, a systematic study of the regulation and the incorporation of predictive models of biological activity of nanomaterials was carried out through the analysis of the express nanotechnology regulation on foods, applicable in European Union. It is concluded Machine Learning could improve the application of nanotechnology food regulation, especially methods such as Perturbation Theory Machine Learning (PTML), given that it is aligned with principles promoted by the standards of Organization for Economic Co-operation and Development, European Union regulations and European Food Safety Authority. To our best knowledge this is the first study focused on nanotechnology food regulation and it can help to support technical European Food Safety Authority Opinions for complementary information.

Santana Ricardo, Onieva Enrique, Zuluaga Robin, Duardo-Sánchez Aliuska, Gañán Piedad

2019-Dec-05

Cheminformatic, Nanotechnology, Regulation, Safety, Toxicity

General General

RCorp: a resource for chemical disease semantic extraction in Chinese.

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

BACKGROUND : To robustly identify synergistic combinations of drugs, high-throughput screenings are desirable. It will be of great help to automatically identify the relations in the published papers with machine learning based tools. To support the chemical disease semantic relation extraction especially for chronic diseases, a chronic disease specific corpus for combination therapy discovery in Chinese (RCorp) is manually annotated.

METHODS : In this study, we extracted abstracts from a Chinese medical literature server and followed the annotation framework of the BioCreative CDR corpus, with the guidelines modified to make the combination therapy related relations available. An annotation tool was incorporated to the standard annotation process.

RESULTS : The resulting RCorp consists of 339 Chinese biomedical articles with 2367 annotated chemicals, 2113 diseases, 237 symptoms, 164 chemical-induce-disease relations, 163 chemical-induce-symptom relations, and 805 chemical-treat-disease relations. Each annotation includes both the mention text spans and normalized concept identifiers. The corpus gets an inter-annotator agreement score of 0.883 for chemical entities, 0.791 for disease entities which are measured by F score. And the F score for chemical-treat-disease relations gets 0.788 after unifying the entity mentions.

CONCLUSIONS : We extracted and manually annotated a chronic disease specific corpus for combination therapy discovery in Chinese. The result analysis of the corpus proves its quality for the combination therapy related knowledge discovery task. Our annotated corpus would be a useful resource for the modelling of entity recognition and relation extraction tools. In the future, an evaluation based on the corpus will be held.

Sun Yueping, Hou Li, Qin Lu, Liu Yan, Li Jiao, Qian Qing

2019-Dec-05

Chemical-disease relations, Chronic diseases, Combination therapy, Corpus annotation, Relation extraction

Radiology Radiology

Usefulness of a deep learning system for diagnosing Sjögren's syndrome using ultrasonography images.

In Dento maxillo facial radiology ; h5-index 0.0

OBJECTIVES : We evaluated the diagnostic performance of a deep learning system for the detection of Sjögren's syndrome (SjS) in ultrasonography (US) images, and compared it with the performance of inexperienced radiologists.

METHODS : One hundred patients with a confirmed diagnosis of SjS according to both the Japanese criteria and American-European Consensus Group criteria and 100 non-SjS patients that had a dry mouth and suspected SjS but were definitively diagnosed as non-SjS were enrolled in this study. All the patients underwent US scans of both the parotid glands (PG) and submandibular glands (SMG). The training group consisted of 80 SjS patients and 80 non-SjS patients, whereas the test group consisted of 20 SjS patients and 20 non-SjS patients for deep learning analysis. The performance of the deep learning system for diagnosing SjS from the US images was compared with the diagnoses made by three inexperienced radiologists.

RESULTS : The accuracy, sensitivity, and specificity of the deep learning system for the PG were 89.5%, 90.0%, and 89.0%, respectively, and those for the inexperienced radiologists were 76.7%, 67.0%, and 86.3%, respectively. The deep learning system results for the SMG were 84.0%, 81.0%, and 87.0%, respectively, and those for the inexperienced radiologists were 72.0%, 78.0%, and 66.0%, respectively. The area under the curve for the inexperienced radiologists was significantly different from that of the deep learning system.

CONCLUSIONS : The deep learning system had a high diagnostic ability for SjS. This suggests that deep learning could be used for diagnostic support when interpreting US images.

Kise Yoshitaka, Shimizu Mayumi, Ikeda Haruka, Fujii Takeshi, Kuwada Chiaki, Nishiyama Masako, Funakoshi Takuma, Ariji Yoshiko, Fujita Hiroshi, Katsumata Akitoshi, Yoshiura Kazunori, Ariji Eiichiro

2019-Dec-05

“Sjögrens syndrome”, deep learning, ultrasonography

General General

Prediction of hospital no-show appointments through artificial intelligence algorithms.

In Annals of Saudi medicine ; h5-index 0.0

BACKGROUND : No-shows, a major issue for healthcare centers, can be quite costly and disruptive. Capacity is wasted and expensive resources are underutilized. Numerous studies have shown that reducing uncancelled missed appointments can have a tremendous impact, improving efficiency, reducing costs and improving patient outcomes. Strategies involving machine learning and artificial intelligence could provide a solution.

OBJECTIVE : Use artificial intelligence to build a model that predicts no-shows for individual appointments.

DESIGN : Predictive modeling.

SETTING : Major tertiary care center.

PATIENTS AND METHODS : All historic outpatient clinic scheduling data in the electronic medical record for a one-year period between 01 January 2014 and 31 December 2014 were used to independently build predictive models with JRip and Hoeffding tree algorithms.

MAIN OUTCOME MEASURES : No show appointments.

SAMPLE SIZE : 1 087 979 outpatient clinic appointments.

RESULTS : The no show rate was 11.3% (123 299). The most important information-gain ranking for predicting no-shows in descending order were history of no shows (0.3596), appointment location (0.0323), and specialty (0.025). The following had very low information-gain ranking: age, day of the week, slot description, time of appointment, gender and nationality. Both JRip and Hoeffding algorithms yielded a reasonable degrees of accuracy 76.44% and 77.13%, respectively, with area under the curve indices at acceptable discrimination power for JRip at 0.776 and at 0.861 with excellent discrimination for Hoeffding trees.

CONCLUSION : Appointments having high risk of no-shows can be predicted in real-time to set appropriate proactive interventions that reduce the negative impact of no-shows.

LIMITATIONS : Single center. Only one year of data.

CONFLICT OF INTEREST : None.

AlMuhaideb Sarab, Alswailem Osama, Alsubaie Nayef, Ferwana Ibtihal, Alnajem Afnan

General General

Direct and indirect effects of mindfulness, PTSD, and depression on self-stigma of mental illness in OEF/OIF veterans.

In Psychological trauma : theory, research, practice and policy ; h5-index 0.0

OBJECTIVE : Two of the most common and costly mental health diagnoses among military veterans who served in the post-9/11 conflicts in Afghanistan and Iraq are posttraumatic stress disorder (PTSD) and depression, but over half of veterans who screen positive for these problems do not seek treatment. A key barrier is self-stigma of mental illness. Mindfulness has shown promise as an explanatory variable in the context of mental health symptoms and self-stigma, but these associations are underexplored in the veterans' literature. This study examines direct and indirect effects among mindfulness, PTSD and depression, and self-stigma in post-9/11-era military veterans.

METHOD : A sample of 577 veterans from 3 large American cities completed surveys capturing mindfulness, symptoms of PTSD and depression, and self-stigma. A structural equation modeling approach was used to examine direct and indirect effects among study variables.

RESULTS : Mindfulness was associated with less PTSD and depression and indirectly with less self-stigma through the PTSD pathway. PTSD was associated with more depression and self-stigma, and depression was not significantly associated with self-stigma.

CONCLUSION : PTSD is strongly associated with self-stigma in military veterans, many of whom do not seek mental health treatment. Findings show that mindfulness is a promising intervention target for reducing symptoms of PTSD directly and reducing associated self-stigma of mental illness indirectly. Additional investigation of links between mindfulness, PTSD and depressive symptoms, and self-stigma in military veterans is warranted. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Barr Nicholas, Davis Jordan P, Diguiseppi Graham, Keeling Mary, Castro Carl

2019-Dec-05

General General

Machine Learning in Nanoscience: Big Data at Small Scales.

In Nano letters ; h5-index 188.0

Recent advances in machine learning (ML) offer new tools to extract new insights from large data sets and to acquire small data sets more effectively. Researchers in nanoscience are experimenting with these tools to tackle challenges in many fields. In addition to ML's advancement of nanoscience, nanoscience provides the foundation for neuromorphic computing hardware to expand the implementation of ML algorithms. In this mini-review, which is not able to be comprehensive, we highlight some recent efforts to connect the ML and nanoscience communities focusing on three types of interaction: (1) using ML to analyze and extract new information from large nanoscience data sets, (2) applying ML to accelerate materials discovery, including the use of active learning to guide experimental design, and (3) the nanoscience of memristive devices to realize hardware tailored for ML. We conclude with a discussion of challenges and opportunities for future interactions between nanoscience and ML researchers.

Brown Keith A, Brittman Sarah, Maccaferri Nicolò, Jariwala Deep, Celano Umberto

2019-Dec-05

General General

Your evidence? Machine learning algorithms for medical diagnosis and prediction.

In Human brain mapping ; h5-index 0.0

Computer systems for medical diagnosis based on machine learning are not mere science fiction. Despite undisputed potential benefits, such systems may also raise problems. Two (interconnected) issues are particularly significant from an ethical point of view: The first issue is that epistemic opacity is at odds with a common desire for understanding and potentially undermines information rights. The second (related) issue concerns the assignment of responsibility in cases of failure. The core of the two issues seems to be that understanding and responsibility are concepts that are intrinsically tied to the discursive practice of giving and asking for reasons. The challenge is to find ways to make the outcomes of machine learning algorithms compatible with our discursive practice. This comes down to the claim that we should try to integrate discursive elements into machine learning algorithms. Under the title of "explainable AI" initiatives heading in this direction are already under way. Extensive research in this field is needed for finding adequate solutions.

Heinrichs Bert, Eickhoff Simon B

2019-Dec-05

discursive practice, epistemic opacity, explainability, machine learning, medical diagnosis, medical ethics, medical prediction, responsibility, understanding

General General

Deep learning for automated classification and characterization of amorphous materials.

In Soft matter ; h5-index 0.0

It is difficult to quantify structure-property relationships and to identify structural features of complex materials. The characterization of amorphous materials is especially challenging because their lack of long-range order makes it difficult to define structural metrics. In this work, we apply deep learning algorithms to accurately classify amorphous materials and characterize their structural features. Specifically, we show that convolutional neural networks and message passing neural networks can classify two-dimensional liquids and liquid-cooled glasses from molecular dynamics simulations with greater than 0.98 AUC, with no a priori assumptions about local particle relationships, even when the liquids and glasses are prepared at the same inherent structure energy. Furthermore, we demonstrate that message passing neural networks surpass convolutional neural networks in this context in both accuracy and interpretability. We extract a clear interpretation of how message passing neural networks evaluate liquid and glass structures by using a self-attention mechanism. Using this interpretation, we derive three novel structural metrics that accurately characterize glass formation. The methods presented here provide a procedure to identify important structural features in materials that could be missed by standard techniques and give unique insight into how these neural networks process data.

Swanson Kirk, Trivedi Shubhendu, Lequieu Joshua, Swanson Kyle, Kondor Risi

2019-Dec-05

General General

Enhancing monolayer photoluminescence on optical micro/nanofibers for low-threshold lasing.

In Science advances ; h5-index 0.0

Although monolayer transition metal dichalcogenides (TMDs) have direct bandgaps, the low room-temperature photoluminescence quantum yields (QYs), especially under high pump intensity, limit their practical applications. Here, we use a simple photoactivation method to enhance the room-temperature QYs of monolayer MoS2 grown on to silica micro/nanofibers by more than two orders of magnitude in a wide pump dynamic range. The high-density oxygen dangling bonds released from the tapered micro/nanofiber surface are the key to this strong enhancement of QYs. As the pump intensity increases from 10-1 to 104 W cm-2, our photoactivated monolayer MoS2 exhibits QYs from ~30 to 1% while maintaining high environmental stability, allowing direct lasing with greatly reduced thresholds down to 5 W cm-2. Our strategy can be extended to other TMDs and offers a solution to the most challenging problem toward the realization of efficient and stable light emitters at room temperature based on these atomically thin materials.

Liao Feng, Yu Jiaxin, Gu Zhaoqi, Yang Zongyin, Hasan Tawfique, Linghu Shuangyi, Peng Jian, Fang Wei, Zhuang Songlin, Gu Min, Gu Fuxing

2019-Nov

Public Health Public Health

Artificial intelligence enabled healthcare: A hype, hope or harm.

In Journal of family medicine and primary care ; h5-index 0.0

In this paper, we have described the health care problem (maldistribution of doctors) in India. Later, we have introduced the concept of artificial intelligence and we have described this technology with various examples, how it is rapidly changing the health care scenario across the world. We have also described the various advantages of artificial intelligence technology. At the end of the paper, we have raised some serious concerns regarding complete replacement of human based health care technology with artificial intelligence technology. Lastly, we concluded that we have to use artificial intelligent technology to prevent human sufferings/health care problems with proper caution.

Bhattacharya Sudip, Pradhan Keerti Bhusan, Bashar Md Abu, Tripathi Shailesh, Semwal Jayanti, Marzo Roy Rillera, Bhattacharya Sandip, Singh Amarjeet

2019-Nov

Algorithm, artificial intelligence, doctors, healthcare, machine learning

General General

A Non-invasive Radiomic Method Using 18F-FDG PET Predicts Isocitrate Dehydrogenase Genotype and Prognosis in Patients With Glioma.

In Frontiers in oncology ; h5-index 0.0

Purpose: We aimed to analyze 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images via the radiomic method to develop a model and validate the potential value of features reflecting glioma metabolism for predicting isocitrate dehydrogenase (IDH) genotype and prognosis. Methods: PET images of 127 patients were retrospectively analyzed. A series of quantitative features reflecting the metabolic heterogeneity of the tumors were extracted, and a radiomic signature was generated using the support vector machine method. A combined model that included clinical characteristics and the radiomic signature was then constructed by multivariate logistic regression to predict the IDH genotype status, and the model was evaluated and verified by receiver operating characteristic (ROC) curves and calibration curves. Finally, Kaplan-Meier curves and log-rank tests were used to analyze overall survival (OS) according to the predicted result. Results: The generated radiomic signature was significantly associated with IDH genotype (p < 0.05) and could achieve large areas under the ROC curve of 0.911 and 0.900 on the training and validation cohorts, respectively, with the incorporation of age and type of tumor metabolism. The good agreement of the calibration curves in the validation cohort further validated the efficacy of the constructed model. Moreover, the predicted results showed a significant difference in OS between high- and low-risk groups (p < 0.001). Conclusions: Our results indicate that the 18F-FDG metabolism-related features could effectively predict the IDH genotype of gliomas and stratify the OS of patients with different prognoses.

Li Longfei, Mu Wei, Wang Yaning, Liu Zhenyu, Liu Zehua, Wang Yu, Ma Wenbin, Kong Ziren, Wang Shuo, Zhou Xuezhi, Wei Wei, Cheng Xin, Lin Yusong, Tian Jie

2019

18F-FDG PET, glioma, isocitrate dehydrogenase, non-invasive prediction, radiomics

General General

Is it time for artificial intelligence to predict the function of natural products based on 2D-structure.

In MedChemComm ; h5-index 0.0

Currently, there is no established technique that allows the function of a compound produced by nature to be predicted by looking at its 2-dimensional chemical structure. One of chemistry's grand challenges: to find a function for every known metabolite. We explore the opportunity for Artificial Intelligence to provide rationale interrogation of metabolites to predict their function.

Liu Miaomiao, Karuso Peter, Feng Yunjiang, Kellenberger Esther, Liu Fei, Wang Can, Quinn Ronald J

2019-Oct-01

Surgery Surgery

Natural language processing for populating lung cancer clinical research data.

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

BACKGROUND : Lung cancer is the second most common cancer for men and women; the wide adoption of electronic health records (EHRs) offers a potential to accelerate cohort-related epidemiological studies using informatics approaches. Since manual extraction from large volumes of text materials is time consuming and labor intensive, some efforts have emerged to automatically extract information from text for lung cancer patients using natural language processing (NLP), an artificial intelligence technique.

METHODS : In this study, using an existing cohort of 2311 lung cancer patients with information about stage, histology, tumor grade, and therapies (chemotherapy, radiotherapy and surgery) manually ascertained, we developed and evaluated an NLP system to extract information on these variables automatically for the same patients from clinical narratives including clinical notes, pathology reports and surgery reports.

RESULTS : Evaluation showed promising results with the recalls for stage, histology, tumor grade, and therapies achieving 89, 98, 78, and 100% respectively and the precisions were 70, 88, 90, and 100% respectively.

CONCLUSION : This study demonstrated the feasibility and accuracy of automatically extracting pre-defined information from clinical narratives for lung cancer research.

Wang Liwei, Luo Lei, Wang Yanshan, Wampfler Jason, Yang Ping, Liu Hongfang

2019-Dec-05

Histology, Lung cancer, Natural language processing, Stage, Treatments, Tumor grade

Radiology Radiology

FULLY-AUTOMATIC SEGMENTATION OF KIDNEYS IN CLINICAL ULTRASOUND IMAGES USING A BOUNDARY DISTANCE REGRESSION NETWORK.

In Proceedings. IEEE International Symposium on Biomedical Imaging ; h5-index 0.0

It remains challenging to automatically segment kidneys in clinical ultrasound images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic methods have achieved promising performance. In this study, we developed a novel boundary distance regression deep neural network to segment the kidneys, informed by the fact that the kidney boundaries are relatively consistent across images in terms of their appearance. Particularly, we first use deep neural networks pre-trained for classification of natural images to extract high-level image features from ultrasound images, then these feature maps are used as input to learn kidney boundary distance maps using a boundary distance regression network, and finally the predicted boundary distance maps are classified as kidney pixels or non-kidney pixels using a pixel classification network in an end-to-end learning fashion. Experimental results have demonstrated that our method could effectively improve the performance of automatic kidney segmentation, significantly better than deep learning based pixel classification networks.

Yin Shi, Zhang Zhengqiang, Li Hongming, Peng Qinmu, You Xinge, Furth Susan L, Tasian Gregory E, Fan Yong

2019-Apr

Ultrasound imaging, boundary detection, deep learning, fully-automatic segmentation

Radiology Radiology

COLLABORATIVE CLUSTERING OF SUBJECTS AND RADIOMIC FEATURES FOR PREDICTING CLINICAL OUTCOMES OF RECTAL CANCER PATIENTS.

In Proceedings. IEEE International Symposium on Biomedical Imaging ; h5-index 0.0

Most machine learning approaches in radiomics studies ignore the underlying difference of radiomic features computed from heterogeneous groups of patients, and intrinsic correlations of the features are not fully exploited yet. In order to better predict clinical outcomes of cancer patients, we adopt an unsupervised machine learning method to simultaneously stratify cancer patients into distinct risk groups based on their radiomic features and learn low-dimensional representations of the radiomic features for robust prediction of their clinical outcomes. Based on nonnegative matrix tri-factorization techniques, the proposed method applies collaborative clustering to radiomic features of cancer patients to obtain clusters of both the patients and their radiomic features so that patients with distinct imaging patterns are stratified into different risk groups and highly correlated radiomic features are grouped in the same radiomic feature clusters. Experiments on a FDG-PET/CT dataset of rectal cancer patients have demonstrated that the proposed method facilitates better stratification of patients with distinct survival patterns and learning of more effective low-dimensional feature representations that ultimately leads to accurate prediction of patient survival, outperforming conventional methods under comparison.

Liu Hangfan, Li Hongming, Boimel Pamela, Janopaul-Naylor James, Zhong Haoyu, Xiao Ying, Ben-Josef Edgar, Fan Yong

2019-Apr

Collaborative clustering, nonnegative matrix tri-factorization, patient stratification, radiomics, rectal cancer, unsupervised learning

Radiology Radiology

EARLY PREDICTION OF ALZHEIMER'S DISEASE DEMENTIA BASED ON BASELINE HIPPOCAMPAL MRI AND 1-YEAR FOLLOW-UP COGNITIVE MEASURES USING DEEP RECURRENT NEURAL NETWORKS.

In Proceedings. IEEE International Symposium on Biomedical Imaging ; h5-index 0.0

Multi-modal biological, imaging, and neuropsychological markers have demonstrated promising performance for distinguishing Alzheimer's disease (AD) patients from cognitively normal elders. However, it remains difficult to early predict when and which mild cognitive impairment (MCI) individuals will convert to AD dementia. Informed by pattern classification studies which have demonstrated that pattern classifiers built on longitudinal data could achieve better classification performance than those built on cross-sectional data, we develop a deep learning model based on recurrent neural networks (RNNs) to learn informative representation and temporal dynamics of longitudinal cognitive measures of individual subjects and combine them with baseline hippocampal MRI for building a prognostic model of AD dementia progression. Experimental results on a large cohort of MCI subjects have demonstrated that the deep learning model could learn informative measures from longitudinal data for characterizing the progression of MCI subjects to AD dementia, and the prognostic model could early predict AD progression with high accuracy.

Li Hongming, Fan Yong

2019-Apr

Alzheimer’s disease, Prognosis, longitudinal data, recurrent neural networks

Radiology Radiology

Quantifying the Metabolic Signature of Multiple Sclerosis by in vivo Proton Magnetic Resonance Spectroscopy: Current Challenges and Future Outlook in the Translation From Proton Signal to Diagnostic Biomarker.

In Frontiers in neurology ; h5-index 0.0

Proton magnetic resonance spectroscopy (1H-MRS) offers a growing variety of methods for querying potential diagnostic biomarkers of multiple sclerosis in living central nervous system tissue. For the past three decades, 1H-MRS has enabled the acquisition of a rich dataset suggestive of numerous metabolic alterations in lesions, normal-appearing white matter, gray matter, and spinal cord of individuals with multiple sclerosis, but this body of information is not free of seeming internal contradiction. The use of 1H-MRS signals as diagnostic biomarkers depends on reproducible and generalizable sensitivity and specificity to disease state that can be confounded by a multitude of influences, including experiment group classification and demographics; acquisition sequence; spectral quality and quantifiability; the contribution of macromolecules and lipids to the spectroscopic baseline; spectral quantification pipeline; voxel tissue and lesion composition; T1 and T2 relaxation; B1 field characteristics; and other features of study design, spectral acquisition and processing, and metabolite quantification about which the experimenter may possess imperfect or incomplete information. The direct comparison of 1H-MRS data from individuals with and without multiple sclerosis poses a special challenge in this regard, as several lines of evidence suggest that experimental cohorts may differ significantly in some of these parameters. We review the existing findings of in vivo1H-MRS on central nervous system metabolic abnormalities in multiple sclerosis and its subtypes within the context of study design, spectral acquisition and processing, and metabolite quantification and offer an outlook on technical considerations, including the growing use of machine learning, by future investigations into diagnostic biomarkers of multiple sclerosis measurable by 1H-MRS.

Swanberg Kelley M, Landheer Karl, Pitt David, Juchem Christoph

2019

absolute quantification, biomarker, in vivo proton magnetic resonance spectroscopy 1H-MRS, longitudinal relaxation T1, macromolecules, multiple sclerosis, spectroscopic baseline, transverse relaxation T2