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General General

Regular combined training and vitamins modulated the apoptosis process in diabetic rats: Bioinformatics analysis of heart failure's differential genes expression network correlated with anti-apoptotic process.

In Journal of food biochemistry

The apoptosis process could impose significantly by hyperglycemia. According to in silico language processing and high throughput raw data analysis, we recognized hub molecular mechanisms involved in the pathogenesis of diabetic hearts and suggested a new pharmaceutical approach for declining myocardial programed cell death. Fifty male Sprague-Dawley rats were classified into five groups: healthy rats as control, diabetic rats, diabetic combined resistance/endurance training, diabetic rats which consumed supplementation vitamins E and C, and the combined supplementation and training. Here, we calculated changes in gene expression based on artificial intelligence methods and evaluated gene expression in apoptotic influencing combined training and antioxidants vitamins consumption in heart injured models by streptozotocin via Real-Time PCR. Moreover, we assessed the binding affinity of the 3D structure of small molecules on macromolecule SIRT3 to a new compound pharmaceutical suggesting the decline in cell death program. The computational intelligence surveys revealed that the apoptosis process was a remarkable pathomechanism in the abnormality function of heart tissue in diabetic conditions. Furthermore, we showed that synchronizing antioxidant vitamin consumption and regular combined training could significantly decrease irreversible myocardial cell death in diabetic myocardiopathy. Hence, levels of antiapoptotic mRNA were modified in the combined training/vitamin consumption group compared with other classifications. We found that regular combined exercise and vitamin consumption could reverse the apoptosis process to enhance the survival of cardiac muscle cells in diabetes conditions. PRACTICAL APPLICATIONS: Machine learning and system biology indicated that the apoptosis process is a vital pathomechanism of hyperglycemia-induced heart failure. Sirt3/Fas/Bcl-2/Cycs and Bax, as a critical network of apoptosis, play an essential role in heart failure induced by hyperglycemia. Moreover, Type 2 diabetes and obesity increase the risk of heart failure by increasing high blood sugar levels. We calculated the binding power of the vitamins E and C on SIRT3 protein based on the drug software. In addition, this study assessed that regular combined training and vitamin consumption had an antiapoptotic effect. Also, our data might improve the hyperglycemia state.

Heydarnia Elaheh, Taghian Farzaneh, Jalali Dehkordi Khosro, Moghadasi Mehrzad

2022-Jul-02

antioxidant supplement, apoptosis markers, combined exercise, streptozotocin-induced diabetic rats

General General

Biomarkers of mitochondrial origin: a futuristic cancer diagnostic.

In Integrative biology : quantitative biosciences from nano to macro

Cancer is a highly fatal disease without effective early-stage diagnosis and proper treatment. Along with the oncoproteins and oncometabolites, several organelles from cancerous cells are also emerging as potential biomarkers. Mitochondria isolated from cancer cells are one such biomarker candidates. Cancerous mitochondria exhibit different profiles compared with normal ones in morphology, genomic, transcriptomic, proteomic and metabolic landscape. Here, the possibilities of exploring such characteristics as potential biomarkers through single-cell omics and Artificial Intelligence (AI) are discussed. Furthermore, the prospects of exploiting the biomarker-based diagnosis and its futuristic utilization through circulatory tumor cell technology are analyzed. A successful alliance of circulatory tumor cell isolation protocols and a single-cell omics platform can emerge as a next-generation diagnosis and personalized treatment procedure.

Gayan Sukanya, Joshi Gargee, Dey Tuli

2022-Jul-02

cancer biomarker, circulatory tumor cell, mitochondria, mtDNA, single-cell omics

General General

Machine learning for prediction of schizophrenia using genetic and demographic factors in the UK biobank.

In Schizophrenia research ; h5-index 61.0

Machine learning (ML) holds promise for precision psychiatry, but its predictive performance is unclear. We assessed whether ML provided added value over logistic regression for prediction of schizophrenia, and compared models built using polygenic risk scores (PRS) or clinical/demographic factors. LASSO and ridge-penalised logistic regression, support vector machines (SVM), random forests, boosting, neural networks and stacked models were trained to predict schizophrenia, using PRS for schizophrenia (PRSSZ), sex, parental depression, educational attainment, winter birth, handedness and number of siblings as predictors. Models were evaluated for discrimination using area under the receiver operator characteristic curve (AUROC) and relative importance of predictors using permutation feature importance (PFI). In a secondary analysis, fitted models were tested for association with schizophrenia-related traits which had not been used in model development. Following learning curve analysis, 738 cases and 3690 randomly sampled controls were selected from the UK Biobank. ML models combining all predictors showed the highest discrimination (linear SVM, AUROC = 0.71), but did not significantly outperform logistic regression. AUROC was robust over 100 random resamples of controls. PFI identified PRSSZ as the most important predictor. Highest variance in fitted models was explained by schizophrenia-related traits including fluid intelligence (most associated: linear SVM), digit symbol substitution (RBF SVM), BMI (XGBoost), smoking status (XGBoost) and deprivation (linear SVM). In conclusion, ML approaches did not provide substantial added value for prediction of schizophrenia over logistic regression, as indexed by AUROC; however, risk scores derived with different ML approaches differ with respect to association with schizophrenia-related traits.

Bracher-Smith Matthew, Rees Elliott, Menzies Georgina, Walters James T R, O’Donovan Michael C, Owen Michael J, Kirov George, Escott-Price Valentina

2022-Jun-29

Machine learning, Polygenic risk scores, Precision psychiatry, Schizophrenia

General General

Elevated blood pressure is associated with advanced brain aging in mid-life: A 30-year follow-up of The CARDIA Study.

In Alzheimer's & dementia : the journal of the Alzheimer's Association

BACKGROUND : High blood pressure (BP) is a risk factor for late-life brain health; however, the association of elevated BP with brain health in mid-life is unclear.

METHODS : We identified 661 participants from the Coronary Artery Risk Development in Young Adults Study (age 18-30 at baseline) with 30 years of follow-up and brain magnetic resonance imaging at year 30. Cumulative exposure of BP was estimated by time-weighted averages (TWA). Ideal cardiovascular health was defined as systolic BP < 120 mm Hg, diastolic BP < 80 mm Hg. Brain age was calculated using previously validated high dimensional machine learning pattern analyses.

RESULTS : Every 5 mmHg increment in TWA systolic BP was associated with approximately 1-year greater brain age (95% confidence interval [CI]: 0.50-1.36) Participants with TWA systolic or diastolic BP over the recommended guidelines for ideal cardiovascular health, had on average 3-year greater brain age (95% CI: 1.00-4.67; 95% CI: 1.45-5.13, respectively).

CONCLUSION : Elevated BP from early to mid adulthood, even below clinical cut-offs, is associated with advanced brain aging in mid-life.

Dintica Christina S, Habes Mohamad, Erus Guray, Vittinghoff Eric, Davatzikos Christos, Nasrallah Ilya M, Launer Lenore J, Sidney Stephen, Yaffe Kristine

2022-Jul-02

blood pressure, brain age, brain imaging, cognition, longitudinal, magnetic resonance imaging, mid-life, risk factors

General General

Machine learning models in the prediction of 1-year mortality in patients with advanced hepatocellular cancer on immunotherapy: a proof-of-concept study.

In Hepatology international

INTRODUCTION : Immunotherapy is a new promising treatment for patients with advanced hepatocellular carcinoma (HCC), but is costly and potentially associated with considerable side effects. This study aimed to evaluate the role of machine learning (ML) models in predicting the 1-year cancer-related mortality in advanced HCC patients treated with immunotherapy.

METHOD : 395 HCC patients who had received immunotherapy (including nivolumab, pembrolizumab or ipilimumab) between 2014 and 2019 in Hong Kong were included. The whole data sets were randomly divided into training (n = 316) and internal validation (n = 79) set. The data set, including 47 clinical variables, was used to construct six different ML models in predicting the risk of 1-year mortality. The performances of ML models were measured by the area under receiver operating characteristic curve (AUC) and their performances were compared with C-Reactive protein and Alpha Fetoprotein in ImmunoTherapY score (CRAFITY) and albumin-bilirubin (ALBI) score. The ML models were further validated with an external cohort between 2020 and 2021.

RESULTS : The 1-year cancer-related mortality was 51.1%. Of the six ML models, the random forest (RF) has the highest AUC of 0.92 (95% CI 0.87-0.98), which was better than logistic regression (0.82, p = 0.01) as well as the CRAFITY (0.68, p < 0.01) and ALBI score (0.84, p = 0.04). RF had the lowest false positive (2.0%) and false negative rate (5.2%), and performed better than CRAFITY score in the external validation cohort (0.91 vs 0.66, p < 0.01). High baseline AFP, bilirubin and alkaline phosphatase were three common risk factors identified by all ML models.

CONCLUSION : ML models could predict 1-year cancer-related mortality in HCC patients treated with immunotherapy, which may help to select patients who would benefit from this treatment.

Lui Thomas Ka Luen, Cheung Ka Shing, Leung Wai Keung

2022-Jul-02

Artificial intelligence, Gradient boosting, Hepatocellular carcinoma, Immunotherapy, Ipilimumab, Machine learning, Mortality, Nivolumab, Pembrolizumab, Random forest

Radiology Radiology

Multi-center validation of an artificial intelligence system for detection of COVID-19 on chest radiographs in symptomatic patients.

In European radiology ; h5-index 62.0

OBJECTIVES : While chest radiograph (CXR) is the first-line imaging investigation in patients with respiratory symptoms, differentiating COVID-19 from other respiratory infections on CXR remains challenging. We developed and validated an AI system for COVID-19 detection on presenting CXR.

METHODS : A deep learning model (RadGenX), trained on 168,850 CXRs, was validated on a large international test set of presenting CXRs of symptomatic patients from 9 study sites (US, Italy, and Hong Kong SAR) and 2 public datasets from the US and Europe. Performance was measured by area under the receiver operator characteristic curve (AUC). Bootstrapped simulations were performed to assess performance across a range of potential COVID-19 disease prevalence values (3.33 to 33.3%). Comparison against international radiologists was performed on an independent test set of 852 cases.

RESULTS : RadGenX achieved an AUC of 0.89 on 4-fold cross-validation and an AUC of 0.79 (95%CI 0.78-0.80) on an independent test cohort of 5,894 patients. Delong's test showed statistical differences in model performance across patients from different regions (p < 0.01), disease severity (p < 0.001), gender (p < 0.001), and age (p = 0.03). Prevalence simulations showed the negative predictive value increases from 86.1% at 33.3% prevalence, to greater than 98.5% at any prevalence below 4.5%. Compared with radiologists, McNemar's test showed the model has higher sensitivity (p < 0.001) but lower specificity (p < 0.001).

CONCLUSION : An AI model that predicts COVID-19 infection on CXR in symptomatic patients was validated on a large international cohort providing valuable context on testing and performance expectations for AI systems that perform COVID-19 prediction on CXR.

KEY POINTS : • An AI model developed using CXRs to detect COVID-19 was validated in a large multi-center cohort of 5,894 patients from 9 prospectively recruited sites and 2 public datasets. • Differences in AI model performance were seen across region, disease severity, gender, and age. • Prevalence simulations on the international test set demonstrate the model's NPV is greater than 98.5% at any prevalence below 4.5%.

Kuo Michael D, Chiu Keith W H, Wang David S, Larici Anna Rita, Poplavskiy Dmytro, Valentini Adele, Napoli Alessandro, Borghesi Andrea, Ligabue Guido, Fang Xin Hao B, Wong Hing Ki C, Zhang Sailong, Hunter John R, Mousa Abeer, Infante Amato, Elia Lorenzo, Golemi Salvatore, Yu Leung Ho P, Hui Christopher K M, Erickson Bradley J

2022-Jul-02

Artificial intelligence, COVID-19, Public health, Radiology, Thoracic

General General

Impact of a content-based image retrieval system on the interpretation of chest CTs of patients with diffuse parenchymal lung disease.

In European radiology ; h5-index 62.0

OBJECTIVES : Content-based image retrieval systems (CBIRS) are a new and potentially impactful tool for radiological reporting, but their clinical evaluation is largely missing. This study aimed at assessing the effect of CBIRS on the interpretation of chest CT scans from patients with suspected diffuse parenchymal lung disease (DPLD).

MATERIALS AND METHODS : A total of 108 retrospectively included chest CT scans with 22 unique, clinically and/or histopathologically verified diagnoses were read by eight radiologists (four residents, four attending, median years reading chest CT scans 2.1± 0.7 and 12 ± 1.8, respectively). The radiologists read and provided the suspected diagnosis at a certified radiological workstation to simulate clinical routine. Half of the readings were done without CBIRS and half with the additional support of the CBIRS. The CBIRS retrieved the most likely of 19 lung-specific patterns from a large database of 6542 thin-section CT scans and provided relevant information (e.g., a list of potential differential diagnoses).

RESULTS : Reading time decreased by 31.3% (p < 0.001) despite the radiologists searching for additional information more frequently when the CBIRS was available (154 [72%] vs. 95 [43%], p < 0.001). There was a trend towards higher overall diagnostic accuracy (42.2% vs 34.7%, p = 0.083) when the CBIRS was available.

CONCLUSION : The use of the CBIRS had a beneficial impact on the reading time of chest CT scans in cases with DPLD. In addition, both resident and attending radiologists were more likely to consult informational resources if they had access to the CBIRS. Further studies are needed to confirm the observed trend towards increased diagnostic accuracy with the use of a CBIRS in practice.

KEY POINTS : • A content-based image retrieval system for supporting the diagnostic process of reading chest CT scans can decrease reading time by 31.3% (p < 0.001). • The decrease in reading time was present despite frequent usage of the content-based image retrieval system. • Additionally, a trend towards higher diagnostic accuracy was observed when using the content-based image retrieval system (42.2% vs 34.7%, p = 0.083).

Röhrich Sebastian, Heidinger Benedikt H, Prayer Florian, Weber Michael, Krenn Markus, Zhang Rui, Sufana Julie, Scheithe Jakob, Kanbur Incifer, Korajac Aida, Pötsch Nina, Raudner Marcus, Al-Mukhtar Ali, Fueger Barbara J, Milos Ruxandra-Iulia, Scharitzer Martina, Langs Georg, Prosch Helmut

2022-Jul-02

Artificial intelligence, Diagnosis, Computer assisted, Lung diseases, Interstitial, Tomography, X-ray computed

General General

Severe asthma and personalized approach in the choice of biologic.

In Current opinion in allergy and clinical immunology

PURPOSE OF REVIEW : Severe asthma requires intensive pharmacological treatment to achieve disease control. Oral corticosteroids are effective, but their use is burdened with important side effects. Biologics targeting the specific inflammatory pathways underpinning the disease have been shown to be effective but not all patients respond equally well. As we treat more patients than those who can respond, our inability to predict responders has important healthcare costs considering that biologics are expensive drugs. Thus, a more precise choice of the 'right patients' to be prescribed with the 'right biologics' would be desirable.

RECENT FINDINGS : Machine learning techniques showed that it is possible to increase our ability to predict outcomes in patients treated with biologics. Recently, we identified by cluster analysis four different clusters within the T2 high phenotype with differential benralizumab response. Two of these clusters, characterized by higher levels of inflammatory markers, showed the highest response rate (80-90%).

SUMMARY : Machine learning holds promise for asthma research enabling us to predict which patients will respond to which drug. These techniques can facilitate the diagnostic workflow and increase the chance of selecting the more appropriate treatment option for the individual patient, enhancing patient care and satisfaction.

Di Bona Danilo, Spataro Federico, Carlucci Palma, Paoletti Giovanni, Canonica Giorgio W

2022-Jul-04

General General

Human age reversal: Fact or fiction?

In Aging cell ; h5-index 58.0

Although chronological age correlates with various age-related diseases and conditions, it does not adequately reflect an individual's functional capacity, well-being, or mortality risk. In contrast, biological age provides information about overall health and indicates how rapidly or slowly a person is aging. Estimates of biological age are thought to be provided by aging clocks, which are computational models (e.g., elastic net) that use a set of inputs (e.g., DNA methylation sites) to make a prediction. In the past decade, aging clock studies have shown that several age-related diseases, social variables, and mental health conditions associate with an increase in predicted biological age relative to chronological age. This phenomenon of age acceleration is linked to a higher risk of premature mortality. More recent research has demonstrated that predicted biological age is sensitive to specific interventions. Human trials have reported that caloric restriction, a plant-based diet, lifestyle changes involving exercise, a drug regime including metformin, and vitamin D3 supplementation are all capable of slowing down or reversing an aging clock. Non-interventional studies have connected high-quality sleep, physical activity, a healthy diet, and other factors to age deceleration. Specific molecules have been associated with the reduction or reversal of predicted biological age, such as the antihypertensive drug doxazosin or the metabolite alpha-ketoglutarate. Although rigorous clinical trials are needed to validate these initial findings, existing data suggest that aging clocks are malleable in humans. Additional research is warranted to better understand these computational models and the clinical significance of lowering or reversing their outputs.

Johnson Adiv A, English Bradley W, Shokhirev Maxim N, Sinclair David A, Cuellar Trinna L

2022-Jul-02

aging clock, biological age, epigenetic age, healthspan, lifespan, longevity, machine learning, mortality

General General

Applicability of probabilistic graphical models for early detection of SARS-CoV-2 reactive antibodies after SARS-CoV-2 vaccination in hematological patients.

In Annals of hematology ; h5-index 39.0

Prior studies of antibody response after full SARS-CoV-2 vaccination in hematological patients have confirmed lower antibody levels compared to the general population. Serological response in hematological patients varies widely according to the disease type and its status, and the treatment given and its timing with respect to vaccination. Through probabilistic machine learning graphical models, we estimated the conditional probabilities of having detectable anti-SARS-CoV-2 antibodies at 3-6 weeks after SARS-CoV-2 vaccination in a large cohort of patients with several hematological diseases (n= 1166). Most patients received mRNA-based vaccines (97%), mainly Moderna® mRNA-1273 (74%) followed by Pfizer-BioNTech® BNT162b2 (23%). The overall antibody detection rate at 3 to 6 weeks after full vaccination for the entire cohort was 79%. Variables such as type of disease, timing of anti-CD20 monoclonal antibody therapy, age, corticosteroids therapy, vaccine type, disease status, or prior infection with SARS-CoV-2 are among the most relevant conditions influencing SARS-CoV-2-IgG-reactive antibody detection. A lower probability of having detectable antibodies was observed in patients with B-cell non-Hodgkin's lymphoma treated with anti-CD20 monoclonal antibodies within 6 months before vaccination (29.32%), whereas the highest probability was observed in younger patients with chronic myeloproliferative neoplasms (99.53%). The Moderna® mRNA-1273 compound provided higher probabilities of antibody detection in all scenarios. This study depicts conditional probabilities of having detectable antibodies in the whole cohort and in specific scenarios such as B cell NHL, CLL, MM, and cMPN that may impact humoral responses. These results could be useful to focus on additional preventive and/or monitoring interventions in these highly immunosuppressed hematological patients.

Piñana José Luis, Rodríguez-Belenguer Pablo, Caballero Dolores, Martino Rodrigo, Lopez-Corral Lucia, Terol María-José, Vazquez Lourdes, Calabuig Marisa, Sanz-Linares Gabriela, Marin-Jimenez Francisca, Alonso Carmen, Montoro Juan, Ferrer Elena, Facal Ana, Pascual María-Jesús, Rodriguez-Fernandez Alicia, Olave María T, Cascales-Hernandez Almudena, Gago Beatriz, Hernández-Rivas José-Ángel, Villalon Lucia, Corona Magdalena, Roldán-Pérez Alicia, Ribes-Amoros Julia, González-Santillana Clara, Garcia-Sanz Ramon, Navarro David, Serrano-López Antonio J, Cedillo Ángel, Soria-Olivas Emilio, Sureda Anna, Solano Carlos

2022-Jul-02

Allogeneic stem cell transplantation, Autologous stem cell transplantation, Bayesian Networks, CAR-T therapy, COVID-19, Chronic lymphocytic leukemia, Hematological malignancies, Immunocompromised patients, Moderna mRNA-1273, Non-Hodgkin lymphoma, Pfizer-BioNTech BNT162b2, Probabilistic graphical models, Respiratory virus, SARS-CoV-2 vaccines, mRNA vaccine

General General

eDNAssay: a machine learning tool that accurately predicts qPCR cross-amplification.

In Molecular ecology resources

Environmental DNA (eDNA) sampling is a highly sensitive and cost-effective technique for wildlife monitoring, notably through the use of qPCR assays. However, it can be difficult to ensure assay specificity when many closely related species cooccur. In theory, specificity may be assessed in silico by determining whether assay oligonucleotides have enough base-pair mismatches with nontarget sequences to preclude amplification. However, the mismatch qualities required are poorly understood, making in silico assessments difficult and often necessitating extensive in vitro testing-typically the greatest bottleneck in assay development. Increasing the accuracy of in silico assessments would therefore streamline the assay development process. In this study, we paired 10 qPCR assays with 82 synthetic gene fragments for 530 specificity tests using SYBR Green intercalating dye (n = 262) and TaqMan hydrolysis probes (n = 268). Test results were used to train random forest classifiers to predict amplification. The primer-only model (SYBR Green-based) and full-assay model (TaqMan probe-based) were 99.6% and 100% accurate, respectively, in cross-validation. We further assessed model performance using six independent assays not used in model training. In these tests the primer-only model was 92.4% accurate (n = 119) and the full-assay model was 96.5% accurate (n = 144). The high performance achieved by these models makes it possible for eDNA practitioners to more quickly and confidently develop assays specific to the intended target. Practitioners can access the full-assay model via eDNAssay (https://NationalGenomicsCenter.shinyapps.io/eDNAssay), a user-friendly online tool for predicting qPCR cross-amplification.

Kronenberger J A, Wilcox T M, Mason D H, Franklin T W, McKelvey K S, Young M K, Schwartz M K

2022-Jul-01

assay, base-pair mismatches, eDNA, environmental DNA, random forest, specificity

Radiology Radiology

Multi-branch fusion auxiliary learning for the detection of pneumonia from chest X-ray images.

In Computers in biology and medicine

Lung infections caused by bacteria and viruses are infectious and require timely screening and isolation, and different types of pneumonia require different treatment plans. Therefore, finding a rapid and accurate screening method for lung infections is critical. To achieve this goal, we proposed a multi-branch fusion auxiliary learning (MBFAL) method for pneumonia detection from chest X-ray (CXR) images. The MBFAL method was used to perform two tasks through a double-branch network. The first task was to recognize the absence of pneumonia (normal), COVID-19, other viral pneumonia and bacterial pneumonia from CXR images, and the second task was to recognize the three types of pneumonia from CXR images. The latter task was used to assist the learning of the former task to achieve a better recognition effect. In the process of auxiliary parameter updating, the feature maps of different branches were fused after sample screening through label information to enhance the model's ability to recognize case of pneumonia without impacting its ability to recognize normal cases. Experiments show that an average classification accuracy of 95.61% is achieved using MBFAL. The single class accuracy for normal, COVID-19, other viral pneumonia and bacterial pneumonia was 98.70%, 99.10%, 96.60% and 96.80%, respectively, and the recall was 97.20%, 98.60%, 96.10% and 89.20%, respectively, using the MBFAL method. Compared with the baseline model and the model constructed using the above methods separately, better results for the rapid screening of pneumonia were achieved using MBFAL.

Liu Jia, Qi Jing, Chen Wei, Nian Yongjian

2022-Jun-15

Auxiliary learning, Deep learning, Feature fusion, Multi-task learning, Pneumonia

General General

Machine learning-based time series models for effective CO2 emission prediction in India.

In Environmental science and pollution research international

China, India, and the USA are the countries with the highest energy consumption and CO2 emissions globally. As per the report of datacommons.org , CO2 emission in India is 1.80 metric tons per capita, which is harmful to living beings, so this paper presents India's detrimental CO2 emission effect with the prediction of CO2 emission for the next 10 years based on univariate time-series data from 1980 to 2019. We have used three statistical models; autoregressive-integrated moving average (ARIMA) model, seasonal autoregressive-integrated moving average with exogenous factors (SARIMAX) model, and the Holt-Winters model, two machine learning models, i.e., linear regression and random forest model and a deep learning-based long short-term memory (LSTM) model. This paper brings together a variety of models and allows us to work on data prediction. The performance analysis shows that LSTM, SARIMAX, and Holt-Winters are the three most accurate models among the six models based on nine performance metrics. Results conclude that LSTM is the best model for CO2 emission prediction with the 3.101% MAPE value, 60.635 RMSE value, 28.898 MedAE value, and along with other performance metrics. A comparative study also concludes the same. Therefore, the deep learning-based LSTM model is suggested as one of the most appropriate models for CO2 emission prediction.

Kumari Surbhi, Singh Sunil Kumar

2022-Jul-02

Air pollution, CO 2 emissions, Holt-Winters, LSTM, Linear regression, Random forest regressor, Time series forecasting

General General

Applicability of probabilistic graphical models for early detection of SARS-CoV-2 reactive antibodies after SARS-CoV-2 vaccination in hematological patients.

In Annals of hematology ; h5-index 39.0

Prior studies of antibody response after full SARS-CoV-2 vaccination in hematological patients have confirmed lower antibody levels compared to the general population. Serological response in hematological patients varies widely according to the disease type and its status, and the treatment given and its timing with respect to vaccination. Through probabilistic machine learning graphical models, we estimated the conditional probabilities of having detectable anti-SARS-CoV-2 antibodies at 3-6 weeks after SARS-CoV-2 vaccination in a large cohort of patients with several hematological diseases (n= 1166). Most patients received mRNA-based vaccines (97%), mainly Moderna® mRNA-1273 (74%) followed by Pfizer-BioNTech® BNT162b2 (23%). The overall antibody detection rate at 3 to 6 weeks after full vaccination for the entire cohort was 79%. Variables such as type of disease, timing of anti-CD20 monoclonal antibody therapy, age, corticosteroids therapy, vaccine type, disease status, or prior infection with SARS-CoV-2 are among the most relevant conditions influencing SARS-CoV-2-IgG-reactive antibody detection. A lower probability of having detectable antibodies was observed in patients with B-cell non-Hodgkin's lymphoma treated with anti-CD20 monoclonal antibodies within 6 months before vaccination (29.32%), whereas the highest probability was observed in younger patients with chronic myeloproliferative neoplasms (99.53%). The Moderna® mRNA-1273 compound provided higher probabilities of antibody detection in all scenarios. This study depicts conditional probabilities of having detectable antibodies in the whole cohort and in specific scenarios such as B cell NHL, CLL, MM, and cMPN that may impact humoral responses. These results could be useful to focus on additional preventive and/or monitoring interventions in these highly immunosuppressed hematological patients.

Piñana José Luis, Rodríguez-Belenguer Pablo, Caballero Dolores, Martino Rodrigo, Lopez-Corral Lucia, Terol María-José, Vazquez Lourdes, Calabuig Marisa, Sanz-Linares Gabriela, Marin-Jimenez Francisca, Alonso Carmen, Montoro Juan, Ferrer Elena, Facal Ana, Pascual María-Jesús, Rodriguez-Fernandez Alicia, Olave María T, Cascales-Hernandez Almudena, Gago Beatriz, Hernández-Rivas José-Ángel, Villalon Lucia, Corona Magdalena, Roldán-Pérez Alicia, Ribes-Amoros Julia, González-Santillana Clara, Garcia-Sanz Ramon, Navarro David, Serrano-López Antonio J, Cedillo Ángel, Soria-Olivas Emilio, Sureda Anna, Solano Carlos

2022-Jul-02

Allogeneic stem cell transplantation, Autologous stem cell transplantation, Bayesian Networks, CAR-T therapy, COVID-19, Chronic lymphocytic leukemia, Hematological malignancies, Immunocompromised patients, Moderna mRNA-1273, Non-Hodgkin lymphoma, Pfizer-BioNTech BNT162b2, Probabilistic graphical models, Respiratory virus, SARS-CoV-2 vaccines, mRNA vaccine

General General

Gestational age-specific serum creatinine can predict adverse pregnancy outcomes.

In Scientific reports ; h5-index 158.0

Serum creatinine level (SCr) typically decreases during pregnancy due to physiologic glomerular hyperfiltration. Therefore, the clinical practice of estimated glomerular filtration rate (eGFR) based on SCr concentrations might be inapplicable to pregnant women with kidney disease since it does not take into account of the pregnancy-related biological changes. We integrated the Wonju Severance Christian Hospital (WSCH)-based findings and prior knowledge from big data to reveal the relationship between the abnormal but hidden SCr level and adverse pregnancy outcomes. We analyzed 4004 pregnant women who visited in WSCH. Adverse pregnancy outcomes included preterm birth, preeclampsia, fetal growth retardation, and intrauterine fetal demise. We categorized the pregnant women into four groups based on the gestational age (GA)-unadjusted raw distribution (Q1-4raw), and then GA-specific (Q1-4adj) SCr distribution. Linear regression analysis revealed that Q1-4adj groups had better predictive outcomes than the Q1-4raw groups. In logistic regression model, the Q1-4adj groups exhibited a robust non-linear U-shaped relationship with the risk of adverse pregnancy outcomes, compared to the Q1-4raw groups. The integrative analysis on SCr with respect to GA-specific distribution could be used to screen out pregnant women with a normal SCr coupled with a decreased renal function.

Kang Jieun, Hwang Sangwon, Lee Tae Sic, Cho Jooyoung, Seo Dong Min, Choi Seong Jin, Uh Young

2022-Jul-02

Public Health Public Health

A convolutional neural network highlights mutations relevant to antimicrobial resistance in Mycobacterium tuberculosis.

In Nature communications ; h5-index 260.0

Long diagnostic wait times hinder international efforts to address antibiotic resistance in M. tuberculosis. Pathogen whole genome sequencing, coupled with statistical and machine learning models, offers a promising solution. However, generalizability and clinical adoption have been limited by a lack of interpretability, especially in deep learning methods. Here, we present two deep convolutional neural networks that predict antibiotic resistance phenotypes of M. tuberculosis isolates: a multi-drug CNN (MD-CNN), that predicts resistance to 13 antibiotics based on 18 genomic loci, with AUCs 82.6-99.5% and higher sensitivity than state-of-the-art methods; and a set of 13 single-drug CNNs (SD-CNN) with AUCs 80.1-97.1% and higher specificity than the previous state-of-the-art. Using saliency methods to evaluate the contribution of input sequence features to the SD-CNN predictions, we identify 18 sites in the genome not previously associated with resistance. The CNN models permit functional variant discovery, biologically meaningful interpretation, and clinical applicability.

Green Anna G, Yoon Chang Ho, Chen Michael L, Ektefaie Yasha, Fina Mack, Freschi Luca, Gröschel Matthias I, Kohane Isaac, Beam Andrew, Farhat Maha

2022-Jul-02

General General

Protein-protein interaction and non-interaction predictions using gene sequence natural vector.

In Communications biology

Predicting protein-protein interaction and non-interaction are two important different aspects of multi-body structure predictions, which provide vital information about protein function. Some computational methods have recently been developed to complement experimental methods, but still cannot effectively detect real non-interacting protein pairs. We proposed a gene sequence-based method, named NVDT (Natural Vector combine with Dinucleotide and Triplet nucleotide), for the prediction of interaction and non-interaction. For protein-protein non-interactions (PPNIs), the proposed method obtained accuracies of 86.23% for Homo sapiens and 85.34% for Mus musculus, and it performed well on three types of non-interaction networks. For protein-protein interactions (PPIs), we obtained accuracies of 99.20, 94.94, 98.56, 95.41, and 94.83% for Saccharomyces cerevisiae, Drosophila melanogaster, Helicobacter pylori, Homo sapiens, and Mus musculus, respectively. Furthermore, NVDT outperformed established sequence-based methods and demonstrated high prediction results for cross-species interactions. NVDT is expected to be an effective approach for predicting PPIs and PPNIs.

Zhao Nan, Zhuo Maji, Tian Kun, Gong Xinqi

2022-Jul-02

General General

A comparative study on deep learning models for text classification of unstructured medical notes with various levels of class imbalance.

In BMC medical research methodology

BACKGROUND : Discharge medical notes written by physicians contain important information about the health condition of patients. Many deep learning algorithms have been successfully applied to extract important information from unstructured medical notes data that can entail subsequent actionable results in the medical domain. This study aims to explore the model performance of various deep learning algorithms in text classification tasks on medical notes with respect to different disease class imbalance scenarios.

METHODS : In this study, we employed seven artificial intelligence models, a CNN (Convolutional Neural Network), a Transformer encoder, a pretrained BERT (Bidirectional Encoder Representations from Transformers), and four typical sequence neural networks models, namely, RNN (Recurrent Neural Network), GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory), and Bi-LSTM (Bi-directional Long Short-Term Memory) to classify the presence or absence of 16 disease conditions from patients' discharge summary notes. We analyzed this question as a composition of 16 binary separate classification problems. The model performance of the seven models on each of the 16 datasets with various levels of imbalance between classes were compared in terms of AUC-ROC (Area Under the Curve of the Receiver Operating Characteristic), AUC-PR (Area Under the Curve of Precision and Recall), F1 Score, and Balanced Accuracy as well as the training time. The model performances were also compared in combination with different word embedding approaches (GloVe, BioWordVec, and no pre-trained word embeddings).

RESULTS : The analyses of these 16 binary classification problems showed that the Transformer encoder model performs the best in nearly all scenarios. In addition, when the disease prevalence is close to or greater than 50%, the Convolutional Neural Network model achieved a comparable performance to the Transformer encoder, and its training time was 17.6% shorter than the second fastest model, 91.3% shorter than the Transformer encoder, and 94.7% shorter than the pre-trained BERT-Base model. The BioWordVec embeddings slightly improved the performance of the Bi-LSTM model in most disease prevalence scenarios, while the CNN model performed better without pre-trained word embeddings. In addition, the training time was significantly reduced with the GloVe embeddings for all models.

CONCLUSIONS : For classification tasks on medical notes, Transformer encoders are the best choice if the computation resource is not an issue. Otherwise, when the classes are relatively balanced, CNNs are a leading candidate because of their competitive performance and computational efficiency.

Lu Hongxia, Ehwerhemuepha Louis, Rakovski Cyril

2022-Jul-02

BERT, CNN, Deep learning, Embedding, Medical notes, Text classification, Transformer encoder

Radiology Radiology

Added value of an artificial intelligence solution for fracture detection in the radiologist's daily trauma emergencies workflow.

In Diagnostic and interventional imaging

PURPOSE : The main objective of this study was to compare radiologists' performance without and with artificial intelligence (AI) assistance for the detection of bone fractures from trauma emergencies.

MATERIALS AND METHODS : Five hundred consecutive patients (232 women, 268 men) with a mean age of 37 ± 28 (SD) years (age range: 0.25-99 years) were retrospectively included. Three radiologists independently interpreted radiographs without then with AI assistance after a 1-month minimum washout period. The ground truth was determined by consensus reading between musculoskeletal radiologists and AI results. Patient-wise sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for fracture detection and reading time were compared between unassisted and AI-assisted readings of radiologists. Their performances were also assessed by receiver operating characteristic (ROC) curves.

RESULTS : AI improved the patient-wise sensitivity of radiologists for fracture detection by 20% (95% confidence interval [CI]: 14-26), P< 0.001) and their specificity by 0.6% (95% CI: -0.9-1.5; P = 0.47). It increased the PPV by 2.9% (95% CI: 0.4-5.4; P = 0.08) and the NPV by 10% (95% CI: 6.8-13.3; P < 0.001). Thanks to AI, the area under the ROC curve for fracture detection of readers increased respectively by 10.6%, 10.2% and 9.9%. Their mean reading time per patient decreased by respectively 10, 16 and 12 s (P < 0.001).

CONCLUSIONS : AI-assisted radiologists work better and faster compared to unassisted radiologists. AI is of great aid to radiologists in daily trauma emergencies, and could reduce the cost of missed fractures.

Canoni-Meynet Lisa, Verdot Pierre, Danner Alexis, Calame Paul, Aubry Sébastien

2022-Jun-29

Artificial intelligence, Bone fracture, Emergency radiology, Musculoskeletal, Radiography

General General

Cerebral cortex layer segmentation using diffusion magnetic resonance imaging in vivo with applications to laminar connections and working memory analysis.

In Human brain mapping

Understanding the laminar brain structure is of great help in further developing our knowledge of the functions of the brain. However, since most layer segmentation methods are invasive, it is difficult to apply them to the human brain in vivo. To systematically explore the human brain's laminar structure noninvasively, the K-means clustering algorithm was used to automatically segment the left hemisphere into two layers, the superficial and deep layers, using a 7 Tesla (T) diffusion magnetic resonance imaging (dMRI)open dataset. The obtained layer thickness was then compared with the layer thickness of the BigBrain reference dataset, which segmented the neocortex into six layers based on the von Economo atlas. The results show a significant correlation not only between our automatically segmented superficial layer thickness and the thickness of layers 1-3 from the reference histological data, but also between our automatically segmented deep layer thickness and the thickness of layers 4-6 from the reference histological data. Second, we constructed the laminar connections between two pairs of unidirectional connected regions, which is consistent with prior research. Finally, we conducted the laminar analysis of the working memory, which was challenging to do in the past, and explained the conclusions of the functional analysis. Our work successfully demonstrates that it is possible to segment the human cortex noninvasively into layers using dMRI data and further explores the mechanisms of the human brain.

Zhang Jie, Sun Zhe, Duan Feng, Shi Liang, Zhang Yu, Solé-Casals Jordi, Caiafa Cesar F

2022-Jul-01

cortical layers, diffusion magnetic resonance imaging, in vivo, laminar connections, noninvasive, working memory

Radiology Radiology

Multi-center validation of an artificial intelligence system for detection of COVID-19 on chest radiographs in symptomatic patients.

In European radiology ; h5-index 62.0

OBJECTIVES : While chest radiograph (CXR) is the first-line imaging investigation in patients with respiratory symptoms, differentiating COVID-19 from other respiratory infections on CXR remains challenging. We developed and validated an AI system for COVID-19 detection on presenting CXR.

METHODS : A deep learning model (RadGenX), trained on 168,850 CXRs, was validated on a large international test set of presenting CXRs of symptomatic patients from 9 study sites (US, Italy, and Hong Kong SAR) and 2 public datasets from the US and Europe. Performance was measured by area under the receiver operator characteristic curve (AUC). Bootstrapped simulations were performed to assess performance across a range of potential COVID-19 disease prevalence values (3.33 to 33.3%). Comparison against international radiologists was performed on an independent test set of 852 cases.

RESULTS : RadGenX achieved an AUC of 0.89 on 4-fold cross-validation and an AUC of 0.79 (95%CI 0.78-0.80) on an independent test cohort of 5,894 patients. Delong's test showed statistical differences in model performance across patients from different regions (p < 0.01), disease severity (p < 0.001), gender (p < 0.001), and age (p = 0.03). Prevalence simulations showed the negative predictive value increases from 86.1% at 33.3% prevalence, to greater than 98.5% at any prevalence below 4.5%. Compared with radiologists, McNemar's test showed the model has higher sensitivity (p < 0.001) but lower specificity (p < 0.001).

CONCLUSION : An AI model that predicts COVID-19 infection on CXR in symptomatic patients was validated on a large international cohort providing valuable context on testing and performance expectations for AI systems that perform COVID-19 prediction on CXR.

KEY POINTS : • An AI model developed using CXRs to detect COVID-19 was validated in a large multi-center cohort of 5,894 patients from 9 prospectively recruited sites and 2 public datasets. • Differences in AI model performance were seen across region, disease severity, gender, and age. • Prevalence simulations on the international test set demonstrate the model's NPV is greater than 98.5% at any prevalence below 4.5%.

Kuo Michael D, Chiu Keith W H, Wang David S, Larici Anna Rita, Poplavskiy Dmytro, Valentini Adele, Napoli Alessandro, Borghesi Andrea, Ligabue Guido, Fang Xin Hao B, Wong Hing Ki C, Zhang Sailong, Hunter John R, Mousa Abeer, Infante Amato, Elia Lorenzo, Golemi Salvatore, Yu Leung Ho P, Hui Christopher K M, Erickson Bradley J

2022-Jul-02

Artificial intelligence, COVID-19, Public health, Radiology, Thoracic

General General

Machine Learning Methods in Health Economics and Outcomes Research-The PALISADE Checklist: A Good Practices Report of an ISPOR Task Force.

In Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research

Advances in machine learning (ML) and artificial intelligence offer tremendous potential benefits to patients. Predictive analytics using ML are already widely used in healthcare operations and care delivery, but how can ML be used for health economics and outcomes research (HEOR)? To answer this question, ISPOR established an emerging good practices task force for the application of ML in HEOR. The task force identified 5 methodological areas where ML could enhance HEOR: (1) cohort selection, identifying samples with greater specificity with respect to inclusion criteria; (2) identification of independent predictors and covariates of health outcomes; (3) predictive analytics of health outcomes, including those that are high cost or life threatening; (4) causal inference through methods, such as targeted maximum likelihood estimation or double-debiased estimation-helping to produce reliable evidence more quickly; and (5) application of ML to the development of economic models to reduce structural, parameter, and sampling uncertainty in cost-effectiveness analysis. Overall, ML facilitates HEOR through the meaningful and efficient analysis of big data. Nevertheless, a lack of transparency on how ML methods deliver solutions to feature selection and predictive analytics, especially in unsupervised circumstances, increases risk to providers and other decision makers in using ML results. To examine whether ML offers a useful and transparent solution to healthcare analytics, the task force developed the PALISADE Checklist. It is a guide for balancing the many potential applications of ML with the need for transparency in methods development and findings.

Padula William V, Kreif Noemi, Vanness David J, Adamson Blythe, Rueda Juan-David, Felizzi Federico, Jonsson Pall, IJzerman Maarten J, Butte Atul, Crown William

2022-Jul

artificial intelligence, machine learning

General General

Clinical and genetic study of three families with 15q11q13 duplications.

In Taiwanese journal of obstetrics & gynecology

OBJECTIVE : To report three families with chromosome 15q11q13 duplications.

CASE REPORT : We report the prenatal diagnosis and genetic counseling of three 15q11q13 duplications.

CONCLUSION : Chromosomal microdeletions and microduplications are difficult to be detected by conventional cytogenetics. With molecular genetic techniques including array-based methods, the number of reported cases has rapidly increased. An integration of prenatal ultrasound, NIPT, karyotype analysis, CMA and genetic counseling is helpful for the prenatal diagnosis of chromosomal microdeletions/microduplications.

Song Jieping, Liu Xu, Zhang Chengcheng, Xu Fei, Wang Bo

2022-Jul

Chromosomal microarray analysis (CMA), Chromosomal microdeletions/microduplications, Chromosome karyotype, Noninvasive prenatal testing (NIPT), Prenatal diagnosis

General General

Network pharmacology study to reveal active compounds of Qinggan Yin formula against pulmonary inflammation by inhibiting MAPK activation.

In Journal of ethnopharmacology ; h5-index 59.0

ETHNOPHARMACOLOGICAL RELEVANCE : Pneumonia is common and frequently-occurred disease caused by pathogens which predisposes to lung parenchymal inflammation leading pulmonary dysfunction. To prevent and alleviate the symptoms of pneumonia, Qinggan Yin formula (QGY) was composed based on clinical experience and four classical traditional Chinese medicine prescriptions which frequently applied to treat infectious diseases.

AIM OF THE STUDY : Traditional Chinese medicine is a complex mixture and it is difficult to distinguish the effective component molecules. The aim of this study is to identify the compounds of QGY with anti-inflammatory effects and investigate the molecular mechanism.

MATERIALS AND METHODS : The high-resolution mass spectrometry and molecular networking were performed for comprehensive chemical profiling of QGY. Network pharmacology was used to generate "herbal-target-pathway" network for target predictions. The anti-inflammation effects of QGY were evaluated in mice model of lipopolysaccharide (LPS) induced acute inflammation. Tail transected zebrafish was also employed to validate macrophage migration reversed effect of QGY. Based on the molecular enrichment analysis, the active substances of QGY with anti-inflammatory effects were further identified in cellular model of macrophage activation. The mechanisms of active substances were investigated by testing their effects on the expression of correlated proteins by Western blot.

RESULTS : In total, 71 compounds are identified as major substances of QGY. According to the results of network pharmacology, QGY shows moderate anti-inflammatory effects and inhibit pulmonary injury. MAPK signaling pathway was predicted as the most related pathway regulated by QGY. Moreover, QGY significantly inhibit LPS-induced pulmonary inflammation in mice, and reversed macrophage migration toward the injury site in zebrafish. We also validate that some major compounds in QGY significantly attenuated the release of IL-1β, IL-6 and TNF-α in LPS-stimulated macrophage. Those active substances including acacetin and arctiin can inhibit the phosphorylation of ERK/JNK and down-regulated the protein expression of BCL-2.

CONCLUSION : Collectively, QGY possessed pronounced anti-inflammation effects. The integration of network pharmacology and experimental results indicated arctiin, iridin, acacetin, liquiritin, and arctigenin are major active substances of QGY with anti-inflammatory effects. The underlying mechanism of QGY involves MAPK signaling pathway and oxidative stress pathway.

Jin Zehua, Sheng Hongda, Wang Shufang, Wang Yi, Cheng Yiyu

2022-Jun-29

Active compounds screening, Inflammation, MAPK signaling Pathway, Mass spectrometry molecular networking, Network pharmacology, Qinggan Yin formula

Public Health Public Health

In-home environmental exposures predicted from geospatial characteristics of the built environment and electronic health records of children with asthma.

In Annals of epidemiology ; h5-index 39.0

OBJECTIVE : Children may be exposed to numerous in-home environmental exposures (IHEE) that trigger asthma exacerbations. Spatially linking social and environmental exposures to electronic health records (EHR) can aid exposure assessment, epidemiology, and clinical treatment, but EHR data on exposures are missing for many children with asthma. To address the issue, we predicted presence of indoor asthma trigger allergens, and estimated effects of their key geospatial predictors.

STUDY DESIGN AND SETTING : Our study samples were comprised of children with asthma who provided self-reported IHEE data in EHR at a safety-net hospital in New England during 2004-2015. We used an ensemble machine learning algorithm and 86 multilevel features (e.g., individual, housing, neighborhood) to predict presence of cockroaches, rodents (mice or rats), mold, and bedroom carpeting/rugs in homes. We reduced dimensionality via elastic net regression and estimated effects by the G-computation causal inference method.

RESULTS : Our models reasonably predicted presence of cockroaches (Area under receiver operating curves [AUC]=0.65), rodents (AUC=0.64), and bedroom carpeting/rugs (AUC=0.64), but not mold (AUC=0.54). In models adjusted for confounders, higher average household sizes in census tracts were associated with more reports of pests (cockroaches and rodents). Tax-exempt parcels were associated with more reports of cockroaches in homes. Living in a White-segregated neighborhood was linked with lower reported rodent presence, and mixed residential/commercial housing and newer buildings were associated with more reports of bedroom carpeting/rugs in bedrooms.

CONCLUSION : We innovatively applied a machine learning and causal inference mixture methodology to detail IHEE among children with asthma using EHR and geospatial data, which could have wide applicability and utility.

Bozigar Matthew, Connolly Catherine L, Legler Aaron, Adams William G, Milando Chad W, Reynolds David B, Carnes Fei, Jimenez Raquel B, Peer Komal, Vermeer Kimberly, Levy Jonathan I, Fabian M Patricia

2022-Jun-29

Exposure assessment, asthma triggers, electronic health records, housing, neighborhoods

General General

Evolutionary dynamics of zero-determinant strategies in repeated multiplayer games.

In Journal of theoretical biology

Several studies have confirmed the existence of zero-determinant (ZD) strategies in repeated social dilemmas since Press and Dyson's ingenious discovery of ZD strategies in iterated prisoner's dilemmas. However, less research studies evolutionary performance of multiplayer ZD strategies, especially from a theoretical perspective. Here, we use a state-clustering method to theoretically analyze evolutionary dynamics of two representative ZD strategies: generous ZD strategies and extortionate ZD strategies. We consider two new settings for multiplayer ZD strategies: competitions with all ZD strategies and competitions with all memory-one strategies, apart from the competitions between these strategies and some classical ones. Moreover, we investigate the influence of the level of generosity and extortion on evolutionary dynamics of generous and extortionate ZD strategies, which was commonly ignored in previous studies. Theoretical results show that players with limited generosity are at an advantageous place and extortioners extorting more severely hold their ground more readily. Our results may provide new insights into better understanding evolutionary dynamics of ZD strategies in repeated multiplayer games.

Chen Fang, Wu Te, Wang Long

2022-Jun-29

Direct reciprocity, Evolutionary game, Repeated multiplayer games, Zero-determinant strategies

General General

STAT3-mediated ferroptosis is involved in ulcerative colitis.

In Free radical biology & medicine

Ferroptosis is a form of iron-dependent lipid peroxidation cell death that plays an important role in inflammation. However, the mechanism of ferroptosis in ulcerative colitis (UC) remains to be further investigated. In the present study, we merged the differentially expressed genes (DEGs) of UC in GEO database with the ferroptosis-related genes of FerrDb for bioinformatics analysis and successfully screened out the ferroptosis-related hub gene STAT3 (signal transducer and activator of transcription 3). Then we further validated the role of STAT3-mediated ferroptosis in vitro and in vivo models of colitis. The results showed that ferroptosis was increased in DSS-induced colitis, salmonella typhimurium (S. Tm) colitis and H2O2-induced IEC-6 cells. And the phosphorylation level of the hub gene STAT3 was down-regulated in IEC-6 cells treated with H2O2, while Fer-1, an ferroptosis inhibitor, reactivated the phosphorylation level of STAT3. In addition, co-treatment of cells with H2O2 and STAT3 inhibitor (stattic) showed an additive effect on the extent of ferroptosis. Taken together, these findings suggest that ferroptosis is closely associated with the development of colitis and ferroptosis-related gene STAT3 could serve as a potential biomarker for diagnosis and treatment of ulcerative colitis.

Huang Fangfang, Zhang Suzhou, Li Xiaoling, Huang Yuge, He Shasha, Luo Lianxiang

2022-Jun-29

Ferroptosis, Machine learning, STAT3, Salmonella typhimurium, Ulcerative colitis

Pathology Pathology

Digital pathology with artificial intelligence analyses provides greater insights into treatment-induced fibrosis regression in NASH.

In Journal of hepatology ; h5-index 119.0

BACKGROUND AND AIMS : Liver fibrosis is a key prognostic determinant for clinical outcomes in non-alcoholic steatohepatitis (NASH). Current scoring systems have limitations, especially in assessing fibrosis regression. Second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) microscopy with artificial intelligence (AI) analyses provides standardized evaluation of NASH features, especially liver fibrosis and collagen fiber quantitation on a continuous scale. This approach was applied to gain in-depth understanding of fibrosis dynamics after treatment with tropifexor (TXR), a non-bile acid farnesoid X receptor (FXR) agonist in patients participating in FLIGHT-FXR study (NCT02855164).

METHOD : Unstained sections from 198 liver biopsies (paired: baseline and end-of-treatment) from 99 patients with NASH (fibrosis stage F2 or F3) who received placebo (n=34), TXR 140 μg (n=37), or TXR 200 μg (n=28) for 48 weeks were examined. Liver fibrosis (qFibrosis ®), hepatic fat (qSteatosis®), and ballooned hepatocytes (qBallooning®) were quantitated using SHG/TPEF microscopy. Changes in septa morphology, collagen fiber parameters, and zonal distribution within liver lobules were also quantitatively assessed.

RESULTS : Digital analyses revealed treatment-associated reduction of overall liver fibrosis (qFibrosis®), unlike conventional microscopy, as well as marked regression in perisinusoidal fibrosis in patients who had either F2 or F3 fibrosis at baseline. Concomitant zonal quantitation of fibrosis and steatosis revealed that patients with greater qSteatosis reduction also have greatest reduction in perisinusoidal fibrosis. Regressive changes in septa morphology and reduction in septa parameters were observed almost exclusively in F3 patients, who were adjudged as 'unchanged' with conventional scoring.

CONCLUSION : Fibrosis regression following hepatic fat reduction occurs initially in the perisinusoidal regions, around areas of steatosis reduction. Digital pathology provides new insights in treatment-induced fibrosis regression in NASH, which are not captured by current staging systems.

LAY SUMMARY : The degree of liver fibrosis in non-alcoholic steatohepatitis (NASH) is the principal feature that predicts clinical outcomes. Accurate assessment of liver fibrosis amount and architecture is fundamental for patients' enrolment in NASH clinical trials and determining treatment efficacy. Using digital microscopy with artificial intelligence analyses, the present study demonstrates that this novel approach has greater sensitivity and granularity in demonstrating treatment-induced reversal of fibrotic changes in the liver than current systems which use ordinal assessment of liver fibrosis in patients with NASH. Furthermore, additional details are obtained regarding the pathobiology of NASH disease and effects of therapy.

Naoumov Nikolai V, Brees Dominique, Loeffler Juergen, Chng Elaine, Ren Yayun, Lopez Patricia, Tai Dean, Lamle Sophie, Sanyal Arun J

2022-Jun-29

Digital Pathology with Artificial Intelligence, Farnesoid X Receptor Agonists and NASH Treatment, Fibrosis Regression, Non-alcoholic Steatohepatitis, Perisinusoidal Fibrosis, Second Harmonic Generation Microscopy

Radiology Radiology

Multi-branch fusion auxiliary learning for the detection of pneumonia from chest X-ray images.

In Computers in biology and medicine

Lung infections caused by bacteria and viruses are infectious and require timely screening and isolation, and different types of pneumonia require different treatment plans. Therefore, finding a rapid and accurate screening method for lung infections is critical. To achieve this goal, we proposed a multi-branch fusion auxiliary learning (MBFAL) method for pneumonia detection from chest X-ray (CXR) images. The MBFAL method was used to perform two tasks through a double-branch network. The first task was to recognize the absence of pneumonia (normal), COVID-19, other viral pneumonia and bacterial pneumonia from CXR images, and the second task was to recognize the three types of pneumonia from CXR images. The latter task was used to assist the learning of the former task to achieve a better recognition effect. In the process of auxiliary parameter updating, the feature maps of different branches were fused after sample screening through label information to enhance the model's ability to recognize case of pneumonia without impacting its ability to recognize normal cases. Experiments show that an average classification accuracy of 95.61% is achieved using MBFAL. The single class accuracy for normal, COVID-19, other viral pneumonia and bacterial pneumonia was 98.70%, 99.10%, 96.60% and 96.80%, respectively, and the recall was 97.20%, 98.60%, 96.10% and 89.20%, respectively, using the MBFAL method. Compared with the baseline model and the model constructed using the above methods separately, better results for the rapid screening of pneumonia were achieved using MBFAL.

Liu Jia, Qi Jing, Chen Wei, Nian Yongjian

2022-Jun-15

Auxiliary learning, Deep learning, Feature fusion, Multi-task learning, Pneumonia

General General

Image super-resolution with an enhanced group convolutional neural network.

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

CNNs with strong learning abilities are widely chosen to resolve super-resolution problem. However, CNNs depend on deeper network architectures to improve performance of image super-resolution, which may increase computational cost in general. In this paper, we present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture by fully fusing deep and wide channel features to extract more accurate low-frequency information in terms of correlations of different channels in single image super-resolution (SISR). Also, a signal enhancement operation in the ESRGCNN is useful to inherit more long-distance contextual information for resolving long-term dependency. An adaptive up-sampling operation is gathered into a CNN to obtain an image super-resolution model with low-resolution images of different sizes. Extensive experiments report that our ESRGCNN surpasses the state-of-the-arts in terms of SISR performance, complexity, execution speed, image quality evaluation and visual effect in SISR. Code is found at https://github.com/hellloxiaotian/ESRGCNN.

Tian Chunwei, Yuan Yixuan, Zhang Shichao, Lin Chia-Wen, Zuo Wangmeng, Zhang David

2022-Jun-11

CNN, Group convolution, Image super-resolution, Signal processing

Public Health Public Health

Privacy preserving Generative Adversarial Networks to model Electronic Health Records.

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

Hospitals and General Practitioner (GP) surgeries within National Health Services (NHS), collect patient information on a routine basis to create personal health records such as family medical history, chronic diseases, medications and dosing. The collected information could be used to build and model various machine learning algorithms, to simplify the task of those working within the NHS. However, such Electronic Health Records are not made publicly available due to privacy concerns. In our paper, we propose a privacy-preserving Generative Adversarial Network (pGAN), which can generate synthetic data of high quality, while preserving the privacy and statistical properties of the source data. pGAN is evaluated on two distinct datasets, one posing as a Classification task, and the other as a Regression task. Privacy score of generated data is calculated using the Nearest Neighbour Adversarial Accuracy. Cosine similarity scores of synthetic data from our proposed model indicate that the data generated is similar in nature, but not identical. Additionally, our proposed model was able to preserve privacy while maintaining high utility. Machine learning models trained on both synthetic data and original data have achieved accuracies of 74.3% and 74.5% respectively on the classification dataset; while they have attained an R2-Score of 0.84 and 0.85 on synthetic and original data of the regression task respectively. Our results, therefore, indicate that synthetic data from the proposed model could replace the use of original data for machine learning while preserving privacy.

Venugopal Rohit, Shafqat Noman, Venugopal Ishwar, Tillbury Benjamin Mark John, Stafford Harry Demetrios, Bourazeri Aikaterini

2022-Jun-25

AI, GAN, Machine learning, Privacy, Public health data

General General

External Validation and Transportability of Models to Predict Acute Kidney Injury in the Intensive Care Unit.

In Studies in health technology and informatics ; h5-index 23.0

External validation of models for the prediction of acute kidney injury (AKI) is rare. We externally validate AKI prediction models in intensive care units. The models were developed on the Medical Information Mart for Intensive Care dataset and validated on the eICU dataset. Traditional machine learning models show limited transportability to the new population (AUROC < 0.8). Models based on recurrent neural networks, which can capture complex relationships between the data, transport well to the new population (AUROC 0.8-0.9). Such models can help clinicians to recognize AKI and improve the outcome.

Vagliano Iacopo, Byrne Salsas Carmen, Wünn Tina, Schut Martijn C

2022-Jun-29

Acute kidney injury, ICU, clinical prediction models, external validation, machine learning

General General

Sequence-assignment validation in cryo-EM models with checkMySequence.

In Acta crystallographica. Section D, Structural biology

The availability of new artificial intelligence-based protein-structure-prediction tools has radically changed the way that cryo-EM maps are interpreted, but it has not eliminated the challenges of map interpretation faced by a microscopist. Models will continue to be locally rebuilt and refined using interactive tools. This inevitably results in occasional errors, among which register shifts remain one of the most difficult to identify and correct. Here, checkMySequence, a fast, fully automated and parameter-free method for detecting register shifts in protein models built into cryo-EM maps, is introduced. It is shown that the method can assist model building in cases where poorer map resolution hinders visual interpretation. It is also shown that checkMySequence could have helped to avoid a widely discussed sequence-register error in a model of SARS-CoV-2 RNA-dependent RNA polymerase that was originally detected thanks to a visual residue-by-residue inspection by members of the structural biology community. The software is freely available at https://gitlab.com/gchojnowski/checkmysequence.

Chojnowski Grzegorz

2022-Jul-01

checkMySequence, cryo-EM, model validation, register shifts, sequence assignment

General General

Fully Connected Networks on a Diet With the Mediterranean Matrix Multiplication.

In IEEE transactions on neural networks and learning systems

This article proposes the Mediterranean matrix multiplication, a new, simple and practical randomized algorithm that samples angles between the rows and columns of two matrices with sizes m, n, and p to approximate matrix multiplication in O(k(mn+np+mp)) steps, where k is a constant only related to the precision desired. The number of instructions carried out is mainly bounded by bitwise operators, amenable to a simplified processing architecture and compressed matrix weights. Results show that the method is superior in size and number of operations to the standard approximation with signed matrices. Equally important, this article demonstrates a first application to machine learning inference by showing that weights of fully connected layers can be compressed between 30 × and 100 × with little to no loss in inference accuracy. The requirements for pure floating-point operations are also down as our algorithm relies mainly on simpler bitwise operators.

Eshkiki Hassan, Mora Benjamin, Xie Xianghua

2022-Jul-01

General General

Bad and Good Errors: Value-Weighted Skill Scores in Deep Ensemble Learning.

In IEEE transactions on neural networks and learning systems

Forecast verification is a crucial task for assessing the predictive power of prognostic model forecasts and it is usually implemented by checking quality-based skill scores. In this article, we propose a novel approach to realize forecast verification focusing not just on the forecast quality but rather on its value. Specifically, we introduce a strategy for assessing the severity of forecast errors based on the evidence that, on the one hand, a false alarm just anticipating an occurring event is better than one in the middle of consecutive nonoccurring events, and that, on the other hand, a miss of an isolated event has a worse impact than a miss of a single event, which is part of several consecutive occurrences. Relying on this idea, we introduce a notion of value-weighted skill scores giving greater importance to the value of the prediction rather than to its quality. Then, we introduce an ensemble strategy to maximize quality-based and value-weighted skill scores independently of one another. We test it on the predictions provided by deep learning methods for binary classification in the case of four applications concerned with pollution, space weather, stock price, and IoT data stream forecasting. Our experimental studies show that using the ensemble strategy for maximizing the value-weighted skill scores generally improves both the value and quality of the forecast.

Guastavino Sabrina, Piana Michele, Benvenuto Federico

2022-Jul-01

Surgery Surgery

Economic impact of a machine learning-based strategy for preparation of blood products in brain tumor surgery.

In PloS one ; h5-index 176.0

BACKGROUND : Globally, blood donation has been disturbed due to the pandemic. Consequently, the optimization of preoperative blood preparation should be a point of concern. Machine learning (ML) is one of the modern approaches that have been applied by physicians to help decision-making. The main objective of this study was to identify the cost differences of the ML-based strategy compared with other strategies in preoperative blood products preparation. A secondary objective was to compare the effectiveness indexes of blood products preparation among strategies.

METHODS : The study utilized a retrospective cohort design conducted on brain tumor patients who had undergone surgery between January 2014 and December 2021. Overall data were divided into two cohorts. The first cohort was used for the development and deployment of the ML-based web application, while validation, comparison of the effectiveness indexes, and economic evaluation were performed using the second cohort. Therefore, the effectiveness indexes of blood preparation and cost difference were compared among the ML-based strategy, clinical trial-based strategy, and routine-based strategy.

RESULTS : Over a 2-year period, the crossmatch to transfusion (C/T) ratio, transfusion probability (Tp), and transfusion index (Ti) of the ML-based strategy were 1.10, 57.0%, and 1.62, respectively, while the routine-based strategy had a C/T ratio of 4.67%, Tp of 27.9%%, and Ti of 0.79. The overall costs of blood products preparation among the ML-based strategy, clinical trial-based strategy, and routine-based strategy were 30, 061.56$, 57,313.92$, and 136,292.94$, respectively. From the cost difference between the ML-based strategy and routine-based strategy, we observed cost savings of 92,519.97$ (67.88%) for the 2-year period.

CONCLUSION : The ML-based strategy is one of the most effective strategies to balance the unnecessary workloads at blood banks and reduce the cost of unnecessary blood products preparation from low C/T ratio as well as high Tp and Ti. Further studies should be performed to confirm the generalizability and applicability of the ML-based strategy.

Tunthanathip Thara, Sae-Heng Sakchai, Oearsakul Thakul, Kaewborisutsakul Anukoon, Taweesomboonyat Chin

2022

General General

Deep learning based lithology classification of drill core images.

In PloS one ; h5-index 176.0

Drill core lithology is an important indicator reflecting the geological conditions of the drilling area. Traditional lithology identification usually relies on manual visual inspection, which is time-consuming and professionally demanding. In recent years, the rapid development of convolutional neural networks has provided an innovative way for the automatic prediction of drill core images. In this work, a core dataset containing a total of 10 common lithology categories in underground engineering was constructed. ResNeSt-50 we adopted uses a strategy of combining channel-wise attention and multi-path network to achieve cross-channel feature correlations, which significantly improves the model accuracy without high model complexity. Transfer learning was used to initialize the model parameters, to extract the feature of core images more efficiently. The model achieved superior performance on testing images compared with other discussed CNN models, the average value of its Precision, Recall, F1-score for each category of lithology is 99.62%, 99.62%, and 99.59%, respectively, and the prediction accuracy is 99.60%. The test results show that the proposed method is optimal and effective for automatic lithology classification of borehole cores.

Fu Dong, Su Chao, Wang Wenjun, Yuan Rongyao

2022

General General

Single-cell specific and interpretable machine learning models for sparse scChIP-seq data imputation.

In PloS one ; h5-index 176.0

MOTIVATION : Single-cell Chromatin ImmunoPrecipitation DNA-Sequencing (scChIP-seq) analysis is challenging due to data sparsity. High degree of sparsity in biological high-throughput single-cell data is generally handled with imputation methods that complete the data, but specific methods for scChIP-seq are lacking. We present SIMPA, a scChIP-seq data imputation method leveraging predictive information within bulk data from the ENCODE project to impute missing protein-DNA interacting regions of target histone marks or transcription factors.

RESULTS : Imputations using machine learning models trained for each single cell, each ChIP protein target, and each genomic region accurately preserve cell type clustering and improve pathway-related gene identification on real human data. Results on bulk data simulating single cells show that the imputations are single-cell specific as the imputed profiles are closer to the simulated cell than to other cells related to the same ChIP protein target and the same cell type. Simulations also show that 100 input genomic regions are already enough to train single-cell specific models for the imputation of thousands of undetected regions. Furthermore, SIMPA enables the interpretation of machine learning models by revealing interaction sites of a given single cell that are most important for the imputation model trained for a specific genomic region. The corresponding feature importance values derived from promoter-interaction profiles of H3K4me3, an activating histone mark, highly correlate with co-expression of genes that are present within the cell-type specific pathways in 2 real human and mouse datasets. The SIMPA's interpretable imputation method allows users to gain a deep understanding of individual cells and, consequently, of sparse scChIP-seq datasets.

AVAILABILITY AND IMPLEMENTATION : Our interpretable imputation algorithm was implemented in Python and is available at https://github.com/salbrec/SIMPA.

Albrecht Steffen, Andreani Tommaso, Andrade-Navarro Miguel A, Fontaine Jean Fred

2022

Surgery Surgery

Fully automated determination of the cervical vertebrae maturation stages using deep learning with directional filters.

In PloS one ; h5-index 176.0

INTRODUCTION : We aim to apply deep learning to achieve fully automated detection and classification of the Cervical Vertebrae Maturation (CVM) stages. We propose an innovative custom-designed deep Convolutional Neural Network (CNN) with a built-in set of novel directional filters that highlight the edges of the Cervical Vertebrae in X-ray images.

METHODS : A total of 1018 Cephalometric radiographs were labeled and classified according to the Cervical Vertebrae Maturation (CVM) stages. The images were cropped to extract the cervical vertebrae using an Aggregate Channel Features (ACF) object detector. The resulting images were used to train four different Deep Learning (DL) models: our proposed CNN, MobileNetV2, ResNet101, and Xception, together with a set of tunable directional edge enhancers. When using MobileNetV2, ResNet101 and Xception, data augmentation is adopted to allow adequate network complexity while avoiding overfitting. The performance of our CNN model was compared with that of MobileNetV2, ResNet101 and Xception with and without the use of directional filters. For validation and performance assessment, k-fold cross-validation, ROC curves, and p-values were used.

RESULTS : The proposed innovative model that uses a CNN preceded with a layer of tunable directional filters achieved a validation accuracy of 84.63%84.63% in CVM stage classification into five classes, exceeding the accuracy achieved with the other DL models investigated. MobileNetV2, ResNet101 and Xception used with directional filters attained accuracies of 78.54%, 74.10%, and 80.86%, respectively. The custom-designed CNN method also achieves 75.11% in six-class CVM stage classification. The effectiveness of the directional filters is reflected in the improved performance attained in the results. If the custom-designed CNN is used without the directional filters, the test accuracy decreases to 80.75%. In the Xception model without the directional filters, the testing accuracy drops slightly to 79.42% in the five-class CVM stage classification.

CONCLUSION : The proposed model of a custom-designed CNN together with the tunable Directional Filters (CNNDF) is observed to provide higher accuracy than the commonly used pre-trained network models that we investigated in the fully automated determination of the CVM stages.

Atici Salih Furkan, Ansari Rashid, Allareddy Veerasathpurush, Suhaym Omar, Cetin Ahmet Enis, Elnagar Mohammed H

2022

Public Health Public Health

Long-Term Associations between Disaster-Related Home Loss and Health and Well-Being of Older Survivors: Nine Years after the 2011 Great East Japan Earthquake and Tsunami.

In Environmental health perspectives ; h5-index 89.0

BACKGROUND : Little research has examined associations between disaster-related home loss and multiple domains of health and well-being, with extended long-term follow-up and comprehensive adjustment for pre-disaster characteristics of survivors.

OBJECTIVES : We examined the longitudinal associations between disaster-induced home loss and 34 indicators of health and well-being, assessed 9y post-disaster.

METHODS : We used data from a preexisting cohort study of Japanese older adults in an area directly impacted by the 2011 Japan Earthquake (n=3,350 and n=2,028, depending on the outcomes). The study was initiated in 2010, and disaster-related home loss status was measured in 2013 retrospectively. The 34 outcomes were assessed in 2020 and covered dimensions of physical health, mental health, health behaviors/sleep, social well-being, cognitive social capital, subjective well-being, and prosocial/altruistic behaviors. We estimated the associations between disaster-related home loss and the outcomes, using targeted maximum likelihood estimation and SuperLearner. We adjusted for pre-disaster characteristics from the wave conducted 7 months before the disaster (i.e., 2010), including prior outcome values that were available.

RESULTS : After Bonferroni correction for multiple testing, we found that home loss (vs. no home loss) was associated with increased posttraumatic stress symptoms (standardized difference=0.50; 95% CI: 0.35, 0.65), increased daily sleepiness (0.38; 95% CI: 0.21, 0.54), lower trust in the community (-0.36; 95% CI: -0.53, -0.18), lower community attachment (-0.60; 95% CI: -0.75, -0.45), and lower prosociality (-0.39; 95% CI: -0.55, -0.24). We found modest evidence for the associations with increased depressive symptoms, increased hopelessness, more chronic conditions, higher body mass index, lower perceived mutual help in the community, and decreased happiness. There was little evidence for associations with the remaining 23 outcomes.

DISCUSSION : Home loss due to a disaster may have long-lasting adverse impacts on the cognitive social capital, mental health, and prosociality of older adult survivors. https://doi.org/10.1289/EHP10903.

Shiba Koichiro, Hikichi Hiroyuki, Okuzono Sakurako S, VanderWeele Tyler J, Arcaya Mariana, Daoud Adel, Cowden Richard G, Yazawa Aki, Zhu David T, Aida Jun, Kondo Katsunori, Kawachi Ichiro

2022-Jul

Surgery Surgery

A deep learning based CT image analytics protocol to identify lung adenocarcinoma category and high-risk tumor area.

In STAR protocols

We present a protocol which implements deep learning-based identification of the lung adenocarcinoma category with high accuracy and generalizability, and labeling of the high-risk area on Computed Tomography (CT) images. The protocol details the execution of the python project based on the dataset used in the original publication or a custom dataset. Detailed steps include data standardization, data preprocessing, model implementation, results display through heatmaps, and statistical analysis process with Origin software or python codes. For complete details on the use and execution of this protocol, please refer to Chen et al. (2022).

Chen Liuyin, Qi Haoyang, Lu Di, Zhai Jianxue, Cai Kaican, Wang Long, Liang Guoyuan, Zhang Zijun

2022-Jun-22

Bioinformatics, Biotechnology and bioengineering, Cancer, Computer sciences, Health Sciences, Systems biology

General General

Inverse design of coupled subwavelength dielectric resonators with targeted eigenfrequency and Q factor utilizing deep learning.

In Optics letters

Subwavelength all-dielectric resonators supporting Mie resonances are promising building blocks in nanophotonics. The coupling of dielectric resonators facilitates advanced shaping of Mie resonances. However, coupled dielectric resonators with anisotropic geometry can only be designed by time-consuming simulation utilizing parameter scanning, hampering their applications in nanophotonics. Herein, we propose and demonstrate that a combination of two fully connected networks can effectively design coupled dielectric resonators with targeted eigenfrequency and Q factor. Typical examples are given for validating the proposed network, where the normalized deviation rates of eigenfrequency and Q factor are 0.39% and 1.29%, respectively. The proposed neutral network might become a useful tool in designing coupled dielectric resonators and beyond.

Pan Tuqiang, Ye Jianwei, Zhang Zhanyuan, Xu Yi

2022-Jul-01

General General

Long short-term memory neural network for directly inverse design of nanofin metasurface.

In Optics letters

In this Letter, the neural network long short-term memory (LSTM) is used to quickly and accurately predict the polarization sensitivity of a nanofin metasurface. In the forward prediction, we construct a deep neural network (DNN) with the same structure for comparison with LSTM. The test results show that LSTM has a higher accuracy and better robustness than DNN in similar cases. In the inverse design, we directly build an LSTM to reverse the design similar to the forward prediction network. By inputting the extinction ratio value in 8-12 µm, the inverse network can directly provide the unit cell geometry of the nanofin metasurface. Compared with other methods used to inverse design photonic structures using deep learning, our method is more direct because no other networks are introduced.

Deng Wenqiang, Xu Zhengji, Wang Jinhao, Lv Jinwen

2022-Jul-01

General General

Artificial Intelligence-Based Mobile Application for Sensing Children Emotion Through Drawings.

In Studies in health technology and informatics ; h5-index 23.0

Children go through varied emotions such as happiness, sadness, and fear. At times, it may be difficult for children to express their emotions. Detecting and understanding the unexpressed emotions of children is very important to address their needs and prevent mental health issues. In this paper, we develop an artificial intelligence (AI) based Emotion Sensing Recognition App (ESRA) to help parents and teachers understand the emotions of children by analyzing their drawings. We collected 102 drawings from a local school in Doha and 521 drawings from Google and Instagram. Four different experiments were conducted using a combination of the two datasets. The deep learning model was trained using the Fastai library in Python. The model classifies the drawings into positive or negative emotions. The model accuracy ranged from 55% to 79% in the four experiments. This study showed that ESRA has the potential in identifying the emotions of children. However, the underlying algorithm needs to be trained and evaluated using more drawings to improve its current accuracy and to be able to identify more specific emotions.

Ali Nashva, Abd-Alrazaq Alaa, Shah Zubair, Alajlani Mohannad, Alam Tanvir, Househ Mowafa

2022-Jun-29

Artificial Intelligence, Children, Emotion Sensing, Mobile Application

Public Health Public Health

Using Twitter Data to Understand Public Perceptions of Approved versus Off-label Use for COVID-19-related Medications.

In Journal of the American Medical Informatics Association : JAMIA

OBJECTIVE : Understanding public discourse on emergency use of unproven therapeutics is essential to monitor safe use and combat misinformation. We developed a natural language processing (NLP)-based pipeline to understand public perceptions of and stances on COVID-19-related drugs on Twitter across time.

METHODS : This retrospective study included 609,189 US-based tweets between January 29th, 2020 and November 30th, 2021 on four drugs that gained wide public attention during the COVID-19 pandemic: 1) Hydroxychloroquine and Ivermectin, drug therapies with anecdotal evidence; and 2) Molnupiravir and Remdesivir, FDA-approved treatment options for eligible patients. Time-trend analysis was used to understand the popularity and related events. Content and demographic analyses were conducted to explore potential rationales of people's stances on each drug.

RESULTS : Time-trend analysis revealed that Hydroxychloroquine and Ivermectin received much more discussion than Molnupiravir and Remdesivir, particularly during COVID-19 surges. Hydroxychloroquine and Ivermectin were highly politicized, related to conspiracy theories, hearsay, celebrity effects, etc. The distribution of stance between the two major US political parties was significantly different (p < 0.001); Republicans were much more likely to support Hydroxychloroquine (+55%) and Ivermectin (+30%) than Democrats. People with healthcare backgrounds tended to oppose Hydroxychloroquine (+7%) more than the general population; in contrast, the general population was more likely to support Ivermectin (+14%).

CONCLUSION : Our study found that social media users with have different perceptions and stances on off-label versus FDA-authorized drug use across different stages of COVID-19, indicating that health systems, regulatory agencies, and policymakers should design "targeted" strategies to monitor and reduce misinformation for promoting safe drug use. Our analysis pipeline and stance detection models are made public at https://github.com/ningkko/COVID-drug.

Hua Yining, Jiang Hang, Lin Shixu, Yang Jie, Plasek Joseph M, Bates David W, Zhou Li

2022-Jul-01

COVID-19, Deep Learning, Drug Safety, Natural Language Processing, Public Health, Social Media

General General

A Moisture-Resistant Soft Actuator with Low Driving Voltages for Haptic Stimulations in Virtual Games.

In ACS applied materials & interfaces ; h5-index 147.0

Strong and robust stimulations to human skins with low driving voltages under high moisture working conditions are desirable for wearable haptic feedback applications. Here, a soft actuator based on the "air bubble" electret structure is developed to work in high-moisture environments and produce haptic sensations to human skin with low driving voltages. Experimentally, the water soaking and drying process has been conducted repeatedly for the first time and the 20th time to test the antimoisture ability of the actuator as it recovers its output force up 90 and 65% of the initial value, respectively. The threshold voltages for sensible haptic sensations for the fingertip and palm of volunteers have been characterized as 7 and 10 V, respectively. Furthermore, a demonstration example has been designed and conducted in a virtual boxing game to generate the designated haptic sensations according to the gaming conditions with an accuracy of 98% for more than 100 tests. As such, the design principle, performance characteristic, and demonstration example in this work could inspire various applications with improved reliability for wearable haptic devices.

Qiu Wenying, Li Zhaoyang, Wang Guocheng, Peng Yande, Zhang Min, Wang Xiaohao, Zhong Junwen, Lin Liwei

2022-Jul-01

haptic feedback, low voltage, moisture resistance, soft actuator, wearable electronics

General General

PHILM2Web: A high-throughput database of macromolecular host-pathogen interactions on the Web.

In Database : the journal of biological databases and curation

During infection, the pathogen's entry into the host organism, breaching the host immune defense, spread and multiplication are frequently mediated by multiple interactions between the host and pathogen proteins. Systematic studying of host-pathogen interactions (HPIs) is a challenging task for both experimental and computational approaches and is critically dependent on the previously obtained knowledge about these interactions found in the biomedical literature. While several HPI databases exist that manually filter HPI protein-protein interactions from the generic databases and curated experimental interactomic studies, no comprehensive database on HPIs obtained from the biomedical literature is currently available. Here, we introduce a high-throughput literature-mining platform for extracting HPI data that includes the most comprehensive to date collection of HPIs obtained from the PubMed abstracts. Our HPI data portal, PHILM2Web (Pathogen-Host Interactions by Literature Mining on the Web), integrates an automatically generated database of interactions extracted by PHILM, our high-precision HPI literature-mining algorithm. Currently, the database contains 23 581 generic HPIs between 157 host and 403 pathogen organisms from 11 609 abstracts. The interactions were obtained from processing 608 972 PubMed abstracts, each containing mentions of at least one host and one pathogen organisms. In response to the coronavirus disease 2019 (COVID-19) pandemic, we also utilized PHILM to process 25 796 PubMed abstracts obtained by the same query as the COVID-19 Open Research Dataset. This COVID-19 processing batch resulted in 257 HPIs between 19 host and 31 pathogen organisms from 167 abstracts. The access to the entire HPI dataset is available via a searchable PHILM2Web interface; scientists can also download the entire database in bulk for offline processing. Database URL: http://philm2web.live.

Le Tuan-Dung, Nguyen Phuong D, Korkin Dmitry, Thieu Thanh

2022-Jun-30

General General

Chemical identification and indexing in PubMed full-text articles using deep learning and heuristics.

In Database : the journal of biological databases and curation

The identification of chemicals in articles has attracted a large interest in the biomedical scientific community, given its importance in drug development research. Most of previous research have focused on PubMed abstracts, and further investigation using full-text documents is required because these contain additional valuable information that must be explored. The manual expert task of indexing Medical Subject Headings (MeSH) terms to these articles later helps researchers find the most relevant publications for their ongoing work. The BioCreative VII NLM-Chem track fostered the development of systems for chemical identification and indexing in PubMed full-text articles. Chemical identification consisted in identifying the chemical mentions and linking these to unique MeSH identifiers. This manuscript describes our participation system and the post-challenge improvements we made. We propose a three-stage pipeline that individually performs chemical mention detection, entity normalization and indexing. Regarding chemical identification, we adopted a deep-learning solution that utilizes the PubMedBERT contextualized embeddings followed by a multilayer perceptron and a conditional random field tagging layer. For the normalization approach, we use a sieve-based dictionary filtering followed by a deep-learning similarity search strategy. Finally, for the indexing we developed rules for identifying the more relevant MeSH codes for each article. During the challenge, our system obtained the best official results in the normalization and indexing tasks despite the lower performance in the chemical mention recognition task. In a post-contest phase we boosted our results by improving our named entity recognition model with additional techniques. The final system achieved 0.8731, 0.8275 and 0.4849 in the chemical identification, normalization and indexing tasks, respectively. The code to reproduce our experiments and run the pipeline is publicly available. Database URL https://github.com/bioinformatics-ua/biocreativeVII_track2.

Almeida Tiago, Antunes Rui, F Silva João, Almeida João R, Matos Sérgio

2022-Jul-01

Radiology Radiology

Deep Learning-Enhanced Parallel Imaging and Simultaneous Multislice Acceleration Reconstruction in Knee MRI.

In Investigative radiology ; h5-index 46.0

OBJECTIVES : This study aimed to examine various combinations of parallel imaging (PI) and simultaneous multislice (SMS) acceleration imaging using deep learning (DL)-enhanced and conventional reconstruction. The study also aimed at comparing the diagnostic performance of the various combinations in internal knee derangement and provided a quantitative evaluation of image noise and sharpness using edge rise distance (ERD) and noise power (NP), respectively.

MATERIALS AND METHODS : The data from adult patients who underwent knee magnetic resonance imaging using various DL-enhanced acquisitions between June 2021 and January 2022 were retrospectively analyzed. The participants underwent conventional 2-fold PI and DL protocols with 4- to 8-fold acceleration imaging (P2S2 [2-fold PI with 2-fold SMS], P3S2, and P4S2). Three readers evaluated the internal knee derangement and the overall image quality. The diagnostic performance was calculated using consensus reading as a standard reference, and we conducted comparative evaluations. We calculated the ERD and NP for quantitative evaluations of image sharpness and noise, respectively. Interreader and intermethod agreements were calculated using Fleiss κ.

RESULTS : A total of 33 patients (mean age, 49 ± 19 years; 20 women) were included in this study. The diagnostic performance for internal knee derangement and the overall image quality were similar among the evaluated protocols. The NP values were significantly lower using the DL protocols than with conventional imaging (P < 0.001), whereas the ERD values were similar among these methods (P > 0.12). Interreader and intermethod agreements were moderate-to-excellent (κ = 0.574-0.838) and good-to-excellent (κ = 0.755-1.000), respectively. In addition, the mean acquisition time was reduced by 47% when using DL with P2S2, by 62% with P3S2, and by 71% with P4S2, compared with conventional P2 imaging (2 minutes and 55 seconds).

CONCLUSIONS : The combined use of DL-enhanced 8-fold acceleration imaging (4-fold PI with 2-fold SMS) showed comparable performance with conventional 2-fold PI for the evaluation of internal knee derangement, with a 71% reduction in acquisition time.

Kim MinWoo, Lee Sang-Min, Park Chankue, Lee Dongeon, Kim Kang Soo, Jeong Hee Seok, Kim Shinyoung, Choi Min-Hyeok, Nickel Dominik

2022-Jul-01

Surgery Surgery

Objective Outcomes of an Extended Anti-reflux Mucosectomy in the Treatment of PPI-Dependent Gastroesophageal Reflux Disease (with Video).

In Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract

BACKGROUND : Anti-reflux mucosectomy (ARMS) is a choice for proton pump inhibitor (PPI)-dependent patients with gastroesophageal reflux disease (GERD). We present an extended anti-reflux mucosectomy, named ligation-assisted anti-reflux mucosectomy (L-ARMS). The aim of this study was to assess the feasibility of the procedure and short-term outcomes on PPI use and symptom resolution.

METHODS : Institutional review board approval was obtained for retrospective review of a prospectively collected database including patients who underwent L-ARMS. L-ARMS includes mucosa ligation and endoscopic mucosectomy without submucosal injection around the squamocolumnar junction. The GERD symptoms, endoscopy, 24-h pH monitoring results, and manometry were collected by chart review. Voluntary validated surveys assessed symptomatic improvement over time.

RESULTS : There were 69 patients available for review. The procedure was technically completed in all cases with no severe complications, and the average operation time was 33 min. At 6 months after L-ARMS, treatment with PPIs had been halted in 55.1% of the patients, 30.4% of the enrolled patients used PPIs occasionally, and the lower esophageal sphincter (LES) pressure, DeMeester scores, and GERD-health-related quality of life questionnaire (GERD-HRQL) scores showed a significant improvement compared with the baseline measurements (P < 0.001). Forty-five patients complained of mild dysphagia and were relieved in 4 weeks with no specific treatment. Compared to patients without dysphagia, patients complained of dysphagia after surgery had better clinical benefits indicated by GERD-HRQL and DeMeester score.

CONCLUSIONS : As a modified ARMS, L-ARMS is an effective procedure for controlling GERD symptoms, esophageal acid exposure, and LES pressure, which can be safely performed endoscopically in a time-saving and simple manner.

He Jian, Yin Yani, Tang Wen, Jiang Jiahui, Gu Lei, Yi Jun, Yan Lu, Chen Shuijiao, Wu Yu, Liu Xiaowei

2022-Jul-01

Anti-reflux mucosectomy, Endoscopy, Gastroesophageal reflux disease, Mucosa ligation, Short-term outcomes

General General

Full-Fiber Auxetic-Interlaced Yarn Sensor for Sign-Language Translation Glove Assisted by Artificial Neural Network.

In Nano-micro letters

Yarn sensors have shown promising application prospects in wearable electronics owing to their shape adaptability, good flexibility, and weavability. However, it is still a critical challenge to develop simultaneously structure stable, fast response, body conformal, mechanical robust yarn sensor using full microfibers in an industrial-scalable manner. Herein, a full-fiber auxetic-interlaced yarn sensor (AIYS) with negative Poisson's ratio is designed and fabricated using a continuous, mass-producible, structure-programmable, and low-cost spinning technology. Based on the unique microfiber interlaced architecture, AIYS simultaneously achieves a Poisson's ratio of-1.5, a robust mechanical property (0.6 cN/dtex), and a fast train-resistance responsiveness (0.025 s), which enhances conformality with the human body and quickly transduce human joint bending and/or stretching into electrical signals. Moreover, AIYS shows good flexibility, washability, weavability, and high repeatability. Furtherly, with the AIYS array, an ultrafast full-letter sign-language translation glove is developed using artificial neural network. The sign-language translation glove achieves an accuracy of 99.8% for all letters of the English alphabet within a short time of 0.25 s. Furthermore, owing to excellent full letter-recognition ability, real-time translation of daily dialogues and complex sentences is also demonstrated. The smart glove exhibits a remarkable potential in eliminating the communication barriers between signers and non-signers.

Wu Ronghui, Seo Sangjin, Ma Liyun, Bae Juyeol, Kim Taesung

2022-Jul-01

Deep learning, Interlaced yarn sensors, Negative Poisson’s ratio yarns, Sign-language translation, Smart glove

General General

Panicle Ratio Network: Streamlining rice panicle measurement by deep learning with ultra-high-definition aerial images in fields.

In Journal of experimental botany

The heading date and effective tiller percentage are important traits in rice, and they directly affect plant architecture and yield. Both traits are related to the ratio of the panicle number to the maximum tiller number, or simply called panicle ratio (PR). In this study, an automatic PR estimation model (PRNet) based on a deep convolutional neural network was developed. Ultra-high-definition unmanned aerial vehicle (UAV) images were collected from cultivated rice varieties planted in 2384 experimental plots in 2019 and 2020 and in a large field in 2021. The determination coefficient between estimated PR and ground measured PR reached 0.935, and the root mean square error (RMSE) values for the estimations of the heading date and effective tiller percentage were 0.687 days and 4.84%, respectively. Based on the result analysis, various factors affecting PR estimation and strategies for improving PR estimation accuracy were investigated. The satisfactory results obtained in this study demonstrate the feasibility of using UAV and deep learning techniques to replace ground-based manual methods to accurately extract phenotypic information of crop micro targets (such as grains per panicle, panicle flowering, etc.) for rice and potentially for other cereal crops with future research.

Guo Ziyue, Yang Chenghai, Yang Wangnen, Chen Guoxing, Jiang Zhao, Wang Botao, Zhang Jian

2022-Jul-01

deep convolutional neural network, effective tiller percentage, heading date, rice panicle ratio network, ultra-high-definition image, unmanned aerial vehicle

General General

Sequence-assignment validation in cryo-EM models with checkMySequence.

In Acta crystallographica. Section D, Structural biology

The availability of new artificial intelligence-based protein-structure-prediction tools has radically changed the way that cryo-EM maps are interpreted, but it has not eliminated the challenges of map interpretation faced by a microscopist. Models will continue to be locally rebuilt and refined using interactive tools. This inevitably results in occasional errors, among which register shifts remain one of the most difficult to identify and correct. Here, checkMySequence, a fast, fully automated and parameter-free method for detecting register shifts in protein models built into cryo-EM maps, is introduced. It is shown that the method can assist model building in cases where poorer map resolution hinders visual interpretation. It is also shown that checkMySequence could have helped to avoid a widely discussed sequence-register error in a model of SARS-CoV-2 RNA-dependent RNA polymerase that was originally detected thanks to a visual residue-by-residue inspection by members of the structural biology community. The software is freely available at https://gitlab.com/gchojnowski/checkmysequence.

Chojnowski Grzegorz

2022-Jul-01

checkMySequence, cryo-EM, model validation, register shifts, sequence assignment

Radiology Radiology

Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning.

In Journal of magnetic resonance imaging : JMRI

BACKGROUND : Accurate and rapid measurement of the MRI volume of meningiomas is essential in clinical practice to determine the growth rate of the tumor. Imperfect automation and disappointing performance for small meningiomas of previous automated volumetric tools limit their use in routine clinical practice.

PURPOSE : To develop and validate a computational model for fully automated meningioma segmentation and volume measurement on contrast-enhanced MRI scans using deep learning.

STUDY TYPE : Retrospective.

POPULATION : A total of 659 intracranial meningioma patients (median age, 59.0 years; interquartile range: 53.0-66.0 years) including 554 women and 105 men.

FIELD STRENGTH/SEQUENCE : The 1.0 T, 1.5 T, and 3.0 T; three-dimensional, T1 -weighted gradient-echo imaging with contrast enhancement.

ASSESSMENT : The tumors were manually segmented by two neurosurgeons, H.K. and C.-K.P., with 10 and 26 years of clinical experience, respectively, for use as the ground truth. Deep learning models based on U-Net and nnU-Net were trained using 459 subjects and tested for 100 patients from a single institution (internal validation set [IVS]) and 100 patients from other 24 institutions (external validation set [EVS]), respectively. The performance of each model was evaluated with the Sørensen-Dice similarity coefficient (DSC) compared with the ground truth.

STATISTICAL TESTS : According to the normality of the data distribution verified by the Shapiro-Wilk test, variables with three or more categories were compared by the Kruskal-Wallis test with Dunn's post hoc analysis.

RESULTS : A two-dimensional (2D) nnU-Net showed the highest median DSCs of 0.922 and 0.893 for the IVS and EVS, respectively. The nnU-Nets achieved superior performance in meningioma segmentation than the U-Nets. The DSCs of the 2D nnU-Net for small meningiomas less than 1 cm3 were 0.769 and 0.780 with the IVS and EVS, respectively.

DATA CONCLUSION : A fully automated and accurate volumetric measurement tool for meningioma with clinically applicable performance for small meningioma using nnU-Net was developed.

EVIDENCE LEVEL : 3 TECHNICAL EFFICACY: Stage 2.

Kang Ho, Witanto Joseph Nathanael, Pratama Kevin, Lee Doohee, Choi Kyu Sung, Choi Seung Hong, Kim Kyung-Min, Kim Min-Sung, Kim Jin Wook, Kim Yong Hwy, Park Sang Joon, Park Chul-Kee

2022-Jul-01

artificial intelligence, convolutional neural network, meningioma, nnU-Net, segmentation, volumetry

Public Health Public Health

Using Twitter Data to Understand Public Perceptions of Approved versus Off-label Use for COVID-19-related Medications.

In Journal of the American Medical Informatics Association : JAMIA

OBJECTIVE : Understanding public discourse on emergency use of unproven therapeutics is essential to monitor safe use and combat misinformation. We developed a natural language processing (NLP)-based pipeline to understand public perceptions of and stances on COVID-19-related drugs on Twitter across time.

METHODS : This retrospective study included 609,189 US-based tweets between January 29th, 2020 and November 30th, 2021 on four drugs that gained wide public attention during the COVID-19 pandemic: 1) Hydroxychloroquine and Ivermectin, drug therapies with anecdotal evidence; and 2) Molnupiravir and Remdesivir, FDA-approved treatment options for eligible patients. Time-trend analysis was used to understand the popularity and related events. Content and demographic analyses were conducted to explore potential rationales of people's stances on each drug.

RESULTS : Time-trend analysis revealed that Hydroxychloroquine and Ivermectin received much more discussion than Molnupiravir and Remdesivir, particularly during COVID-19 surges. Hydroxychloroquine and Ivermectin were highly politicized, related to conspiracy theories, hearsay, celebrity effects, etc. The distribution of stance between the two major US political parties was significantly different (p < 0.001); Republicans were much more likely to support Hydroxychloroquine (+55%) and Ivermectin (+30%) than Democrats. People with healthcare backgrounds tended to oppose Hydroxychloroquine (+7%) more than the general population; in contrast, the general population was more likely to support Ivermectin (+14%).

CONCLUSION : Our study found that social media users with have different perceptions and stances on off-label versus FDA-authorized drug use across different stages of COVID-19, indicating that health systems, regulatory agencies, and policymakers should design "targeted" strategies to monitor and reduce misinformation for promoting safe drug use. Our analysis pipeline and stance detection models are made public at https://github.com/ningkko/COVID-drug.

Hua Yining, Jiang Hang, Lin Shixu, Yang Jie, Plasek Joseph M, Bates David W, Zhou Li

2022-Jul-01

COVID-19, Deep Learning, Drug Safety, Natural Language Processing, Public Health, Social Media

General General

Beyond the Brain: MIDS Extends BIDS to Multiple Modalities and Anatomical Regions.

In Studies in health technology and informatics ; h5-index 23.0

Brain Imaging Data Structure (BIDS) provides a valuable tool to organise brain imaging data into a clear and easy standard directory structure. Moreover, BIDS is widely supported by the scientific community and has been established as a powerful standard for medical imaging management. Nonetheless, the original BIDS is restricted to magnetic resonance imaging (MRI) of the brain, limiting its implantation to other techniques and anatomical regions. We developed Medical Imaging Data Structure (MIDS), conceived to extend BIDS methodology to other anatomical regions and multiple imaging systems in these areas. The MIDS standard was developed to store and manage medical images as an extension of BIDS. It allows the user to handily save studies of multiple anatomical regions and imaging techniques. Besides, MIDS improves the classification of multiple images within the structure, allowing the possibility to unify them in a single study to apply on them preprocessing or artificial intelligence algorithms. Finally, the results generated are saved in the derivatives folder.

Saborit-Torres Jose Manuel, Nadal-Almela Silvia, Montell-Serrano Joaquim Angel, Oliver-Garcia Elena, Carceller Hector, Gómez-Ádrian Jon Ander, Caparrós-Redondo Marisa, García-García Francisco, Domenech-Fernández Julio, De La Iglesia-Vayá Maria

2022-Jun-29

BIDS, Database, OMOP, Standardization

General General

PaCAR: COVID-19 Pandemic Control Decision Making via Large-Scale Agent-Based Modeling and Deep Reinforcement Learning.

In Medical decision making : an international journal of the Society for Medical Decision Making

BACKGROUND : Policy makers are facing more complicated challenges to balance saving lives and economic development in the post-vaccination era during a pandemic. Epidemic simulation models and pandemic control methods are designed to tackle this problem. However, most of the existing approaches cannot be applied to real-world cases due to the lack of adaptability to new scenarios and micro representational ability (especially for system dynamics models), the huge computation demand, and the inefficient use of historical information.

METHODS : We propose a novel Pandemic Control decision making framework via large-scale Agent-based modeling and deep Reinforcement learning (PaCAR) to search optimal control policies that can simultaneously minimize the spread of infection and the government restrictions. In the framework, we develop a new large-scale agent-based simulator with vaccine settings implemented to be calibrated and serve as a realistic environment for a city or a state. We also design a novel reinforcement learning architecture applicable to the pandemic control problem, with a reward carefully designed by the net monetary benefit framework and a sequence learning network to extract information from the sequential epidemiological observations, such as number of cases, vaccination, and so forth.

RESULTS : Our approach outperforms the baselines designed by experts or adopted by real-world governments and is flexible in dealing with different variants, such as Alpha and Delta in COVID-19. PaCAR succeeds in controlling the pandemic with the lowest economic costs and relatively short epidemic duration and few cases. We further conduct extensive experiments to analyze the reasoning behind the resulting policy sequence and try to conclude this as an informative reference for policy makers in the post-vaccination era of COVID-19 and beyond.

LIMITATIONS : The modeling of economic costs, which are directly estimated by the level of government restrictions, is rather simple. This article mainly focuses on several specific control methods and single-wave pandemic control.

CONCLUSIONS : The proposed framework PaCAR can offer adaptive pandemic control recommendations on different variants and population sizes. Intelligent pandemic control empowered by artificial intelligence may help us make it through the current COVID-19 and other possible pandemics in the future with less cost both of lives and economy.

HIGHLIGHTS : We introduce a new efficient, large-scale agent-based epidemic simulator in our framework PaCAR, which can be applied to train reinforcement learning networks in a real-world scenario with a population of more than 10,000,000.We develop a novel learning mechanism in PaCAR, which augments reinforcement learning with sequence learning, to learn the tradeoff policy decision of saving lives and economic development in the post-vaccination era.We demonstrate that the policy learned by PaCAR outperforms different benchmark policies under various reality conditions during COVID-19.We analyze the resulting policy given by PaCAR, and the lessons may shed light on better pandemic preparedness plans in the future.

Guo Xudong, Chen Peiyu, Liang Shihao, Jiao Zengtao, Li Linfeng, Yan Jun, Huang Yadong, Liu Yi, Fan Wenhui

2022-Jul-01

COVID-19, SARS-CoV-2, agent-based modeling, artificial intelligence, epidemic simulation, health policy, infectious disease, nonpharmaceutical interventions, pandemic control, reinforcement learning

Radiology Radiology

Automated Volumetric Determination of High R2 * Regions in Substantia Nigra (SN): A Feasibility Study of Quantifying SN Atrophy in PSP.

In NMR in biomedicine ; h5-index 41.0

The establishment of an unbiased protocol for the automated volumetric measurement of iron-rich regions in the substantia nigra (SN) is clinically important for diagnosing neurodegenerative diseases exhibiting midbrain atrophy, such as progressive supranuclear palsy (PSP). This study aimed to automatically quantify the volume and surface properties of the iron-rich 3D regions in the SN using the quantitative MRI-R2 * map. 367 slices of R2 * map and susceptibility-weighted imaging (SWI) at 3T MRI from healthy control (HC) individuals and Parkinson's disease (PD) patients were used to train customized U-net++ convolutional neural network based on expert-segmented masks. Age- and sex-matched participants were selected from HC, PD, and PSP groups to automate the volumetric determination of iron-rich areas in the SN. Dice similarity coefficient (DSC) values between expert-segmented and detected masks from the proposed network were 0.91±0.07 for R2 * maps and 0.89±0.08 for SWI. Reductions in iron-rich SN volume from the R2 * map (SWI) were observed in PSP with an area under the receiver operating characteristic curve values of 0.96 (0.89) and 0.98 (0.92) compared to HC and PD, respectively. The mean curvature of the PSP showed SN deformation along the side closer to the red nucleus. We demonstrated the automated volumetric measurement of iron-rich regions in the SN using deep learning can quantify the SN atrophy in PSP compared to PD and HC.

Tessema Abel Worku, Lee Hansol, Gong Yelim, Cho Hwapyeong, Adem Hamdia Murad, Lyu Ilwoo, Lee Jae-Hyeok, Cho Hyung Joon

2022-Jul-01

Convolutional neural network, Progressive supranuclear palsy, Quantitative analysis, Segmentation, Substantia nigra

General General

Deep learning improves implant classification by dental professionals: a multi-center evaluation of accuracy and efficiency.

In Journal of periodontal & implant science

PURPOSE : The aim of this study was to evaluate and compare the accuracy performance of dental professionals in the classification of different types of dental implant systems (DISs) using panoramic radiographic images with and without the assistance of a deep learning (DL) algorithm.

METHODS : Using a self-reported questionnaire, the classification accuracy of dental professionals (including 5 board-certified periodontists, 8 periodontology residents, and 31 dentists not specialized in implantology working at 3 dental hospitals) with and without the assistance of an automated DL algorithm were determined and compared. The accuracy, sensitivity, specificity, confusion matrix, receiver operating characteristic (ROC) curves, and area under the ROC curves were calculated to evaluate the classification performance of the DL algorithm and dental professionals.

RESULTS : Using the DL algorithm led to a statistically significant improvement in the average classification accuracy of DISs (mean accuracy: 78.88%) compared to that without the assistance of the DL algorithm (mean accuracy: 63.13%, P<0.05). In particular, when assisted by the DL algorithm, board-certified periodontists (mean accuracy: 88.56%) showed higher average accuracy than did the DL algorithm, and dentists not specialized in implantology (mean accuracy: 77.83%) showed the largest improvement, reaching an average accuracy similar to that of the algorithm (mean accuracy: 80.56%).

CONCLUSIONS : The automated DL algorithm classified DISs with accuracy and performance comparable to those of board-certified periodontists, and it may be useful for dental professionals for the classification of various types of DISs encountered in clinical practice.

Lee Jae-Hong, Kim Young-Taek, Lee Jong-Bin, Jeong Seong-Nyum

2022-Jun

Artificial intelligence, Deep learning, Dental implants, Dentist

General General

PaCAR: COVID-19 Pandemic Control Decision Making via Large-Scale Agent-Based Modeling and Deep Reinforcement Learning.

In Medical decision making : an international journal of the Society for Medical Decision Making

BACKGROUND : Policy makers are facing more complicated challenges to balance saving lives and economic development in the post-vaccination era during a pandemic. Epidemic simulation models and pandemic control methods are designed to tackle this problem. However, most of the existing approaches cannot be applied to real-world cases due to the lack of adaptability to new scenarios and micro representational ability (especially for system dynamics models), the huge computation demand, and the inefficient use of historical information.

METHODS : We propose a novel Pandemic Control decision making framework via large-scale Agent-based modeling and deep Reinforcement learning (PaCAR) to search optimal control policies that can simultaneously minimize the spread of infection and the government restrictions. In the framework, we develop a new large-scale agent-based simulator with vaccine settings implemented to be calibrated and serve as a realistic environment for a city or a state. We also design a novel reinforcement learning architecture applicable to the pandemic control problem, with a reward carefully designed by the net monetary benefit framework and a sequence learning network to extract information from the sequential epidemiological observations, such as number of cases, vaccination, and so forth.

RESULTS : Our approach outperforms the baselines designed by experts or adopted by real-world governments and is flexible in dealing with different variants, such as Alpha and Delta in COVID-19. PaCAR succeeds in controlling the pandemic with the lowest economic costs and relatively short epidemic duration and few cases. We further conduct extensive experiments to analyze the reasoning behind the resulting policy sequence and try to conclude this as an informative reference for policy makers in the post-vaccination era of COVID-19 and beyond.

LIMITATIONS : The modeling of economic costs, which are directly estimated by the level of government restrictions, is rather simple. This article mainly focuses on several specific control methods and single-wave pandemic control.

CONCLUSIONS : The proposed framework PaCAR can offer adaptive pandemic control recommendations on different variants and population sizes. Intelligent pandemic control empowered by artificial intelligence may help us make it through the current COVID-19 and other possible pandemics in the future with less cost both of lives and economy.

HIGHLIGHTS : We introduce a new efficient, large-scale agent-based epidemic simulator in our framework PaCAR, which can be applied to train reinforcement learning networks in a real-world scenario with a population of more than 10,000,000.We develop a novel learning mechanism in PaCAR, which augments reinforcement learning with sequence learning, to learn the tradeoff policy decision of saving lives and economic development in the post-vaccination era.We demonstrate that the policy learned by PaCAR outperforms different benchmark policies under various reality conditions during COVID-19.We analyze the resulting policy given by PaCAR, and the lessons may shed light on better pandemic preparedness plans in the future.

Guo Xudong, Chen Peiyu, Liang Shihao, Jiao Zengtao, Li Linfeng, Yan Jun, Huang Yadong, Liu Yi, Fan Wenhui

2022-Jul-01

COVID-19, SARS-CoV-2, agent-based modeling, artificial intelligence, epidemic simulation, health policy, infectious disease, nonpharmaceutical interventions, pandemic control, reinforcement learning

General General

Derivation and validation of risk prediction for posttraumatic stress symptoms following trauma exposure.

In Psychological medicine ; h5-index 82.0

BACKGROUND : Posttraumatic stress symptoms (PTSS) are common following traumatic stress exposure (TSE). Identification of individuals with PTSS risk in the early aftermath of TSE is important to enable targeted administration of preventive interventions. In this study, we used baseline survey data from two prospective cohort studies to identify the most influential predictors of substantial PTSS.

METHODS : Self-identifying black and white American women and men (n = 1546) presenting to one of 16 emergency departments (EDs) within 24 h of motor vehicle collision (MVC) TSE were enrolled. Individuals with substantial PTSS (⩾33, Impact of Events Scale - Revised) 6 months after MVC were identified via follow-up questionnaire. Sociodemographic, pain, general health, event, and psychological/cognitive characteristics were collected in the ED and used in prediction modeling. Ensemble learning methods and Monte Carlo cross-validation were used for feature selection and to determine prediction accuracy. External validation was performed on a hold-out sample (30% of total sample).

RESULTS : Twenty-five percent (n = 394) of individuals reported PTSS 6 months following MVC. Regularized linear regression was the top performing learning method. The top 30 factors together showed good reliability in predicting PTSS in the external sample (Area under the curve = 0.79 ± 0.002). Top predictors included acute pain severity, recovery expectations, socioeconomic status, self-reported race, and psychological symptoms.

CONCLUSIONS : These analyses add to a growing literature indicating that influential predictors of PTSS can be identified and risk for future PTSS estimated from characteristics easily available/assessable at the time of ED presentation following TSE.

Kim Raphael, Lin Tina, Pang Gehao, Liu Yufeng, Tungate Andrew S, Hendry Phyllis L, Kurz Michael C, Peak David A, Jones Jeffrey, Rathlev Niels K, Swor Robert A, Domeier Robert, Velilla Marc-Anthony, Lewandowski Christopher, Datner Elizabeth, Pearson Claire, Lee David, Mitchell Patricia M, McLean Samuel A, Linnstaedt Sarah D

2022-Jul-01

Machine learning, PTSD, prediction, risk factors, trauma

General General

Using machine learning to improve diagnostic assessment of ASD in the light of specific differential and co-occurring diagnoses.

In Journal of child psychology and psychiatry, and allied disciplines

BACKGROUND : Diagnostic assessment of ASD requires substantial clinical experience and is particularly difficult in the context of other disorders with behavioral symptoms in the domain of social interaction and communication. Observation measures such as the Autism Diagnostic Observation Schedule (ADOS) do not take into account such co-occurring disorders.

METHOD : We used a well-characterized clinical sample of individuals (n = 1,251) that had received detailed outpatient evaluation for the presence of an ASD diagnosis (n = 481) and covered a range of additional overlapping diagnoses, including anxiety-related disorders (ANX, n = 122), ADHD (n = 439), and conduct disorder (CD, n = 194). We focused on ADOS module 3, covering the age range with particular high prevalence of such differential diagnoses. We used machine learning (ML) and trained random forest models on ADOS single item scores to predict a clinical best-estimate diagnosis of ASD in the context of these differential diagnoses (ASD vs. ANX, ASD vs. ADHD, ASD vs. CD), in the context of co-occurring ADHD, and an unspecific model using all available data. We employed nested cross-validation for an unbiased estimate of classification performance and made available a Webapp to showcase the results and feasibility for translation into clinical practice.

RESULTS : We obtained very good overall sensitivity (0.89-0.94) and specificity (0.87-0.89). In particular for individuals with less severe symptoms, our models showed increases of up to 35% in sensitivity or specificity. Furthermore, we analyzed item importance profiles of the ANX, ADHD, and CD models in comparison with the unspecific model revealing distinct patterns of importance for specific ADOS items with respect to differential diagnoses.

CONCLUSIONS : ML-based diagnostic classification may improve clinical decisions by utilizing the full range of information from detailed diagnostic observation instruments such as the ADOS. Importantly, this strategy might be of particular relevance for older children with less severe symptoms for whom the diagnostic decision is often particularly difficult.

Schulte-Rüther Martin, Kulvicius Tomas, Stroth Sanna, Wolff Nicole, Roessner Veit, Marschik Peter B, Kamp-Becker Inge, Poustka Luise

2022-Jul-01

Autism spectrum disorders, Machine learning, diagnosis

General General

Trends in the Prevalence of Chronic Medication Use Within Children in Israel Between 2010 and 2019: Protocol for a Retrospective Cohort Study.

In JMIR research protocols ; h5-index 26.0

BACKGROUND : Prescription of psychostimulants has significantly increased in most countries worldwide for both pre-school and school-aged children. Understanding the trends of chronic medication use among children in different age groups and socio-demographic backgrounds is essential. This must be processed by distinguishing selected therapy areas is essential to help decision-makers evaluate not only the relevant expected medication costs but also the specific services related to these areas.

OBJECTIVE : This study will analyze differences in trends regarding medications considered psycho-behavioral treatments and medications considered non-psycho-behavioral treatments and will identify risk factors and predictors for chronic medication use among children.

METHODS : This is a retrospective study. Data will be extracted from the Clalit Health Services data warehouse. For each year between 2010 and 2019, there are approximately 1,500,000 children aged 0-18 years. All Medication classes will be identified using the Anatomical Therapeutic Chemical code. A time-trend analysis will be performed to investigate if there is a significant difference between the trends of children's psycho-behavioral and non-psycho-behavioral medication prescriptions. A logistic regression combined with machine learning models will be developed to identify variables that may increase the risk for specific chronic medication types and identify children likely to get such treatment.

RESULTS : The project was funded in 2019. Data analysis is currently underway, and the results are expected to be submitted for publication in 2022. Understanding trends regarding medications considered psycho-behavioral treatments and medications considered non-psycho-behavioral treatments will support the identification of risk factors and predictors for chronic medication use among children.

CONCLUSIONS : Analyzing the response of the patient (their parents or caregivers) population over time will hopefully help improve the policies for the prescriptions and the follow-up of chronic treatments in children.

Sadaka Yair, Horwitz Dana, Wolff Leor, Meyerovitch Joseph, Peleg Assaf, Bachmat Eitan, Benis Arriel

2022-Jun-30

General General

Biogeography of cereal stemborers and their natural enemies: forecasting pest management efficacy under changing climate.

In Pest management science

BACKGROUND : Climate warming presents physiological challenges to insects, manifesting as loss of key life-history fitness traits and survival. For interacting host-parasitoid species, physiological responses to heat stress may vary thereby potentially uncoupling trophic ecological relationships. Here, we assessed heat tolerance traits and sensitivity to prevailing and future maximum temperatures for cereal stemborer pests, Chilo partellus, Busseola fusca and Sesamia calamistis and their endo-parasitoids, Cotesia sesamiae and Cotesia flavipes. We further used the machine learning algorithm, Maximum Entropy (MaxEnt), to model current and potential distribution of these species.

RESULTS : The mean critical thermal maxima (CTmax ) ranged from 39.5±0.9° C - 44.6±0.6° C and 46.8±0.7° C- 48.5±0.9° C for parasitoids and stemborers, with C. sesamiae and C. partellus exhibiting the lowest and highest CTmax respectively. From the present climate to 2050s scenario, parasitoids recorded significant reduction in warming tolerance than their hosts. Habitat suitability for all stemborer-parasitoid species was spatially heterogeneous under current and future climatic scenarios. Cotesia sesamiae C. flavipes and B. fusca exhibited significant habitat loss while C. partellus and S. calamistis showed a significant habitat gain under future 2050s predictions. Model metrics based on mean area under the curve ranged from 0.72 to 0.84 for all species, indicating good predictive performance of the models.

CONCLUSION : These results suggest C. sesamiae and C. flavipes may face survival constraints or extirpation than their pest hosts when environmental temperature reaches their upper thermal limits earlier, likely reducing pest regulation through density-mediated effects. The results demonstrate potential destabilisation of stemborer-parasitoid trophic systems potentially compromising biocontrol efficacy under climate warming. This article is protected by copyright. All rights reserved.

Mutamiswa Reyard, Chikowore Gerald, Nyamukondiwa Casper, Mudereri Bester Tawona, Khan Zeyaur Rahman, Chidawanyika Frank

2022-Jun-30

MaxEnt, biogeography, climate change, host-parasitoid interaction, warming tolerance

Radiology Radiology

Radiomics Features on Computed Tomography Combined With Clinical-Radiological Factors Predicting Progressive Hemorrhage of Cerebral Contusion.

In Frontiers in neurology

Background : Traumatic brain injury (TBI) is the main cause of death and severe disability in young adults worldwide. Progressive hemorrhage (PH) worsens the disease and can cause a poor neurological prognosis. Radiomics analysis has been used for hematoma expansion of hypertensive intracerebral hemorrhage. This study attempts to develop an optimal radiomics model based on non-contrast CT to predict PH by machine learning (ML) methods and compare its prediction performance with clinical-radiological models.

Methods : We retrospectively analyzed 165 TBI patients, including 89 patients with PH and 76 patients without PH, whose data were randomized into a training set and a testing set at a ratio of 7:3. A total of 10 different machine learning methods were used to predict PH. Univariate and multivariable logistic regression analyses were implemented to screen clinical-radiological factors and to establish a clinical-radiological model. Then, a combined model combining clinical-radiological factors with the radiomics score was constructed. The area under the receiver operating characteristic curve (AUC), accuracy and F1 score, sensitivity, and specificity were used to evaluate the models.

Results : Among the 10 various ML algorithms, the support vector machine (SVM) had the best prediction performance based on 12 radiomics features, including the AUC (training set: 0.918; testing set: 0.879) and accuracy (training set: 0.872; test set: 0.834). Among the clinical and radiological factors, the onset-to-baseline CT time, the scalp hematoma, and fibrinogen were associated with PH. The radiomics model's prediction performance was better than the clinical-radiological model, while the predictive nomogram combining the radiomics features with clinical-radiological characteristics performed best.

Conclusions : The radiomics model outperformed the traditional clinical-radiological model in predicting PH. The nomogram model of the combined radiomics features and clinical-radiological factors is a helpful tool for PH.

Yang Qingning, Sun Jun, Guo Yi, Zeng Ping, Jin Ke, Huang Chencui, Xu Jingxu, Hou Liran, Li Chuanming, Feng Junbang

2022

machine learning, nomogram, progressive hemorrhage, radiomics, traumatic brain injury

General General

Uncertainty-based Self-training for Biomedical Keyphrase Extraction.

In ... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics

To keep pace with the increased generation and digitization of documents, automated methods that can improve search, discovery and mining of the vast body of literature are essential. Keyphrases provide a concise representation by identifying salient concepts in a document. Various supervised approaches model keyphrase extraction using local context to predict the label for each token and perform much better than the unsupervised counterparts. However, existing supervised datasets have limited annotated examples to train better deep learning models. In contrast, many domains have large amount of un-annotated data that can be leveraged to improve model performance in keyphrase extraction. We introduce a self-learning based model that incorporates uncertainty estimates to select instances from large-scale unlabeled data to augment the small labeled training set. Performance evaluation on a publicly available biomedical dataset demonstrates that our method improves performance of keyphrase extraction over state of the art models.

Gero Zelalem, Ho Joyce C

2021-Jul

Biomedical text processing, Document Summarization, Keyphrase Extraction

Surgery Surgery

Deep Learning-Based Pathology Image Analysis Enhances Magee Feature Correlation With Oncotype DX Breast Recurrence Score.

In Frontiers in medicine

Background : Oncotype DX Recurrence Score (RS) has been widely used to predict chemotherapy benefits in patients with estrogen receptor-positive breast cancer. Studies showed that the features used in Magee equations correlate with RS. We aimed to examine whether deep learning (DL)-based histology image analyses can enhance such correlations.

Methods : We retrieved 382 cases with RS diagnosed between 2011 and 2015 from the Emory University and the Ohio State University. All patients received surgery. DL models were developed to detect nuclei of tumor cells and tumor-infiltrating lymphocytes (TILs) and segment tumor cell nuclei in hematoxylin and eosin (H&E) stained histopathology whole slide images (WSIs). Based on the DL-based analysis, we derived image features from WSIs, such as tumor cell number, TIL number variance, and nuclear grades. The entire patient cohorts were divided into one training set (125 cases) and two validation sets (82 and 175 cases) based on the data sources and WSI resolutions. The training set was used to train the linear regression models to predict RS. For prediction performance comparison, we used independent variables from Magee features alone or the combination of WSI-derived image and Magee features.

Results : The Pearson's correlation coefficients between the actual RS and predicted RS by DL-based analysis were 0.7058 (p-value = 1.32 × 10-13) and 0.5041 (p-value = 1.15 × 10-12) for the validation sets 1 and 2, respectively. The adjusted R 2 values using Magee features alone are 0.3442 and 0.2167 in the two validation sets, respectively. In contrast, the adjusted R 2 values were enhanced to 0.4431 and 0.2182 when WSI-derived imaging features were jointly used with Magee features.

Conclusion : Our results suggest that DL-based digital pathological features can enhance Magee feature correlation with RS.

Li Hongxiao, Wang Jigang, Li Zaibo, Dababneh Melad, Wang Fusheng, Zhao Peng, Smith Geoffrey H, Teodoro George, Li Meijie, Kong Jun, Li Xiaoxian

2022

ER+ breast cancer, Magee equation, Oncotype DX score, deep learning-based algorithm, digital pathology

Public Health Public Health

Physician Acceptance of Machine Learning for Diagnostic Purposes: Caution, Bumpy Road Ahead!

In Studies in health technology and informatics ; h5-index 23.0

This paper aims to explore physicians' adoption of Machine learning models in the healthcare process and barriers that may hinder it. A review of the literature about ML in healthcare included current and potentially beneficial clinical applications and clinicians' adoption and trust towards such applications. While some physicians are looking forward to using ML to improve their outcomes and reduce their load, we uncovered fear of unwanted outcomes and concerns about privacy of data, legal liability, and patient dissatisfaction.

Al-Edresee Thamer

2022-Jun-29

Machine learning adoption by healthcare providers, Physician adoption of machine learning, machine learning ethics, physicians trust of machine learning

Public Health Public Health

Inference Time of a CamemBERT Deep Learning Model for Sentiment Analysis of COVID Vaccines on Twitter.

In Studies in health technology and informatics ; h5-index 23.0

In previous work, we implemented a deep learning model with CamemBERT and PyTorch, and built a microservices architecture using the TorchServe serving library. Without TorchServe, inference time was three times faster when the model was loaded once in memory compared when the model was loaded each time. The preloaded model without TorchServe presented comparable inference time with the TorchServe instance. However, using a PyTorch preloaded model in a web application without TorchServe would necessitate to implement functionalities already present in TorchServe.

Guerdoux Guillaume, Tiffet Théophile, Bousquet Cedric

2022-Jun-29

Artificial Intelligence, COVID-19, MLOps, Social Media, Vaccines

oncology Oncology

Precision Medicine: An Optimal Approach to Patient Care in Renal Cell Carcinoma.

In Frontiers in medicine

Renal cell cancer (RCC) is a heterogeneous tumor that shows both intra- and inter-heterogeneity. Heterogeneity is displayed not only in different patients but also among RCC cells in the same tumor, which makes treatment difficult because of varying degrees of responses generated in RCC heterogeneous tumor cells even with targeted treatment. In that context, precision medicine (PM), in terms of individualized treatment catered for a specific patient or groups of patients, can shift the paradigm of treatment in the clinical management of RCC. Recent progress in the biochemical, molecular, and histological characteristics of RCC has thrown light on many deregulated pathways involved in the pathogenesis of RCC. As PM-based therapies are rapidly evolving and few are already in current clinical practice in oncology, one can expect that PM will expand its way toward the robust treatment of patients with RCC. This article provides a comprehensive background on recent strategies and breakthroughs of PM in oncology and provides an overview of the potential applicability of PM in RCC. The article also highlights the drawbacks of PM and provides a holistic approach that goes beyond the involvement of clinicians and encompasses appropriate legislative and administrative care imparted by the healthcare system and insurance providers. It is anticipated that combined efforts from all sectors involved will make PM accessible to RCC and other patients with cancer, making a tremendous positive leap on individualized treatment strategies. This will subsequently enhance the quality of life of patients.

Sharma Revati, Kannourakis George, Prithviraj Prashanth, Ahmed Nuzhat

2022

artificial intelligence, gut microbiome, nanomedicine, precision medicine, renal cell carcinoma

General General

Long Short-Term Memory-Based Music Analysis System for Music Therapy.

In Frontiers in psychology ; h5-index 92.0

Music can express people's thoughts and emotions. Music therapy is to stimulate and hypnotize the human brain by using various forms of music activities, such as listening, singing, playing and rhythm. With the empowerment of artificial intelligence, music therapy technology has made innovative development in the whole process of "diagnosis, treatment and evaluation." It is necessary to make use of the advantages of artificial intelligence technology to innovate music therapy methods, ensure the accuracy of treatment schemes, and provide more paths for the development of the medical field. This paper proposes an long short-term memory (LSTM)-based generation and classification algorithm for multi-voice music data. A Multi-Voice Music Generation system called MVMG based on the algorithm is developed. MVMG contains two main steps. At first, the music data are modeled to the MDPI and text sequence data by using an autoencoder model, including music features extraction and music clip representation. And then an LSTM-based music generation and classification model is developed for generating and analyzing music in specific treatment scenario. MVMG is evaluated based on the datasets collected by us: the single-melody MIDI files and the Chinese classical music dataset. The experiment shows that the highest accuracy of the autoencoder-based feature extractor can achieve 95.3%. And the average F1-score of LSTM is 95.68%, which is much higher than the DNN-based classification model.

Li Ya, Li Xiulai, Lou Zheng, Chen Chaofan

2022

LSTM, autoencoder, emotion, music analysis, music therapy, psychology

General General

Do Men Have No Need for "Feminist" Artificial Intelligence? Agentic and Gendered Voice Assistants in the Light of Basic Psychological Needs.

In Frontiers in psychology ; h5-index 92.0

Artificial Intelligence (AI) is supposed to perform tasks autonomously, make competent decisions, and interact socially with people. From a psychological perspective, AI can thus be expected to impact users' three Basic Psychological Needs (BPNs), namely (i) autonomy, (ii) competence, and (iii) relatedness to others. While research highlights the fulfillment of these needs as central to human motivation and well-being, their role in the acceptance of AI applications has hitherto received little consideration. Addressing this research gap, our study examined the influence of BPN Satisfaction on Intention to Use (ITU) an AI assistant for personal banking. In a 2×2 factorial online experiment, 282 participants (154 males, 126 females, two non-binary participants) watched a video of an AI finance coach with a female or male synthetic voice that exhibited either high or low agency (i.e., capacity for self-control). In combination, these factors resulted either in AI assistants conforming to traditional gender stereotypes (e.g., low-agency female) or in non-conforming conditions (e.g., high-agency female). Although the experimental manipulations had no significant influence on participants' relatedness and competence satisfaction, a strong effect on autonomy satisfaction was found. As further analyses revealed, this effect was attributable only to male participants, who felt their autonomy need significantly more satisfied by the low-agency female assistant, consistent with stereotypical images of women, than by the high-agency female assistant. A significant indirect effects model showed that the greater autonomy satisfaction that men, unlike women, experienced from the low-agency female assistant led to higher ITU. The findings are discussed in terms of their practical relevance and the risk of reproducing traditional gender stereotypes through technology design.

Moradbakhti Laura, Schreibelmayr Simon, Mara Martina

2022

Artificial Intelligence, agency, autonomy, competence, gender stereotypes, relatedness, technology acceptance, voice assistants

General General

Lessons Learned and Future Directions of MetaTutor: Leveraging Multichannel Data to Scaffold Self-Regulated Learning With an Intelligent Tutoring System.

In Frontiers in psychology ; h5-index 92.0

Self-regulated learning (SRL) is critical for learning across tasks, domains, and contexts. Despite its importance, research shows that not all learners are equally skilled at accurately and dynamically monitoring and regulating their self-regulatory processes. Therefore, learning technologies, such as intelligent tutoring systems (ITSs), have been designed to measure and foster SRL. This paper presents an overview of over 10 years of research on SRL with MetaTutor, a hypermedia-based ITS designed to scaffold college students' SRL while they learn about the human circulatory system. MetaTutor's architecture and instructional features are designed based on models of SRL, empirical evidence on human and computerized tutoring principles of multimedia learning, Artificial Intelligence (AI) in educational systems for metacognition and SRL, and research on SRL from our team and that of other researchers. We present MetaTutor followed by a synthesis of key research findings on the effectiveness of various versions of the system (e.g., adaptive scaffolding vs. no scaffolding of self-regulatory behavior) on learning outcomes. First, we focus on findings from self-reports, learning outcomes, and multimodal data (e.g., log files, eye tracking, facial expressions of emotion, screen recordings) and their contributions to our understanding of SRL with an ITS. Second, we elaborate on the role of embedded pedagogical agents (PAs) as external regulators designed to scaffold learners' cognitive and metacognitive SRL strategy use. Third, we highlight and elaborate on the contributions of multimodal data in measuring and understanding the role of cognitive, affective, metacognitive, and motivational (CAMM) processes. Additionally, we unpack some of the challenges these data pose for designing real-time instructional interventions that scaffold SRL. Fourth, we present existing theoretical, methodological, and analytical challenges and briefly discuss lessons learned and open challenges.

Azevedo Roger, Bouchet François, Duffy Melissa, Harley Jason, Taub Michelle, Trevors Gregory, Cloude Elizabeth, Dever Daryn, Wiedbusch Megan, Wortha Franz, Cerezo Rebeca

2022

intelligent tutoring systems, learning, metacognition, multimodal data, pedagogical agents, scaffolding, self-regulated learning, trace data

General General

A Methodology for Evaluating Operator Usage of Machine Learning Recommendations for Power Grid Contingency Analysis.

In Frontiers in big data

This work presents the application of a methodology to measure domain expert trust and workload, elicit feedback, and understand the technological usability and impact when a machine learning assistant is introduced into contingency analysis for real-time power grid simulation. The goal of this framework is to rapidly collect and analyze a broad variety of human factors data in order to accelerate the development and evaluation loop for deploying machine learning applications. We describe our methodology and analysis, and we discuss insights gained from a pilot participant about the current usability state of an early technology readiness level (TRL) artificial neural network (ANN) recommender.

Wenskovitch John, Jefferson Brett, Anderson Alexander, Baweja Jessica, Ciesielski Danielle, Fallon Corey

2022

cognitive load, contingency analysis, human-machine teaming, power grid, trust evaluation

General General

Individual Tree Crown Segmentation and Crown Width Extraction From a Heightmap Derived From Aerial Laser Scanning Data Using a Deep Learning Framework.

In Frontiers in plant science

Deriving individual tree crown (ITC) information from light detection and ranging (LiDAR) data is of great significance to forest resource assessment and smart management. After proof-of-concept studies, advanced deep learning methods have been shown to have high efficiency and accuracy in remote sensing data analysis and geoscience problem solving. This study proposes a novel concept for synergetic use of the YOLO-v4 deep learning network based on heightmaps directly generated from airborne LiDAR data for ITC segmentation and a computer graphics algorithm for refinement of the segmentation results involving overlapping tree crowns. This concept overcomes the limitations experienced by existing ITC segmentation methods that use aerial photographs to obtain texture and crown appearance information and commonly encounter interference due to heterogeneous solar illumination intensities or interlacing branches and leaves. Three generative adversarial networks (WGAN, CycleGAN, and SinGAN) were employed to generate synthetic images. These images were coupled with manually labeled training samples to train the network. Three forest plots, namely, a tree nursery, forest landscape and mixed tree plantation, were used to verify the effectiveness of our approach. The results showed that the overall recall of our method for detecting ITCs in the three forest plot types reached 83.6%, with an overall precision of 81.4%. Compared with reference field measurement data, the coefficient of determination R 2 was ≥ 79.93% for tree crown width estimation, and the accuracy of our deep learning method was not influenced by the values of key parameters, yielding 3.9% greater accuracy than the traditional watershed method. The results demonstrate an enhancement of tree crown segmentation in the form of a heightmap for different forest plot types using the concept of deep learning, and our method bypasses the visual complications arising from aerial images featuring diverse textures and unordered scanned points with irregular geometrical properties.

Sun Chenxin, Huang Chengwei, Zhang Huaiqing, Chen Bangqian, An Feng, Wang Liwen, Yun Ting

2022

airborne LiDAR, deep learning, forest parameter retrieval, heightmap, individual tree crown segmentation

General General

A Reliable Method to Recognize Soybean Seed Maturation Stages Based on Autofluorescence-Spectral Imaging Combined With Machine Learning Algorithms.

In Frontiers in plant science

In recent years, technological innovations have allowed significant advances in the diagnosis of seed quality. Seeds with superior physiological quality are those with the highest level of physiological maturity and the integration of rapid and precise methods to separate them contributes to better performance in the field. Autofluorescence-spectral imaging is an innovative technique based on fluorescence signals from fluorophores present in seed tissues, which have biological implications for seed quality. Thus, through this technique, it would be possible to classify seeds in different maturation stages. To test this, we produced plants of a commercial cultivar (MG/BR 46 "Conquista") and collected the seeds at five reproductive (R) stages: R7.1 (beginning of maturity), R7.2 (mass maturity), R7.3 (seed disconnected from the mother plant), R8 (harvest point), and R9 (final maturity). Autofluorescence signals were extracted from images captured at different excitation/emission combinations. In parallel, we investigated physical parameters, germination, vigor and the dynamics of pigments in seeds from different maturation stages. To verify the accuracy in predicting the seed maturation stages based on autofluorescence-spectral imaging, we created machine learning models based on three algorithms: (i) random forest, (ii) neural network, and (iii) support vector machine. Here, we reported the unprecedented use of the autofluorescence-spectral technique to classify the maturation stages of soybean seeds, especially using the excitation/emission combination of chlorophyll a (660/700 nm) and b (405/600 nm). Taken together, the machine learning algorithms showed high performance segmenting the different stages of seed maturation. In summary, our results demonstrated that the maturation stages of soybean seeds have their autofluorescence-spectral identity in the wavelengths of chlorophylls, which allows the use of this technique as a marker of seed maturity and superior physiological quality.

Batista Thiago Barbosa, Mastrangelo Clíssia Barboza, de Medeiros André Dantas, Petronilio Ana Carolina Picinini, Fonseca de Oliveira Gustavo Roberto, Dos Santos Isabela Lopes, Crusciol Carlos Alexandre Costa, Amaral da Silva Edvaldo Aparecido

2022

Glycine max, chlorophyll fluorescence, seed maturity, seed quality, support vector machine

Radiology Radiology

Transformer-Based Deep-Learning Algorithm for Discriminating Demyelinating Diseases of the Central Nervous System With Neuroimaging.

In Frontiers in immunology ; h5-index 100.0

Background : Differential diagnosis of demyelinating diseases of the central nervous system is a challenging task that is prone to errors and inconsistent reading, requiring expertise and additional examination approaches. Advancements in deep-learning-based image interpretations allow for prompt and automated analyses of conventional magnetic resonance imaging (MRI), which can be utilized in classifying multi-sequence MRI, and thus may help in subsequent treatment referral.

Methods : Imaging and clinical data from 290 patients diagnosed with demyelinating diseases from August 2013 to October 2021 were included for analysis, including 67 patients with multiple sclerosis (MS), 162 patients with aquaporin 4 antibody-positive (AQP4+) neuromyelitis optica spectrum disorder (NMOSD), and 61 patients with myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD). Considering the heterogeneous nature of lesion size and distribution in demyelinating diseases, multi-modal MRI of brain and/or spinal cord were utilized to build the deep-learning model. This novel transformer-based deep-learning model architecture was designed to be versatile in handling with multiple image sequences (coronal T2-weighted and sagittal T2-fluid attenuation inversion recovery) and scanning locations (brain and spinal cord) for differentiating among MS, NMOSD, and MOGAD. Model performances were evaluated using the area under the receiver operating curve (AUC) and the confusion matrices measurements. The classification accuracy between the fusion model and the neuroradiological raters was also compared.

Results : The fusion model that was trained with combined brain and spinal cord MRI achieved an overall improved performance, with the AUC of 0.933 (95%CI: 0.848, 0.991), 0.942 (95%CI: 0.879, 0.987) and 0.803 (95%CI: 0.629, 0.949) for MS, AQP4+ NMOSD, and MOGAD, respectively. This exceeded the performance using the brain or spinal cord MRI alone for the identification of the AQP4+ NMOSD (AUC of 0.940, brain only and 0.689, spinal cord only) and MOGAD (0.782, brain only and 0.714, spinal cord only). In the multi-category classification, the fusion model had an accuracy of 81.4%, which was significantly higher compared to rater 1 (64.4%, p=0.04<0.05) and comparable to rater 2 (74.6%, p=0.388).

Conclusion : The proposed novel transformer-based model showed desirable performance in the differentiation of MS, AQP4+ NMOSD, and MOGAD on brain and spinal cord MRI, which is comparable to that of neuroradiologists. Our model is thus applicable for interpretating conventional MRI in the differential diagnosis of demyelinating diseases with overlapping lesions.

Huang Chuxin, Chen Weidao, Liu Baiyun, Yu Ruize, Chen Xiqian, Tang Fei, Liu Jun, Lu Wei

2022

MRI, deep learning, demyelinating disease, differential diagnosis, multiple sclerosis, myelin oligodendrocyte glycoprotein antibody-associated disease, neuromyelitis optica spectrum disorder, transformer

General General

How Are Patented AI, Software and Robot Technologies Related to Wage Changes in the United States?

In Frontiers in artificial intelligence

We analyze the relationships of three different types of patented technologies, namely artificial intelligence, software and industrial robots, with individual-level wage changes in the United States from 2011 to 2021. The aim of the study is to investigate if the availability of AI technologies is associated with increases or decreases in individual workers' wages and how this association compares to previous innovations related to software and industrial robots. Our analysis is based on available indicators extracted from the text of patents to measure the exposure of occupations to these three types of technologies. We combine data on individual wages for the United States with the new technology measures and regress individual annual wage changes on these measures controlling for a variety of other factors. Our results indicate that innovations in software and industrial robots are associated with wage decreases, possibly indicating a large displacement effect of these technologies on human labor. On the contrary, for innovations in AI, we find wage increases, which may indicate that productivity effects and effects coming from the creation of new human tasks are larger than displacement effects of AI. AI exposure is associated with positive wage changes in services, whereas exposure to robots is associated with negative wage changes in manufacturing. The relationship of the AI exposure measure with wage increases has become stronger in 2016-2021 in comparison to the 5 years before. JEL Classification: J24, J31, O33.

Fossen Frank M, Samaan Daniel, Sorgner Alina

2022

artificial intelligence, labor market, robots, software, wage dynamics

General General

Facing the FACS-Using AI to Evaluate and Control Facial Action Units in Humanoid Robot Face Development.

In Frontiers in robotics and AI

This paper presents a new approach for evaluating and controlling expressive humanoid robotic faces using open-source computer vision and machine learning methods. Existing research in Human-Robot Interaction lacks flexible and simple tools that are scalable for evaluating and controlling various robotic faces; thus, our goal is to demonstrate the use of readily available AI-based solutions to support the process. We use a newly developed humanoid robot prototype intended for medical training applications as a case example. The approach automatically captures the robot's facial action units through a webcam during random motion, which are components traditionally used to describe facial muscle movements in humans. Instead of manipulating the actuators individually or training the robot to express specific emotions, we propose using action units as a means for controlling the robotic face, which enables a multitude of ways to generate dynamic motion, expressions, and behavior. The range of action units achieved by the robot is thus analyzed to discover its expressive capabilities and limitations and to develop a control model by correlating action units to actuation parameters. Because the approach is not dependent on specific facial attributes or actuation capabilities, it can be used for different designs and continuously inform the development process. In healthcare training applications, our goal is to establish a prerequisite of expressive capabilities of humanoid robots bounded by industrial and medical design constraints. Furthermore, to mediate human interpretation and thus enable decision-making based on observed cognitive, emotional, and expressive cues, our approach aims to find the minimum viable expressive capabilities of the robot without having to optimize for realism. The results from our case example demonstrate the flexibility and efficiency of the presented AI-based solutions to support the development of humanoid facial robots.

Auflem Marius, Kohtala Sampsa, Jung Malte, Steinert Martin

2022

artificial intelligence, facial action units, facial expression, humanoid robots, medical simulation, robot development

Public Health Public Health

The Impact of COVID-19 on Mental Health Services in Scotland, UK.

In Studies in health technology and informatics ; h5-index 23.0

There is a global emergency in relation to mental health (MH) and healthcare. In the UK each year, 1 in 4 people will experience MH problems. Healthcare services are increasingly oversubscribed, and COVID-19 has deepened the healthcare gap. We investigated the effect of COVID-19 on waiting times for MH services in Scotland. We used national registers of MH services provided by Public Health Scotland. The results show that waiting times for adults and children increased drastically during the pandemic. This was seen nationally and across most of the administrative regions of Scotland. We find, however, that child and adolescent services were comparatively less impacted by the pandemic than adult services. This is potentially due to prioritisation of paediatric patients, or due to an increasing demand on adult services triggered by the pandemic itself.

Cooke Elizabeth A, Lemanska Agnieszka, Livings Jennifer, Thomas Spencer A

2022-Jun-29

COVID-19, Deep Learning, Mental Health, Visualization

Public Health Public Health

Comparison of Non-AI and AI-Enabled M-Health Platforms for COVID-19 Self Screening in Indonesia.

In Studies in health technology and informatics ; h5-index 23.0

This study aimed to analyze and differentiate the role of AI and no AI-supported m-health platforms for COVID-19 self-screening in Indonesia. We utilized a mysterious shopping method to develop four standardized cases with various severity levels of COVID-19 tested in Indonesia's most popular mHealth platforms. We selected seven apps from the top 200 free mHealth apps in the "Medical" category in the Google Play Store equipped with COVID-19 symptom checkers. A total of 36 teleconsultations were performed in four chatbot-based, two apps supported with AI combined with a human-based approach, and three apps with the human-based process. Teleconsultations were recorded, classified, and analyzed compared with the COVID-19 guideline by the MoH of Indonesia. The study indicated that most of the self-screening provided questions that had consistently led to the COVID-19 condition such as cough, fever, and shortness of breath and followed the guideline from the national health authority.

Andriani Sekar Putri, Adhyanacarira Padmanaba, Fuad Anis, Pertiwi Ariani Arista Putri

2022-Jun-29

COVID-19, artificial intelligence, mHealth apps, self-screening

General General

Study of the Economic, Environmental, and Social Factors Affecting Chinese Residents' Health Based on Machine Learning.

In Frontiers in public health

The Healthy China Strategy puts realistic demands for residents' health levels, but the reality is that various factors can affect health. In order to clarify which factors have a great impact on residents' health, based on China's provincial panel data from 2011 to 2018, this paper selects 17 characteristic variables from the three levels of economy, environment, and society and uses the XG boost algorithm and Random forest algorithm based on recursive feature elimination to determine the influencing variables. The results show that at the economic level, the number of industrial enterprises above designated size, industrial added value, population density, and per capita GDP have a greater impact on the health of residents. At the environmental level, coal consumption, energy consumption, total wastewater discharge, and solid waste discharge have a greater impact on the health level of residents. Therefore, the Chinese government should formulate targeted measures at both economic and environmental levels, which is of great significance to realizing the Healthy China strategy.

Xu Hui, Pan Wei, Xin Meng, Pan Wulin, Hu Cheng, Wanqiang Dai, Huang Ge

2022

economic factors, environmental factors, machine learning, “residents health”, social factors

General General

Combined Aerobic Exercise and Neurofeedback Lead to Improved Task-Relevant Intrinsic Network Synchrony.

In Frontiers in human neuroscience ; h5-index 79.0

Lifestyle interventions such as exercise and mindfulness training have the potential to ameliorate mental health symptoms and restore dysregulated intrinsic connectivity network (ICN) dynamics, seen in many psychopathologies. Multiple lifestyle interventions, in combination, may interact synergistically for enhanced benefits. While the impacts of lifestyle interventions on subjective measures of mood are well-documented, their impacts on ICN dynamics are not well-established. In this study, we assessed the validity of EEG-derived measures of ICN dynamics as potential markers of mood disorders, by tracking ICN dynamics and mood symptoms through the course of a longitudinal exercise intervention. Specifically, we investigated the separate and combined effects of aerobic exercise and mindfulness-like neurofeedback training on task-linked ICN dynamics of the default mode network (DMN), central executive network (CEN), and salience network (SN). Participants were assigned pseudo-randomly into four experimental conditions-Control, Running, Neurofeedback, and Combined, performing the corresponding intervention for 16 sessions across 8 weeks. Intervention-linked changes in ICN dynamics were studied using EEG-based neuroimaging scans before and after the 8-week intervention, during which participants performed multiple blocks of autobiographical memory recall (AM) and working memory (WM) trials, designed to activate the DMN and CEN, respectively, and to activate the SN in conjunction with the task-appropriate network. The EEG-based features for classification of the three core networks had been identified in our prior research from simultaneously recorded EEG and fMRI during the same AM and WM tasks. We categorized participants as "responders" or "non-responders" based on whether the exercise intervention increased their aerobic capacity (VO2-max) (Running/Combined group), and/or neurofeedback increased the percentage time spent in the calm mindfulness state (Neurofeedback/Combined group). In responders, compared to each intervention alone, the combined exercise-neurofeedback intervention resulted in a more healthy CEN-SN synchrony pattern. Interestingly, non-responders to neurofeedback exhibited a maladaptive pattern of persistent, task-inappropriate DMN-SN synchrony which we speculate could be linked to depressive rumination. Furthermore, the CEN-SN synchrony at baseline predicted NFB response with up to 80% accuracy, demonstrating the potential utility of such network-based biomarkers in personalizing intervention plans.

Shaw Saurabh Bhaskar, Levy Yarden, Mizzi Allison, Herman Gabrielle, McKinnon Margaret C, Heisz Jennifer J, Becker Suzanna

2022

aerobic exercise, central executive network (CEN), default mode network (DMN), intrinsic connectivity networks (ICN), mindfulness, neurofeedback, salience network (SN), tri-network model

General General

An Improved Transformer-Based Neural Machine Translation Strategy: Interacting-Head Attention.

In Computational intelligence and neuroscience

Transformer-based models have gained significant advances in neural machine translation (NMT). The main component of the transformer is the multihead attention layer. In theory, more heads enhance the expressive power of the NMT model. But this is not always the case in practice. On the one hand, the computations of each head attention are conducted in the same subspace, without considering the different subspaces of all the tokens. On the other hand, the low-rank bottleneck may occur, when the number of heads surpasses a threshold. To address the low-rank bottleneck, the two mainstream methods make the head size equal to the sequence length and complicate the distribution of self-attention heads. However, these methods are challenged by the variable sequence length in the corpus and the sheer number of parameters to be learned. Therefore, this paper proposes the interacting-head attention mechanism, which induces deeper and wider interactions across the attention heads by low-dimension computations in different subspaces of all the tokens, and chooses the appropriate number of heads to avoid low-rank bottleneck. The proposed model was tested on machine translation tasks of IWSLT2016 DE-EN, WMT17 EN-DE, and WMT17 EN-CS. Compared to the original multihead attention, our model improved the performance by 2.78 BLEU/0.85 WER/2.90 METEOR/2.65 ROUGE_L/0.29 CIDEr/2.97 YiSi and 2.43 BLEU/1.38 WER/3.05 METEOR/2.70 ROUGE_L/0.30 CIDEr/3.59 YiSi on the evaluation set and the test set, respectively, for IWSLT2016 DE-EN, 2.31 BLEU/5.94 WER/1.46 METEOR/1.35 ROUGE_L/0.07 CIDEr/0.33 YiSi and 1.62 BLEU/6.04 WER/1.39 METEOR/0.11 CIDEr/0.87 YiSi on the evaluation set and newstest2014, respectively, for WMT17 EN-DE, and 3.87 BLEU/3.05 WER/9.22 METEOR/3.81 ROUGE_L/0.36 CIDEr/4.14 YiSi and 4.62 BLEU/2.41 WER/9.82 METEOR/4.82 ROUGE_L/0.44 CIDEr/5.25 YiSi on the evaluation set and newstest2014, respectively, for WMT17 EN-CS.

Li Dongxing, Luo Zuying

2022

General General

Automation of Cephalometrics Using Machine Learning Methods.

In Computational intelligence and neuroscience

Cephalometry is a medical test that can detect teeth, skeleton, or appearance problems. In this scenario, the patient's lateral radiograph of the face was utilised to construct a tracing from the tracing of lines on the lateral radiograph of the face of the soft and hard structures (skin and bone, respectively). Certain cephalometric locations and characteristic lines and angles are indicated after the tracing is completed to do the real examination. In this unique study, it is proposed that machine learning models be employed to create cephalometry. These models can recognise cephalometric locations in X-ray images, allowing the study's computing procedure to be completed faster. To correlate a probability map with an input image, they combine an Autoencoder architecture with convolutional neural networks and Inception layers. These innovative architectures were demonstrated. When many models were compared, it was observed that they all performed admirably in this task.

Alshamrani Khalaf, Alshamrani Hassan, Alqahtani F F, Alshehri Ali H

2022

General General

Music Emotion Classification Method Based on Deep Learning and Explicit Sparse Attention Network.

In Computational intelligence and neuroscience

In order to improve the accuracy of music emotion recognition and classification, this study combines an explicit sparse attention network with deep learning and proposes an effective emotion recognition and classification method for complex music data sets. First, the method uses fine-grained segmentation and other methods to preprocess the sample data set, so as to provide a high-quality input data sample set for the classification model. The explicit sparse attention network is introduced into the deep learning network to reduce the influence of irrelevant information on the recognition results and improve the emotion classification and recognition ability of music sample data set. The simulation experiment is based on the actual data set of the network. The experimental results show that the recognition accuracy of the proposed method is 0.71 for happy emotions and 0.688 for sad emotions. It has a good ability of music emotion recognition and classification.

Jia Xiaoguang

2022

General General

Feature Extraction of Athlete's Post-Match Psychological and Emotional Changes Based on Deep Learning.

In Computational intelligence and neuroscience

Athletes have had to deal with significant shifts in the way they think about psychology and emotion before and after attending a match in their respective fields. It has become increasingly difficult for players of any sport to overcome these differences due to massive technological advancements that aid in analyzing the difficulties of an athlete. The trainer can use the results of the analysis to help motivate and prepare the athletes for the upcoming competitions. The analysis in this study is based on information about the athletes who competed in the Tokyo Olympics. Deep learning models were used to evaluate the study. Image feature detection can be accomplished through the application of a machine learning technique known as deep learning. It employs a neural network, a computer system that mimics the human brain's multiple layers. One or more unique features can be extracted from each layer. A deep learning model called the behavior recognition algorithm is used for the research. The questionnaire from the dataset was used to generate the results of the analysis.

Zhang Shuchang, Shan Fengjun

2022

General General

Application of Machine Learning to Predict Estimated Ultimate Recovery for Multistage Hydraulically Fractured Wells in Niobrara Shale Formation.

In Computational intelligence and neuroscience

The completion design of multistage hydraulic fractured wells including the cluster spacing injected proppant and slurry volumes has shown a great influence on the well production rates and estimated ultimate recovery (EUR). EUR estimation is a critical process to evaluate the well profitability. This study proposes the use of different machine learning techniques to predict the EUR as a function of the completion design including the lateral length, the number of stages, the total injected proppant and slurry volumes, and the maximum treating pressure measured during the fracturing operations. A data set of 200 well production data and completion designs was collected from oil production wells in the Niobrara shale formation. Artificial neural network (ANN) and random forest (RF) techniques were implemented to predict EUR from the completion design. The results showed a low accuracy of direct prediction of the EUR from the completion design. Hence, an intermediate step of estimating the initial well production rate (Q i ) from the completion data was carried out, and then, the Q i and the completion design were used as input parameters to predict the EUR. The ANN and RF models accurately predicted the EUR from the completion design data and the estimated Q i . The correlation coefficient (R) values between actual EUR and predicted EUR from the ANN model were 0.96 and 0.95 compared with 0.99 and 0.95 from the RF model for training and testing, respectively. A new correlation was developed based on the weight and biases from the optimized ANN model with an R value of 0.95. This study provides ML application with an empirical correlation to predict the EUR from the completion design parameters at an early time without the need for complex numerical simulation analysis. The developed models require only the initial flow rate along with the completion design to predict EUR with high certainty without the need for several months of production similar to the DCA models.

Ibrahim Ahmed Farid, Alarifi Sulaiman A, Elkatatny Salaheldin

2022

General General

Znet: Deep Learning Approach for 2D MRI Brain Tumor Segmentation.

In IEEE journal of translational engineering in health and medicine

BACKGROUND : Detection and segmentation of brain tumors using MR images are challenging and valuable tasks in the medical field. Early diagnosing and localizing of brain tumors can save lives and provide timely options for physicians to select efficient treatment plans. Deep learning approaches have attracted researchers in medical imaging due to their capacity, performance, and potential to assist in accurate diagnosis, prognosis, and medical treatment technologies.

METHODS AND PROCEDURES : This paper presents a novel framework for segmenting 2D brain tumors in MR images using deep neural networks (DNN) and utilizing data augmentation strategies. The proposed approach (Znet) is based on the idea of skip-connection, encoder-decoder architectures, and data amplification to propagate the intrinsic affinities of a relatively smaller number of expert delineated tumors, e.g., hundreds of patients of the low-grade glioma (LGG), to many thousands of synthetic cases.

RESULTS : Our experimental results showed high values of the mean dice similarity coefficient (dice = 0.96 during model training and dice = 0.92 for the independent testing dataset). Other evaluation measures were also relatively high, e.g., pixel accuracy = 0.996, F1 score = 0.81, and Matthews Correlation Coefficient, MCC = 0.81. The results and visualization of the DNN-derived tumor masks in the testing dataset showcase the ZNet model's capability to localize and auto-segment brain tumors in MR images. This approach can further be generalized to 3D brain volumes, other pathologies, and a wide range of image modalities.

CONCLUSION : We can confirm the ability of deep learning methods and the proposed Znet framework to detect and segment tumors in MR images. Furthermore, pixel accuracy evaluation may not be a suitable evaluation measure for semantic segmentation in case of class imbalance in MR images segmentation. This is because the dominant class in ground truth images is the background. Therefore, a high value of pixel accuracy can be misleading in some computer vision applications. On the other hand, alternative evaluation metrics, such as dice and IoU (Intersection over Union), are more factual for semantic segmentation.

CLINICAL IMPACT : Artificial intelligence (AI) applications in medicine are advancing swiftly, however, there is a lack of deployed techniques in clinical practice. This research demonstrates a practical example of AI applications in medical imaging, which can be deployed as a tool for auto-segmentation of tumors in MR images.

Ottom Mohammad Ashraf, Rahman Hanif Abdul, Dinov Ivo D

2022

Brain tumor, augmentation, deep learning, neural networks, region segmentation

General General

Digital refocusing based on deep learning in optical coherence tomography.

In Biomedical optics express

We present a deep learning-based digital refocusing approach to extend depth of focus for optical coherence tomography (OCT) in this paper. We built pixel-level registered pairs of en face low-resolution (LR) and high-resolution (HR) OCT images based on experimental data and introduced the receptive field block into the generative adversarial networks to learn the complex mapping relationship between LR-HR image pairs. It was demonstrated by results of phantom and biological samples that the lateral resolutions of OCT images were improved in a large imaging depth clearly. We firmly believe deep learning methods have broad prospects in optimizing OCT imaging.

Yuan Zhuoqun, Yang Di, Yang Zihan, Zhao Jingzhu, Liang Yanmei

2022-May-01

Ophthalmology Ophthalmology

Feasibility of the soft attention-based models for automatic segmentation of OCT kidney images.

In Biomedical optics express

Clinically, optical coherence tomography (OCT) has been utilized to obtain the images of the kidney's proximal convoluted tubules (PCTs), which can be used to quantify the morphometric parameters such as tubular density and diameter. Such parameters are useful for evaluating the status of the donor kidney for transplant. Quantifying PCTs from OCT images by human readers is a time-consuming and tedious process. Despite the fact that conventional deep learning models such as conventional neural networks (CNNs) have achieved great success in the automatic segmentation of kidney OCT images, gaps remain regarding the segmentation accuracy and reliability. Attention-based deep learning model has benefits over regular CNNs as it is intended to focus on the relevant part of the image and extract features for those regions. This paper aims at developing an Attention-based UNET model for automatic image analysis, pattern recognition, and segmentation of kidney OCT images. We evaluated five methods including the Residual-Attention-UNET, Attention-UNET, standard UNET, Residual UNET, and fully convolutional neural network using 14403 OCT images from 169 transplant kidneys for training and testing. Our results show that Residual-Attention-UNET outperformed the other four methods in segmentation by showing the highest values of all the six metrics including dice score (0.81 ± 0.01), intersection over union (IOU, 0.83 ± 0.02), specificity (0.84 ± 0.02), recall (0.82 ± 0.03), precision (0.81 ± 0.01), and accuracy (0.98 ± 0.08). Our results also show that the performance of the Residual-Attention-UNET is equivalent to the human manual segmentation (dice score = 0.84 ± 0.05). Residual-Attention-UNET and Attention-UNET also demonstrated good performance when trained on a small dataset (3456 images) whereas the performance of the other three methods dropped dramatically. In conclusion, our results suggested that the soft Attention-based models and specifically Residual-Attention-UNET are powerful and reliable methods for tubule lumen identification and segmentation and can help clinical evaluation of transplant kidney viability as fast and accurate as possible.

Moradi Mousa, Du Xian, Huan Tianxiao, Chen Yu

2022-May-01

General General

Determining the Set of Items to Include in Breast Operative Reports, Using Clustering Algorithms on Retrospective Data Extracted from Clinical DataWarehouse.

In Studies in health technology and informatics ; h5-index 23.0

Medical reports are key elements to guarantee the quality, and continuity of care but their quality remains an issue. Standardization and structuration of reports can increase their quality, but are usually based on expert opinions. Here, we hypothesize that a structured model of medical reports could be learnt using machine learning on retrospective medical reports extracted from clinical data warehouses (CDW). To investigate our hypothesis, we extracted breast cancer operative reports from our CDW. Each document was preprocessed and split into sentences. Clustering was performed using TFIDF, Paraphrase or Universal Sentence Encoder along with K-Means, DBSCAN, or Hierarchical clustering. The best couple was TFIDF/K-Means, providing a sentence coverage of 89 % on our dataset; and allowing to identify 7 main categories of items to include in breast cancer operative reports. These results are encouraging for a document preset creation task and should then be validated and implemented in real life.

Boukobza Adrien, Wack Maxime, Neuraz Antoine, Geromin Daniela, Badoual Cécile, Bats Anne-Sophie, Burgun Anita, Koual Meriem, Tsopra Rosy

2022-Jun-29

Breast cancer, Clustering, Machine learning, NLP

General General

Health-Related Content in Transformer-Based Deep Neural Network Language Models: Exploring Cross-Linguistic Syntactic Bias.

In Studies in health technology and informatics ; h5-index 23.0

This paper explores a methodology for bias quantification in transformer-based deep neural network language models for Chinese, English, and French. When queried with health-related mythbusters on COVID-19, we observe a bias that is not of a semantic/encyclopaedical knowledge nature, but rather a syntactic one, as predicted by theoretical insights of structural complexity. Our results highlight the need for the creation of health-communication corpora as training sets for deep learning.

Samo Giuseppe, Bonan Caterina, Si Fuzhen

2022-Jun-29

COVID-19, Corpora, Knowledge Reproduction, Language Models, Natural Language Processing

Ophthalmology Ophthalmology

Improving foveal avascular zone segmentation in fluorescein angiograms by leveraging manual vessel labels from public color fundus pictures.

In Biomedical optics express

In clinical routine, ophthalmologists frequently analyze the shape and size of the foveal avascular zone (FAZ) to detect and monitor retinal diseases. In order to extract those parameters, the contours of the FAZ need to be segmented, which is normally achieved by analyzing the retinal vasculature (RV) around the macula in fluorescein angiograms (FA). Computer-aided segmentation methods based on deep learning (DL) can automate this task. However, current approaches for segmenting the FAZ are often tailored to a specific dataset or require manual initialization. Furthermore, they do not take the variability and challenges of clinical FA into account, which are often of low quality and difficult to analyze. In this paper we propose a DL-based framework to automatically segment the FAZ in challenging FA scans from clinical routine. Our approach mimics the workflow of retinal experts by using additional RV labels as a guidance during training. Hence, our model is able to produce RV segmentations simultaneously. We minimize the annotation work by using a multi-modal approach that leverages already available public datasets of color fundus pictures (CFPs) and their respective manual RV labels. Our experimental evaluation on two datasets with FA from 1) clinical routine and 2) large multicenter clinical trials shows that the addition of weak RV labels as a guidance during training improves the FAZ segmentation significantly with respect to using only manual FAZ annotations.

Hofer Dominik, Schmidt-Erfurth Ursula, Orlando José Ignacio, Goldbach Felix, Gerendas Bianca S, Seeböck Philipp

2022-May-01

General General

Forecasting molecular dynamics energetics of polymers in solution from supervised machine learning.

In Chemical science

Machine learning techniques including neural networks are popular tools for chemical, physical and materials applications searching for viable alternative methods in the analysis of structure and energetics of systems ranging from crystals to biomolecules. Efforts are less abundant for prediction of kinetics and dynamics. Here we explore the ability of three well established recurrent neural network architectures for reproducing and forecasting the energetics of a liquid solution of ethyl acetate containing a macromolecular polymer-lipid aggregate at ambient conditions. Data models from three recurrent neural networks, ERNN, LSTM and GRU, are trained and tested on half million points time series of the macromolecular aggregate potential energy and its interaction energy with the solvent obtained from molecular dynamics simulations. Our exhaustive analyses convey that the recurrent neural network architectures investigated generate data models that reproduce excellently the time series although their capability of yielding short or long term energetics forecasts with expected statistical distributions of the time points is limited. We propose an in silico protocol by extracting time patterns of the original series and utilizing these patterns to create an ensemble of artificial network models trained on an ensemble of time series seeded by the additional time patters. The energetics forecast improve, predicting a band of forecasted time series with a spread of values consistent with the molecular dynamics energy fluctuations span. Although the distribution of points from the band of energy forecasts is not optimal, the proposed in silico protocol provides useful estimates of the solvated macromolecular aggregate fate. Given the growing application of artificial networks in materials design, the data-based protocol presented here expands the realm of science areas where supervised machine learning serves as a decision making tool aiding the simulation practitioner to assess when long simulations are worth to be continued.

Andrews James, Gkountouna Olga, Blaisten-Barojas Estela

2022-Jun-15

General General

Effect of Bodybuilding and Fitness Exercise on Physical Fitness Based on Deep Learning.

In Emergency medicine international

With the rapid development of society and economy, people's living standards are improving day by day, and increasingly attention is paid to physical health, which has set off a fitness upsurge. The purpose of this paper was to analyze the impact of bodybuilding exercise on physical fitness based on deep learning. It provides a reference for fitness enthusiasts to choose scientific and targeted exercise methods, and provides a theoretical basis for the promotion of bodybuilding and fitness. This paper first gives a general introduction to deep learning and adds image segmentation technology to design experiments for bodybuilding and fitness. The experiment was divided into groups A and B, and control group C. In this paper, recurrent neural network and gated recurrent neural network are introduced to compare and analyze the data, and the stability of data processing with different activation functions is compared. The data results show that under the scientific and reasonable arrangement of exercise conditions, bodybuilding and fitness exercises have a corresponding positive effect on the body shape and posture of the subjects. It is more practical to choose a combination of aerobic and anaerobic exercise. In this paper, based on the deep learning algorithm, compared with the recurrent neural network, the gated recurrent neural network is more suitable for processing sequence problems. In the experimental analysis part, this paper compares and analyzes the experimental results of the data under different activation functions, sigmoid function, and tanh function. It is found that the tanh activation function and the gated recurrent neural network are more stable for data processing. The highest AUC value of the traditional recurrent neural network differs by 0.78 from the highest AUC value of the gated recurrent neural network. The data analysis results are in line with the actual situation.

Sun Manman, Wang Lijun

2022

General General

Sirtuin Family and Diabetic Kidney Disease.

In Frontiers in endocrinology ; h5-index 55.0

Diabetes mellitus (DM) is gradually attacking the health and life of people all over the world. Diabetic kidney disease (DKD) is one of the most common chronic microvascular complications of DM, whose mechanism is complex and still lacks research. Sirtuin family is a class III histone deacetylase with highly conserved NAD+ binding domain and catalytic functional domain, while different N-terminal and C-terminal structures enable them to bind different deacetylated substrates to participate in the cellular NAD+ metabolism. The kidney is an organ rich in NAD+ and database exploration of literature shows that the Sirtuin family has different expression localization in renal, cellular, and subcellular structures. With the progress of modern technology, a variety of animal models and reagents for the Sirtuin family and DKD emerged. Machine learning in the literature shows that the Sirtuin family can regulate pathophysiological injury mainly in the glomerular filtration membrane, renal tubular absorption, and immune inflammation through various mechanisms such as epigenetics, multiple signaling pathways, and mitochondrial function. These mechanisms are the key nodes participating in DKD. Thus, it is of great significance for target therapy to study biological functions of the Sirtuin family and DKD regulation mechanism in-depth.

Bian Che, Ren Huiwen

2022

NAD+, Sirtuin (SIRT), diabetes, diabetic kidney disease, kidney

General General

Acupuncture Regulates Symptoms of Parkinson's Disease via Brain Neural Activity and Functional Connectivity in Mice.

In Frontiers in aging neuroscience ; h5-index 64.0

Parkinson's disease (PD) is a multilayered progressive brain disease characterized by motor dysfunction and a variety of other symptoms. Although acupuncture has been used to ameliorate various symptoms of neurodegenerative disorders, including PD, the underlying mechanisms are unclear. Here, we investigated the mechanism of acupuncture by revealing the effects of acupuncture treatment on brain neural responses and its functional connectivity in an animal model of PD. We observed that destruction of neuronal network between many brain regions in PD mice were reversed by acupuncture. Using machine learning analysis, we found that the key region associated with the improvement of abnormal behaviors might be related to the neural activity of M1, suggesting that the changes of c-Fos in M1 could predict the improvement of motor function induced by acupuncture treatment. In addition, acupuncture treatment was shown to significantly normalize the brain neural activity not only in M1 but also in other brain regions related to motor behavior (striatum, substantia nigra pars compacta, and globus pallidus) and non-motor symptoms (hippocampus, lateral hypothalamus, and solitary tract) of PD. Taken together, our results demonstrate that acupuncture treatment might improve the PD symptoms by normalizing the brain functional connectivity in PD mice model and provide new insights that enhance our current understanding of acupuncture mechanisms for non-motor symptoms.

Oh Ju-Young, Lee Ye-Seul, Hwang Tae-Yeon, Cho Seong-Jin, Jang Jae-Hwan, Ryu Yeonhee, Park Hi-Joon

2022

Parkinson’s disease, acupuncture, functional connectivity, machine learning, primary motor cortex

General General

Automated Classification of Mild Cognitive Impairment by Machine Learning With Hippocampus-Related White Matter Network.

In Frontiers in aging neuroscience ; h5-index 64.0

Background : Detection of mild cognitive impairment (MCI) is essential to screen high risk of Alzheimer's disease (AD). However, subtle changes during MCI make it challenging to classify in machine learning. The previous pathological analysis pointed out that the hippocampus is the critical hub for the white matter (WM) network of MCI. Damage to the white matter pathways around the hippocampus is the main cause of memory decline in MCI. Therefore, it is vital to biologically extract features from the WM network driven by hippocampus-related regions to improve classification performance.

Methods : Our study proposes a method for feature extraction of the whole-brain WM network. First, 42 MCI and 54 normal control (NC) subjects were recruited using diffusion tensor imaging (DTI), resting-state functional magnetic resonance imaging (rs-fMRI), and T1-weighted (T1w) imaging. Second, mean diffusivity (MD) and fractional anisotropy (FA) were calculated from DTI, and the whole-brain WM networks were obtained. Third, regions of interest (ROIs) with significant functional connectivity to the hippocampus were selected for feature extraction, and the hippocampus (HIP)-related WM networks were obtained. Furthermore, the rank sum test with Bonferroni correction was used to retain significantly different connectivity between MCI and NC, and significant HIP-related WM networks were obtained. Finally, the classification performances of these three WM networks were compared to select the optimal feature and classifier.

Results : (1) For the features, the whole-brain WM network, HIP-related WM network, and significant HIP-related WM network are significantly improved in turn. Also, the accuracy of MD networks as features is better than FA. (2) For the classification algorithm, the support vector machine (SVM) classifier with radial basis function, taking the significant HIP-related WM network in MD as a feature, has the optimal classification performance (accuracy = 89.4%, AUC = 0.954). (3) For the pathologic mechanism, the hippocampus and thalamus are crucial hubs of the WM network for MCI.

Conclusion : Feature extraction from the WM network driven by hippocampus-related regions provides an effective method for the early diagnosis of AD.

Zhou Yu, Si Xiaopeng, Chao Yi-Ping, Chen Yuanyuan, Lin Ching-Po, Li Sicheng, Zhang Xingjian, Sun Yulin, Ming Dong, Li Qiang

2022

Alzheimer’s disease, early diagnosis, feature extraction, machine learning, mild cognitive impairment, white matter connectivity

General General

Deep Learning-Based Prediction of the 3D Postorthodontic Facial Changes.

In Journal of dental research ; h5-index 65.0

With the increase of the adult orthodontic population, there is a need for an accurate and evidence-based prediction of the posttreatment face in 3 dimensions (3D). The objectives of this study are 1) to develop a 3D postorthodontic face prediction method based on a deep learning network using the patient-specific factors and orthodontic treatment conditions and 2) to validate the accuracy and clinical usability of the proposed method. Paired sets (n = 268) of pretreatment (T1) and posttreatment (T2) cone-beam computed tomography (CBCT) of adult patients were trained with a conditional generative adversarial network to generate 3D posttreatment facial data based on the patient's gender, age, and the changes of upper (ΔU1) and lower incisor position (ΔL1) as input. The accuracy was calculated with prediction error and mean absolute distances between real T2 (T2) and predicted T2 (PT2) near 6 perioral landmark regions, as well as percentage of prediction error less than 2 mm using test sets (n = 44). For qualitative evaluation, an online survey was conducted with experienced orthodontists as panels (n = 56). Overall, PT2 indicated similar 3D changes to the T2 face, with the most apparent changes simulated in the perioral regions. The mean prediction error was 1.2 ± 1.01 mm with 80.8% accuracy. More than 50% of the experienced orthodontists were unable to distinguish between real and predicted images. In this study, we proposed a valid 3D postorthodontic face prediction method by applying a deep learning algorithm trained with CBCT data sets.

Park Y S, Choi J H, Kim Y, Choi S H, Lee J H, Kim K H, Chung C J

2022-Jun-30

3-dimensional, conditional GAN, deep learning, orthodontics, outcome simulation, soft tissue prediction

General General

Machine learning models to detect social distress, spiritual pain, and severe physical psychological symptoms in terminally ill patients with cancer from unstructured text data in electronic medical records.

In Palliative medicine ; h5-index 47.0

BACKGROUND : Few studies have developed automatic systems for identifying social distress, spiritual pain, and severe physical and phycological symptoms from text data in electronic medical records.

AIM : To develop models to detect social distress, spiritual pain, and severe physical and psychological symptoms in terminally ill patients with cancer from unstructured text data contained in electronic medical records.

DESIGN : A retrospective study of 1,554,736 narrative clinical records was analyzed 1 month before patients died. Supervised machine learning models were trained to detect comprehensive symptoms, and the performance of the models was tested using the area under the receiver operating characteristic curve (AUROC) and precision recall curve (AUPRC).

SETTING/PARTICIPANTS : A total of 808 patients was included in the study using records obtained from a university hospital in Japan between January 1, 2018 and December 31, 2019. As training data, we used medical records labeled for detecting social distress (n = 10,000) and spiritual pain (n = 10,000), and records that could be combined with the Support Team Assessment Schedule (based on date) for detecting severe physical/psychological symptoms (n = 5409).

RESULTS : Machine learning models for detecting social distress had AUROC and AUPRC values of 0.98 and 0.61, respectively; values for spiritual pain, were 0.90 and 0.58, respectively. The machine learning models accurately identified severe symptoms (pain, dyspnea, nausea, insomnia, and anxiety) with a high level of discrimination (AUROC > 0.8).

CONCLUSION : The machine learning models could detect social distress, spiritual pain, and severe symptoms in terminally ill patients with cancer from text data contained in electronic medical records.

Masukawa Kento, Aoyama Maho, Yokota Shinichiroh, Nakamura Jyunya, Ishida Ryoka, Nakayama Masaharu, Miyashita Mitsunori

2022-Jun-30

Symptom assessment, electronic medical records, machine learning, psychosocial support systems, spirituality, terminal care

General General

Serious Gaming and Artificial Intelligence in Rehabilitation of Rheumatoid Arthritis.

In Studies in health technology and informatics ; h5-index 23.0

This paper presents a current situation of studies and applications which are using serious games and artificial intelligence (AI) in rehabilitation of rheumatoid arthritis, and possible future directions. The objectives of this paper are: to highlight the technologies used for recovery of patients with rheumatoid arthritis (RA), to summarize the state of the art of existing applications and to present the authors work, a software application that aims contributing to the recovery of the specific patients. At this stage the application was tested by a group of 10 patients from Medical Centre Sf. Mary of Timisoara. Most of the patients reported that the physical and psychical effort were between easy-very easy. The patients confirmed that they would use the application in their rehabilitation process and are very excited about this type of rehabilitation that stimulates curiosity and a state of wellbeing. The application works based on the leap motion device that proved to be the most suitable device in terms of precision and the manner of interaction in virtual reality games.

Varga Gabriela, Stoicu-Tivadar Lăcrămioara, Nicola Stelian

2022-Jun-29

artificial intelligence, multimodal interaction, rheumatoid arthritis, serious games

Surgery Surgery

A Machine Learning Solution to Predict Elective Orthopedic Surgery Case Duration.

In Studies in health technology and informatics ; h5-index 23.0

We used surgery durations, patient demographic and personnel data taken from the East Kent Hospitals University NHS Foundation Trust (EKHUFT) over a period of 10 years (2010-2019) for a total of 25,352 patients that underwent 15 highest volume elective orthopedic surgeries, to predict future surgery durations for the subset of elective surgeries under consideration. As part of this study, we compared two different ensemble machine learning methods random forest regression (RF) and XGBoost (eXtreme Gradient Boosting) regression. The two models were approximately 5% superior to the existing model used by the hospital scheduling system.

Sahadev Divya, Lovegrove Thomas, Kunz Holger

2022-Jun-29

Machine learning, Predictive Modelling, Surgery Case Duration

General General

The Role of Artificial Intelligence and Machine Learning During the Covid-19 Pandemic: A Review.

In Studies in health technology and informatics ; h5-index 23.0

Covid-19 is one of the most significant infectious diseases that have faced humanity in the past century from clinical, economic, and social perspectives. Although the role of infectious diseases in human history has been vicious and is well known to humanity, Covid-19 is a special case since it is the first worldwide outbreak in the era of advanced computing and telecommunications. For this reason, it was only logical to see Artificial Intelligence (AI) and Machine Learning (ML) on the top of the list of controls to compact the spread of Covid-19. This paper goes through the applications of AI and ML that were reported in some of the major literature indexes and can be related to the main issues that face healthcare providers during the Covid-19 pandemic. This paper also discusses the applicability of these applications to healthcare organizations and points out the main prerequisites before they can be adopted.

Aldhoayan Mohammed D

2022-Jun-29

Artificial Intelligence, Covid-19, Healthcare, Machine Learning

General General

Federated Learning and Internet of Medical Things - Opportunities and Challenges.

In Studies in health technology and informatics ; h5-index 23.0

The recent advancements in artificial intelligence (AI) and the Internet of Medical Things (IoMT) have opened new horizons for healthcare technology. AI models, however, rely on large data that must be shared with the centralized entity developing the model. Data sharing leads to privacy preservation and legal issues. Federated Learning (FL) enables the training of AI models on distributed data. Hence, a large amount of IoMT data can be put into use without the need for sharing the data. This paper presents the opportunities offered by FL for privacy preservation in IoMT data. With FL, the complicated dynamics and agreements for data-sharing can be avoided. Furthermore, it describes the use cases of FL in facilitating collaborative efforts to develop AI for COVID-19 diagnosis. Since handling data from multiple sites poses its challenges, the paper also highlights the critical challenges associated with FL developments for IoMT data. Addressing these challenges will lead to gaining maximum benefit from data-driven AI technologies in IoMT.

Ali Hazrat, Alam Tanvir, Househ Mowafa, Shah Zubair

2022-Jun-29

Artificial Intelligence, COVID-19, Federated Learning, Healthcare, Internet of Medical Things, Privacy

General General

Predicting the Length of Stay in Neurosurgery with RuGPT-3 Language Model.

In Studies in health technology and informatics ; h5-index 23.0

In this study, we update the evaluation of the Russian GPT3 model presented in our previous paper in predicting the length of stay (LOS) in neurosurgery. We aimed to assess the performance the Russian GPT-3 (ruGPT-3) language model in LOS prediction using narrative medical records in neurosurgery compared to doctors' and patients' expectations. Doctors appeared to have the most realistic LOS expectations (MAE = 2.54), while the model's predictions (MAE = 3.53) were closest to the patients' (MAE = 3.47) but inferior to them (p = 0.011). A detailed analysis showed a solid quality of ruGPT-3 performance based on narrative clinical texts. Considering our previous findings obtained with recurrent neural networks and FastText vector representation, we estimate the new result as important but probably improveable.

Danilov Gleb, Kotik Konstantin, Shevchenko Elena, Usachev Dmitriy, Shifrin Michael, Strunina Yulia, Tsukanova Tatyana, Ishankulov Timur, Lukshin Vasiliy, Potapov Alexander

2022-Jun-29

Length of stay, deep learning, natural language processing, neural networks, neurosurgery, prediction

General General

Artificial Intelligence: On the Way to Doctor's Trust.

In Studies in health technology and informatics ; h5-index 23.0

The submission is devoted to reflections on the role of trust to modern IT systems, especially based on the AI technologies. Its purpose is to draw the attention of the medical informatics community to the need to achieve trust at all stages of the life cycle of MDSS and other information systems.

Shifrin Michael, Khavtorin Anton, Danilov Gleb

2022-Jun-29

Artificial intelligence, blockchain, medical decision support systems, trust

General General

Customized and Automated Machine Learning-Based Models for Diabetes Type 2 Classification.

In Studies in health technology and informatics ; h5-index 23.0

This study aims to develop models to accurately classify patients with type 2 diabetes using the Practice Fusion dataset. We use Random Forest (RF), Support Vector Classifier (SVC), AdaBoost classifier, an ensemble model, and automated machine learning (AutoML) model. We compare the performance of all models in a five-fold cross-validation scheme using four evaluation measures. Experimental results demonstrate that the AutoML model outperformed individual and ensemble models in all evaluation measures.

Mohsen Farida, Biswas Md Rafiul, Ali Hazrat, Alam Tanvir, Househ Mowafa, Shah Zubair

2022-Jun-29

AutoML, Diabetes classification, Ensemble model, Machine learning

General General

Application of Machine Learning Techniques to Examine Social Service Needs Among Hispanic Family Caregivers of Persons with Dementia.

In Studies in health technology and informatics ; h5-index 23.0

We applied machine learning algorithms to examine the relationship between demographics and outcomes of the social work services used by Hispanic family caregivers of persons with dementia recruited for a clinical trial in New York City. The social work service needs were largely concentrated on instrumental support to gain access to the healthcare system rather than other concrete services (e.g., housing or food programs) or to address psychological needs among the caregivers with relatively higher income. A finding from the machine learning approach was that among those who receive medical-related social work services, frequent users (≥10 times) with high family friend support(>4) were more likely than frequent users without such support to have their issues resolved (Accuracy: 81.9%, AUC: 0.82, F-measure: 0.86 by J48). Even though half of the participants received social work services multiple times, the needs of the caregivers remained unmet unless they sought social work services frequently (more than ten times).

Yoon Sunmoo, Mendes Alexandra, Burgio Louis, Mittelman Mary, Dunner Ilana, Levine Jed A, Hoyos Carolina, Tipiani Dante, Ramirez Mildred, Teresi Jeanne A, Luchsinger José A

2022-Jun-29

aging, dementia caregiving, disparities, machine learning, social work

General General

Exploratory Clustering for Emergency Department Patients.

In Studies in health technology and informatics ; h5-index 23.0

Emergency department (ED) overcrowding is an increasing global problem raising safety concerns for the patients. Elaborating an effective triage system that properly separates patients requiring hospital admission remains difficult. The objective of this study was to compare a clustering-related technique assignment of emergency department patients with the admission output using the k-means algorithm. Incorporating such a model into triage practice could theoretically shorten waiting times and reduce ED overcrowding.

Feretzakis Georgios, Sakagianni Aikaterini, Kalles Dimitris, Loupelis Evangelos, Tzelves Lazaros, Panteris Vasileios, Chatzikyriakou Rea, Trakas Nikolaos, Kolokytha Stavroula, Batiani Polyxeni, Rakopoulou Zoi, Tika Aikaterini, Petropoulou Stavroula, Dalainas Ilias, Kaldis Vasileios

2022-Jun-29

Machine learning, clustering, emergency department, hospital admission, k-means, unsupervised learning

General General

Deep Time Growing Neural Network vs Convolutional Neural Network for Intelligent Phonocardiography.

In Studies in health technology and informatics ; h5-index 23.0

This paper explores the capabilities of a sophisticated deep learning method, named Deep Time Growing Neural Network (DTGNN), and compares its possibilities against a generally well-known method, Convolutional Neural network (CNN). The comparison is performed by using time series of the heart sound signal, so-called Phonocardiography (PCG). The classification objective is to discriminate between healthy and patients with cardiac diseases by applying a deep machine learning method to PCGs. This approach which is called intelligent phonocardiography has received interest from the researchers toward the development of a smart stethoscope for decentralized diagnosis of heart disease. It is found that DTGNN associates further flexibility to the approach which enables the classifier to learn subtle contents of PCG, and meanwhile better copes with the complexities intrinsically that exist in the medical applications such as the imbalance training. The structural risk of the two methods is compared using the A-Test method.

Gharehbaghi Arash, Babic Ankica

2022-Jun-29

A-Test method, Deep time growing neural network, deep learning, heart sounds, intelligent phonocardiography

General General

Cluster Analysis Assessment in Proposing a Surgical Technique for Benign Prostatic Enlargement.

In Studies in health technology and informatics ; h5-index 23.0

Benign prostatic enlargement (BPE) is a common disease in men over 50 years old. The phenotype of patients with BPE is heterogenous, regarding both baseline patient characteristics and disease-related parameters. Treatment can be either medical-conservative or surgical. A great variety of surgical techniques are available for surgical management, with three of the most common being monopolar transurethral resection of the prostate (mTUR-P), bipolar transurethral resection of the prostate (bTUR-P), and bipolar transurethral vaporization of the prostate (bTUVis). The selection of each one of these depends on surgeon reasoning, equipment availability, patient characteristics, and preferences. Since all of these techniques are available in our Urology Department, and surgeons are skilled to perform each one of them, we performed a clustering analysis according to patient pre-operative characteristics, using the k-means algorithm, to compare clustering-related technique assignment with the real-life technique used.

Tzelves Lazaros, Feretzakis Georgios, Kalles Dimitris, Manolitsis Ioannis, Katsimperis Stamatis, Bellos Themistoklis, Berdempes Marinos, Anastasiou Athanasios, Koutsouris Dimitrios, Kofopoulou Stavroula, Verykios Vassilios S, Skolarikos Andreas, Varkarakis Ioannis

2022-Jun-29

Benign prostatic enlargement, artificial intelligence, clustering analysis, transurethral prostate resection

General General

Using Association Rules in Antimicrobial Resistance in Stone Disease Patients.

In Studies in health technology and informatics ; h5-index 23.0

Association rule mining is a very popular unsupervised machine learning technique for discovering patterns in large datasets. Patients with stone disease commonly suffer from urinary tract infections (UTI), complicated by the emergence of antimicrobial resistance (AMR), due to the excessive use of antibiotics. In this study, we explore the use of association rule mining in the AMR profile of patients suffering from stone disease.

Manolitsis Ioannis, Feretzakis Georgios, Tzelves Lazaros, Kalles Dimitris, Loupelis Evangelos, Katsimperis Stamatis, Kosmidis Thanos, Anastasiou Athanasios, Koutsouris Dimitrios, Kofopoulou Stavroula, Verykios Vassilios S, Skolarikos Andreas, Varkarakis Ioannis

2022-Jun-29

AMR, Antimicrobial Resistance, Association rules, unsupervised ML

General General

A Formal Model for the FAIR4Health Information Architecture.

In Studies in health technology and informatics ; h5-index 23.0

In the EU project FAIR4Health, a ETL pipeline for the FAIRification of structured health data as well as an agent-based, distributed query platform for the analysis of research hypotheses and the training of machine learning models were developed. The system has been successfully tested in two clinical use cases with patient data from five university hospitals. Currently, the solution is also being considered for use in other hospitals. However, configuring the system and deploying it in the local IT architecture is non-trivial and meets with understandable concerns about security. This paper presents a model for describing the information architecture based on a formal approach, the 3LGM metamodel. The model was evaluated by the developers. As a result, the clear separation of tasks and the software components that implement them as well as the rich description of interactions via interfaces were positively emphasized.

Perbix Mona, Löbe Matthias, Stäubert Sebastian, Sinaci A Anil, Gencturk Mert, Quintero Miriam, Martinez-Garcia Alicia, Alvarez-Romero Celia, Parra-Calderon Carlos L, Winter Alfred

2022-Jun-29

Health Information Systems, Organizational Models, Systems Analysis

General General

Discovering Association Rules in Antimicrobial Resistance in Intensive Care Unit.

In Studies in health technology and informatics ; h5-index 23.0

Multidrug resistant infections in intensive care units represent a worldwide problem with adverse health effects and negative economic implications. As artificial intelligence techniques are increasingly applied in diagnosing, treating, and preventing antimicrobial resistance, in this study, we explore the use of association rule mining in the antibiotic resistance profile of critically ill patients suffering from multidrug resistant infections.

Sakagianni Aikaterini, Feretzakis Georgios, Kalles Dimitris, Loupelis Evangelos, Rakopoulou Zoi, Dalainas Ilias, Fildisis Georgios

2022-Jun-29

Antimicrobial resistance, Association rules, Machine Learning

General General

Artificial Intelligence Solutions to Detect Fraud in Healthcare Settings: A Scoping Review.

In Studies in health technology and informatics ; h5-index 23.0

Over the past decade, Artificial Intelligence (AI) technologies have quickly become implemented in protecting data, including detecting fraud in healthcare organizations. This scoping review aims to explore AI solutions utilized in fraud detection occurring in treatment settings. To find relevant literature, PubMed and Google Scholar were searched. Out of 183 retrieved studies, 31 met all inclusion criteria. This review found that AI has been used to detect different types of fraud such as identify theft and kickbacks in healthcare. Additionally, this review discusses how AI techniques used in network mapping fraud can detect and visualize the hacker's network. A proper system must be implemented in healthcare settings for successful fraud detection, which may overall improve the healthcare system.

Iqbal Mohammad Sharique, Abd-Alrazaq Alaa, Househ Mowafa

2022-Jun-29

Fraud, artificial intelligence, deep learning, healthcare settings, machine learning

Public Health Public Health

The Impact of COVID-19 on Mental Health Services in Scotland, UK.

In Studies in health technology and informatics ; h5-index 23.0

There is a global emergency in relation to mental health (MH) and healthcare. In the UK each year, 1 in 4 people will experience MH problems. Healthcare services are increasingly oversubscribed, and COVID-19 has deepened the healthcare gap. We investigated the effect of COVID-19 on waiting times for MH services in Scotland. We used national registers of MH services provided by Public Health Scotland. The results show that waiting times for adults and children increased drastically during the pandemic. This was seen nationally and across most of the administrative regions of Scotland. We find, however, that child and adolescent services were comparatively less impacted by the pandemic than adult services. This is potentially due to prioritisation of paediatric patients, or due to an increasing demand on adult services triggered by the pandemic itself.

Cooke Elizabeth A, Lemanska Agnieszka, Livings Jennifer, Thomas Spencer A

2022-Jun-29

COVID-19, Deep Learning, Mental Health, Visualization

General General

Conception, Development and Validation of Classification Methods for Coding Support of Rare Diseases Using Artificial Intelligence.

In Studies in health technology and informatics ; h5-index 23.0

Automated coding of diseases can support hospitals in the billing of inpatient cases with the health insurance funds. This paper describes the implementation and evaluation of classification methods for two selected Rare Diseases. Different classifiers of an off-the-shelf system and an own application are applied in a supervised learning process and comparatively examined for their suitability and reliability. Using Natural Language Processing and Machine Learning, disease entities are recognized from unstructured historical patient records and new billing cases are coded automatically. The results of the performed classifications show that even with small datasets (≤ 200), high correctness (F1 score ∼0.8) can be achieved in predicting new cases.

Noll Richard, Minor Mirjam, Berger Alexandra, Naab Lukas, Bay Matthias, Storf Holger, Schaaf Jannik

2022-Jun-29

Artificial Intelligence, Classification, Coding Support, Rare Diseases

General General

Multinomial Classification of Neurosurgical Operations Using Gradient Boosting and Deep Learning Algorithms.

In Studies in health technology and informatics ; h5-index 23.0

This study aimed at testing the feasibility of neurosurgical procedures classification into 100+ classes using natural language processing and machine learning. A catboost algorithm and bidirectional recurrent neural network with a gated recurrent unit showed almost the same accuracy of ∼81%, with suggestions of correct class in top 2-3 scored classes up to 98.9%. The classification of neurosurgical procedures via machine learning appears to be a technically solvable task which can be additionally improved considering data enhancement and classes verification.

Danilov Gleb, Kotik Konstantin, Shifrin Michael, Strunina Yulia, Pronkina Tatiana, Tsukanova Tatiana, Nepomnyashiy Vladimir, Konovalov Nikolay, Potapov Alexander

2022-Jun-29

Neurosurgery, artificial intelligence, classification, deep learning, machine learning, neurosurgical procedures

Dermatology Dermatology

Early Diabetes Prediction: A Comparative Study Using Machine Learning Techniques.

In Studies in health technology and informatics ; h5-index 23.0

Most screening tests for Diabetes Mellitus (DM) in use today were developed using electronically collected data from Electronic Health Record (EHR). However, developing and under-developing countries are still struggling to build EHR in their hospitals. Due to the lack of HER data, early screening tools are not available for those countries. This study develops a prediction model for early DM by direct questionnaires for a tertiary hospital in Bangladesh. Information gain technique was used to reduce irreverent features. Using selected variables, we developed logistic regression, support vector machine, K-nearest neighbor, Naïve Bayes, random forest (RF), and neural network models to predict diabetes at an early stage. RF outperformed other machine learning algorithms achieved 100% accuracy. These findings suggest that a combination of simple questionnaires and a machine learning algorithm can be a powerful tool to identify undiagnosed DM patients.

Poly Tahmina Nasrin, Islam Md Mohaimenul, Li Yu-Chuan Jack

2022-Jun-29

Diabetes, early-stage prediction, machine learning, random forest

General General

Using Machine Learning for Predicting the Hospitalization of Emergency Department Patients.

In Studies in health technology and informatics ; h5-index 23.0

Artificial intelligence processes are increasingly being used in emergency medicine, notably for supporting clinical decisions and potentially improving healthcare services. This study investigated demographics, coagulation tests, and biochemical markers routinely used for patients seen in the Emergency Department (ED) concerning hospitalization. This retrospective observational study included 13,991 emergency department visits of patients who had undergone biomarker testing to a tertiary public hospital in Greece during 2020. After applying five well-known classifiers of the caret package for machine learning of the R programming language in the whole data set and to each ED unit separately, the best performance regarding AUC ROC was observed in the Pulmonology ED unit. Furthermore, among the five classification techniques evaluated, a random forest classifier outperformed other models.

Feretzakis Georgios, Sakagianni Aikaterini, Kalles Dimitris, Loupelis Evangelos, Panteris Vasileios, Tzelves Lazaros, Chatzikyriakou Rea, Trakas Nikolaos, Kolokytha Stavroula, Batiani Polyxeni, Rakopoulou Zoi, Tika Aikaterini, Petropoulou Stavroula, Dalainas Ilias, Kaldis Vasileios

2022-Jun-29

Artificial intelligence, R programming language, emergency department, machine learning, predict hospitalization

General General

Treatment Prediction in the ICU Setting Using a Partitioned, Sequential Deep Time Series Analysis.

In Studies in health technology and informatics ; h5-index 23.0

We developed a neural network architecture to evaluate the patient's state using temporal data, patient's demographics and comorbidities. We examined the model's ability to predict both a binary medication-treatment decision and its specific dose in three common scenarios: hypokalemia, hypoglycemia and hypotension. We partition the common 12-hours horizon window into three sub-windows, examining how patterns of treatment evolve following a key clinical event or state. This partitioned analysis also helps in alleviating the problem of small data sets, by utilizing previous sub-windows' data as additional training data. We also propose a solution to the problem of the relative inability of dose-prediction models to output a "no treatment" classification, through the use of sequential prediction.

Shapiro Michael, Shahar Yuval

2022-Jun-29

Decision support, ICU, Temporal deep learning

General General

Hospital Readmission Prediction via Keyword Extraction and Sentiment Analysis on Clinical Notes.

In Studies in health technology and informatics ; h5-index 23.0

Unplanned hospital readmission is a problem that affects hospitals worldwide and is due to different factors. The identification of those factors can help determine which patients are at greater risk of hospital readmission for early intervention. Our end goal is to predict and identify patterns to (i) feed a decision support system for efficient management of patients and resources and (ii) detect patients at high risk of 30-days readmission enabling preventive actions to improve management of hospital discharges. This study aims to analyze whether natural language processing and specifically keyword extractions tools and sentiment analysis can support 30-days readmission prediction. Features extracted from medical history notes and discharge reports were used to train a Logistic Regression model. The resulting model obtains an AUC of 0.63 indicating that the sentiment polarity score of the discharge report and several of the extracted keywords are representative features to consider.

Zubillaga Aitor, Laccourreye Paula, Kerexeta Jon, Larburu Nekane, Alonso Eduardo, Gómez D Jesús, Martínez Francisco, Alonso-Arce Maykel

2022-Jun-29

Hospital readmissions, machine learning, natural language processing, sentiment analysis, unstructured data

General General

Using Machine Learning to Identify No-Show Telemedicine Encounters in a New York City Hospital.

In Studies in health technology and informatics ; h5-index 23.0

No-show visits are a serious problem for healthcare centers. It costs a major hospital over 15 million dollars annually. The goal of this paper was to build machine learning models to identify potential no-show telemedicine visits and to identify significant factors that affect no-show visits. 257,293 telemedicine sessions and 152,164 unique patients were identified in Mount Sinai Health System between March 2020 and December 2020. 5,124 (2%) of these sessions were no-show encounters. Extreme Gradient Boosting (XGB) with under-sampling was the best machine learning model to identify no-show visits using telemedicine service. The accuracy was 0.74, with an AUC score of 0.68. Patients with previous no-show encounters, non-White or non-Asian patients, and patients living in Bronx and Manhattan were all important factors for no-show encounters. Furthermore, providers' specialties in psychiatry and nutrition, and social workers were more susceptible to higher patient no-show rates.

Cui Wanting, Finkelstein Joseph

2022-Jun-29

No-show visits, supervised machine learning, telemedicine

General General

Methods Used to Compare Narrative Clinical Practice Guidelines: A Scoping Review.

In Studies in health technology and informatics ; h5-index 23.0

Guideline-based clinical decision support systems (CDSSs) need the most recent evidence for reliable performance, making the provision of regularly updated clinical practice guidelines (CPGs) a major issue. Some international guidelines are renewed in short intervals and can be used for checking the status of given national guidelines with regard to the most recent evidence. Considering the volume of medical data and the number of CPGs published, computerized comparison of clinical guidelines can be an effective method. We performed a scoping review to evaluate the methods used for comparing two CPGs. We searched for methods for extracting CPG components and for methods used for comparing CPGs at different levels of abstraction. In each case, computerized and semi-computerized methods were recognized. Expert knowledge has yet a determinant role for assessing the comparisons, this role being more prominent for the extraction of semantic rules and the resolution of inconsistencies.

Azarpira Mohammadreza, Redjdal Akram, Bouaud Jacques, Seroussi Brigitte

2022-Jun-29

Clinical practice guidelines, machine learning, natural language processing, ontology

Dermatology Dermatology

Automatic Wound Type Classification with Convolutional Neural Networks.

In Studies in health technology and informatics ; h5-index 23.0

Chronic wounds are ulcerations of the skin that fail to heal because of an underlying condition such as diabetes mellitus or venous insufficiency. The timely identification of this condition is crucial for healing. However, this identification requires expert knowledge unavailable in some care situations. Here, artificial intelligence technology may support clinicians. In this study, we explore the performance of a deep convolutional neural network to classify diabetic foot and venous leg ulcers using wound images. We trained a convolutional neural network on 863 cropped wound images. Using a hold-out test set with 80 images, the model yielded an F1-score of 0.85 on the cropped and 0.70 on the full images. This study shows promising results. However, the model must be extended in terms of wound images and wound types for application in clinical practice.

Malihi Leila, Hüsers Jens, Richter Mats L, Moelleken Maurice, Przysucha Mareike, Busch Dorothee, Heggemann Jan, Hafer Guido, Wiemeyer Stefan, Heidemann Gunther, Dissemond Joachim, Erfurt-Berge Cornelia, Hübner Ursula

2022-Jun-29

Clinical Decision Support System, Convolutional Neural Networks, Diabetic Foot Ulcer, Health Information Technology, Image Classification, Transfer Learning, Wound Care

General General

Integrating Human Patterns of Qualitative Coding with Machine Learning: A Pilot Study Involving Technology-Induced Error Incident Reports.

In Studies in health technology and informatics ; h5-index 23.0

The objective of this research was to develop a reproducible method of integrating human patterns of qualitative coding with machine learning. The application of qualitative codes from the technology-induced error and safety literatures to the analysis of incident reports was done successfully, helping to identify the factors that lead to an error as well as the errors themselves. The method described in this paper may provide additional insights into understanding technology-induced errors.

Borycki Elizabeth M, Farghali Amr, Kushniruk Andre W

2022-Jun-29

Patient safety, health technology, health technology safety, technology-induced error

General General

Requirement Analysis for an Intelligent Warning System to Alarm the Rapid Response Team Prior to Patient Deterioration.

In Studies in health technology and informatics ; h5-index 23.0

** : The early warning system alarms the rapid response team (RRT) for clinical deterioration monitoring and prediction. Available systems do not perform well to decrease the number of ICU transfers or death. This study aimed to address the requirement of an intelligent warning system for timely and accurate RRT activation.

METHODOLOGY : A literature review was conducted in scientific databases to extract data. Then, a questionnaire was developed for experts' views collection (N=12). The collected data were analyzed using the Content Validity Ratio (CVR). According to the Lawshe table for the corresponding number of experts, the cut-off=0.56 for items to be accepted/rejected was considered. A schematic structure was suggested.

FINDINGS : The analysis of the extracted papers (N=24) and qualitative analysis addressed 44 requirements in the frame of five involved sub-systems, including a patient monitoring system, electronic health record, clinical decision support system, remote monitoring patient, and dashboard &registries. They were confirmed by meeting the least cut-off value (CVR= 0.86).

CONCLUSION : An integrated approach and technologies of IoT, deep and machine learning techniques, big data, advanced databases, and standards to create an intelligent EWS are required.

Rostam Niakan Kalhori Sharareh, Deserno Thomas M

2022-Jun-29

Clinical deterioration, artificial intelligence, monitoring, warning system

General General

The Role of Artificial Intelligence and Machine Learning During the Covid-19 Pandemic: A Review.

In Studies in health technology and informatics ; h5-index 23.0

Covid-19 is one of the most significant infectious diseases that have faced humanity in the past century from clinical, economic, and social perspectives. Although the role of infectious diseases in human history has been vicious and is well known to humanity, Covid-19 is a special case since it is the first worldwide outbreak in the era of advanced computing and telecommunications. For this reason, it was only logical to see Artificial Intelligence (AI) and Machine Learning (ML) on the top of the list of controls to compact the spread of Covid-19. This paper goes through the applications of AI and ML that were reported in some of the major literature indexes and can be related to the main issues that face healthcare providers during the Covid-19 pandemic. This paper also discusses the applicability of these applications to healthcare organizations and points out the main prerequisites before they can be adopted.

Aldhoayan Mohammed D

2022-Jun-29

Artificial Intelligence, Covid-19, Healthcare, Machine Learning

Public Health Public Health

Inference Time of a CamemBERT Deep Learning Model for Sentiment Analysis of COVID Vaccines on Twitter.

In Studies in health technology and informatics ; h5-index 23.0

In previous work, we implemented a deep learning model with CamemBERT and PyTorch, and built a microservices architecture using the TorchServe serving library. Without TorchServe, inference time was three times faster when the model was loaded once in memory compared when the model was loaded each time. The preloaded model without TorchServe presented comparable inference time with the TorchServe instance. However, using a PyTorch preloaded model in a web application without TorchServe would necessitate to implement functionalities already present in TorchServe.

Guerdoux Guillaume, Tiffet Théophile, Bousquet Cedric

2022-Jun-29

Artificial Intelligence, COVID-19, MLOps, Social Media, Vaccines

General General

Comparing Emotional Valence Scores of Twitter Posts from Manual Coding and Machine Learning Algorithms to Gain Insights to Refine Interventions for Family Caregivers of Persons with Dementia.

In Studies in health technology and informatics ; h5-index 23.0

We randomly extracted Korean-language Tweets mentioning dementia/Alzheimer's disease (n= 12,413) from November 28 to December 9, 2020. We independently applied three machine learning algorithms (Afinn, Syuzhet, and Bing) using natural language processing (NLP) techniques and qualitative manual scoring to assign emotional valence scores to Tweets. We then compared the means and distributions of the four emotional valence scores. Visual examination of the graphs produced indicated that each method exhibited unique patterns. The aggregated mean emotional valence scores from the NLP methods were mostly neutral, vs. slightly negative for manual coding (Afinn 0.029, 95% CI [-0.019, 0.077]; Syuzhet 0.266, [0.236, 0.295]; Bing -0.271, [-0.289, -0.252]; manual coding -1.601, [-1.632, -1.569]). One-way analysis of variance (ANOVA) showed no statistically significant differences among the four means after normalization. These findings suggest that the application of NLP can be fairly effective in extracting emotional valence scores from Korean-language Twitter content to gain insights regarding family caregiving for a person with dementia.

Yoon Sunmoo, Broadwell Peter, Sun Frederick F, Jang Sun Joo, Lee Haeyoung

2022-Jun-29

Dementia caregiving, emotional valence, natural language processing

General General

Ethical Issues in the Utilization of Black Boxes for Artificial Intelligence in Medicine.

In Studies in health technology and informatics ; h5-index 23.0

Artificial Intelligence (AI) has made major progress in recent years in many fields. With regard of medicine however, the utilization of AI raises numerous ethical questions, especially since newer and much more accurate algorithms function as black boxes. A trade-off must then be made between having algorithms being very accurate and effective, and algorithms that are explainable but less proficient. In this paper we address the ethical implications of utilizing black box algorithms in medicine.

Beltramin Diva, Lamas Eugenia, Bousquet Cédric

2022-Jun-29

Artificial intelligence, Black boxes, Decision making, Ethics, Trust

Public Health Public Health

Comparison of Non-AI and AI-Enabled M-Health Platforms for COVID-19 Self Screening in Indonesia.

In Studies in health technology and informatics ; h5-index 23.0

This study aimed to analyze and differentiate the role of AI and no AI-supported m-health platforms for COVID-19 self-screening in Indonesia. We utilized a mysterious shopping method to develop four standardized cases with various severity levels of COVID-19 tested in Indonesia's most popular mHealth platforms. We selected seven apps from the top 200 free mHealth apps in the "Medical" category in the Google Play Store equipped with COVID-19 symptom checkers. A total of 36 teleconsultations were performed in four chatbot-based, two apps supported with AI combined with a human-based approach, and three apps with the human-based process. Teleconsultations were recorded, classified, and analyzed compared with the COVID-19 guideline by the MoH of Indonesia. The study indicated that most of the self-screening provided questions that had consistently led to the COVID-19 condition such as cough, fever, and shortness of breath and followed the guideline from the national health authority.

Andriani Sekar Putri, Adhyanacarira Padmanaba, Fuad Anis, Pertiwi Ariani Arista Putri

2022-Jun-29

COVID-19, artificial intelligence, mHealth apps, self-screening

General General

Health-Related Content in Transformer-Based Deep Neural Network Language Models: Exploring Cross-Linguistic Syntactic Bias.

In Studies in health technology and informatics ; h5-index 23.0

This paper explores a methodology for bias quantification in transformer-based deep neural network language models for Chinese, English, and French. When queried with health-related mythbusters on COVID-19, we observe a bias that is not of a semantic/encyclopaedical knowledge nature, but rather a syntactic one, as predicted by theoretical insights of structural complexity. Our results highlight the need for the creation of health-communication corpora as training sets for deep learning.

Samo Giuseppe, Bonan Caterina, Si Fuzhen

2022-Jun-29

COVID-19, Corpora, Knowledge Reproduction, Language Models, Natural Language Processing

General General

Artificial Intelligence-Based Models for Predicting Vaccines Critical Tweets: An Experimental Study.

In Studies in health technology and informatics ; h5-index 23.0

We studied the suitability of Artificial Intelligence (AI)-based models to predict vaccine-critical tweets on the social media platform Twitter. We manually labeled a sample of 800 tweets as either "vaccine-critical" (i.e, anti-vaccine tweets, mentioned concerns related to vaccine safety and efficacy, and are against vaccine mandates or vaccine passports) or "other" (i.e., tweets that are neutral, report news, or are ambiguous) and used them to train and test AI-based models for automatically predicting vaccine-critical tweets. We fine-tuned two pre-trained deep learning-based language models, BERT and BERTweet, and implemented four classical AI-based models, Random Forest, Logistics Regression, Linear Support Vector Machines, and Multinomial Naïve Bayes. We evaluated these AI-based models using f1 score, accuracy, precision, and recall in three-fold cross-validation. We found that BERTweet outperformed all other models using these measures.

Shah Uzair, Ali Hazrat, Alam Tanvir, Househ Mowafa, Shah Zubair

2022-Jun-29

deep learning, machine learning, tweets, vaccines

General General

Federated Learning and Internet of Medical Things - Opportunities and Challenges.

In Studies in health technology and informatics ; h5-index 23.0

The recent advancements in artificial intelligence (AI) and the Internet of Medical Things (IoMT) have opened new horizons for healthcare technology. AI models, however, rely on large data that must be shared with the centralized entity developing the model. Data sharing leads to privacy preservation and legal issues. Federated Learning (FL) enables the training of AI models on distributed data. Hence, a large amount of IoMT data can be put into use without the need for sharing the data. This paper presents the opportunities offered by FL for privacy preservation in IoMT data. With FL, the complicated dynamics and agreements for data-sharing can be avoided. Furthermore, it describes the use cases of FL in facilitating collaborative efforts to develop AI for COVID-19 diagnosis. Since handling data from multiple sites poses its challenges, the paper also highlights the critical challenges associated with FL developments for IoMT data. Addressing these challenges will lead to gaining maximum benefit from data-driven AI technologies in IoMT.

Ali Hazrat, Alam Tanvir, Househ Mowafa, Shah Zubair

2022-Jun-29

Artificial Intelligence, COVID-19, Federated Learning, Healthcare, Internet of Medical Things, Privacy

General General

Comparison of Data Classification Results for Leap Motion Recovery Gestures.

In Studies in health technology and informatics ; h5-index 23.0

Static and dynamic gestures are frequently used in activities supporting learning, recovery healthcare, engineering, and 3D games to increase the interactivity between man and machine. The gestures are detected via hardware devices and data is processed using different software methods. This paper presents the manner of detection and interpretation of two gestures, a hand rotation gesture and a palm closing and opening gesture, using the Leap Motion device. These two dynamic gestures are very often used in hand recovery exercises. For comparing the two gestures we use data classification methods, Support Vector Machine (SVM) and Multilayer Perceptron (MLP). The data for the gesture classification were 80% training data and 20% testing data. The metrics for comparison are precision, recall, F1-score, and the total number of testing cases (support). The SVM classifier gives an accuracy of 99.4% and the MLP classifier a 96.2%. We built two confusion matrices for better visualizing the results.

Nicola Stelian, Chirila Oana-Sorina, Stoicu-Tivadar Lacramioara

2022-Jun-29

Leap Motion, Linear-SVM, MLP, Machine learning, Multimodal interaction, Recovery

Radiology Radiology

The ongoing saga of the evolution of percutaneous coronary interventions: From balloon angioplasty to recent innovations to future prospects.

In The Canadian journal of cardiology

The advances in percutaneous coronary interventions (PCI) have been, above all, dependent on the work of pioneers in surgery, radiology and interventional cardiology. From Grüntzig's first balloon angioplasty, PCI has expanded through technology development, improved protocols, and dissemination of best-practice techniques. We can nowadays treat more complex lesions in higher-risk patients with favourable results. Guidewires, balloon types and profiles, debulking techniques such as atherectomy or lithotripsy, stents and scaffolds all represent evolutions that have allowed us to tackle complex lesions such as an unprotected left main coronary artery, complex bifurcations, or chronic total occlusions. Best-practice PCI, including physiology assessment, imaging, and optimal lesion preparation are now the gold standard when performing PCI for sound indications, and new technologies such as intravascular lithotripsy for lesion preparation, or artificial intelligence, are innovations in the steps of 4 decades of pioneers to improve patient care in interventional cardiology. The present review describes major innovations in PCI since the first balloon angioplasty and also uncertainties and obstacles inherent to such medical advances.

Picard Fabien, Pighi Michele, Marquis-Gravel Guillaume, Labinaz Marino, Cohen Eric A, Tanguay Jean-François

2022-Jun-28

Radiology Radiology

Deep learning-based prediction model using x-ray in nontuberculous mycobacterial pulmonary disease.

In Chest ; h5-index 81.0

BACKGROUND : Prognostic prediction of nontuberculous mycobacteria pulmonary disease using a deep learning technique has not been attempted.

RESEARCH QUESTION : Can a deep learning model using chest radiograph predict the prognosis of nontuberculous mycobacteria pulmonary disease?

STUDY DESIGN AND METHODS : Patients diagnosed with nontuberculous mycobacteria pulmonary disease at Seoul National University Hospital (train/validation dataset) between January 2000 and December 2015 and at Seoul Metropolitan Government-Boramae Medical Center (test dataset) between January 2006 and December 2015 were included. We trained deep learning models to predict the 3-, 5-, and 10-year overall mortality using baseline chest radiographs at diagnosis. We tested the predictability for the corresponding mortality using only deep learning-driven radiographic scores and using both radiographic scores and clinical information (age, sex, body mass index, and mycobacterial species).

RESULTS : The datasets comprised 1,638 (train/validation set) and 566 (test set) chest radiographs from 1,034 and 200 patients, respectively. The deep learning-driven radiographic score provided areas under the receiver operating characteristic curve of 0.844, 0.781, and 0.792 for the 10-, 5-, and 3-year mortality, respectively. The logistic regression model using both the radiographic score and clinical information provided areas under the receiver operating characteristic curves of 0.922, 0.942, and 0.865 for the 10-, 5, and 3-year mortality, respectively.

INTERPRETATION : The deep learning model we developed could predict the mid- to-long-term mortality of patients with nontuberculous mycobacteria pulmonary disease using a baseline radiograph at diagnosis, and the predictability increased with clinical information.

Lee Seowoo, Lee Hyun Woo, Kim Hyung-Jun, Kim Deog Kyeom, Yim Jae-Joon, Yoon Soon Ho, Kwak Nakwon

2022-Jun-28

Artificial Intelligence, Mortality, Mycobacterium Infections, Nontuberculous, Predictive Value of Tests, Prognosis

Radiology Radiology

Data Federation in Healthcare for Artificial Intelligence Solutions.

In Studies in health technology and informatics ; h5-index 23.0

Data federation offers a way to get data moving from multiple sources providing advantages in healthcare systems where medical data is often hard to reach because of regulations or the lack of reliable solutions that can integrate on top of protocols like FHIR, HL7, DICOM, among others. Given the increasing need for solutions that augment healthcare systems with artificial intelligence (AI), in fields like genomics, cancer treatment, and radiology, all of which will require solutions that can provide data at scale while being traceable, safe, and regulatory-compliant. This paper proposes an architectural solution that may provide the core capabilities to implement a data federation approach in a healthcare system to enable AI.

Castellanos Julio, Raposo Gonzalo, Antunez Lucia

2022-Jun-29

Artificial Intelligence, Data Federation, Healthcare Systems

General General

A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19.

In Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie

Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.

Ahmed Faheem, Soomro Afaque Manzoor, Chethikkattuveli Salih Abdul Rahim, Samantasinghar Anupama, Asif Arun, Kang In Suk, Choi Kyung Hyun

2022-Jun-28

AI on networks, Deep learning, Drug repurposing, Machine learning, Network analysis, Network diffusion, Network proximity

General General

Transient DUX4 expression in human embryonic stem cells induces blastomere-like expression program that is marked by SLC34A2.

In Stem cell reports

Embryonic genome activation (EGA) is critical for embryonic development. However, our understanding of the regulatory mechanisms of human EGA is still incomplete. Human embryonic stem cells (hESCs) are an established model for studying developmental processes, but they resemble epiblast and are sub-optimal for modeling EGA. DUX4 regulates human EGA by inducing cleavage-stage-specific genes, while it also induces cell death. We report here that a short-pulsed expression of DUX4 in primed hESCs activates an EGA-like gene expression program in up to 17% of the cells, retaining cell viability. These DUX4-induced cells resembled eight-cell stage blastomeres and were named induced blastomere-like (iBM) cells. The iBM cells showed marked reduction of POU5F1 protein, as previously observed in mouse two-cell-like cells. Finally, the iBM cells were successfully enriched using an antibody against NaPi2b (SLC34A2), which is expressed in human blastomeres. The iBM cells provide an improved model system to study human EGA transcriptome.

Yoshihara Masahito, Kirjanov Ida, Nykänen Sonja, Sokka Joonas, Weltner Jere, Lundin Karolina, Gawriyski Lisa, Jouhilahti Eeva-Mari, Varjosalo Markku, Tervaniemi Mari H, Otonkoski Timo, Trokovic Ras, Katayama Shintaro, Vuoristo Sanna, Kere Juha

2022-Jun-16

DUX4, NaPi2b, SLC34A2, blastomere, embryonic genome activation, embryonic stem cells, human embryo, reprogramming

General General

A common epigenetic clock from childhood to old age.

In Forensic science international. Genetics

Forensic age estimation is a DNA intelligence tool that forms an important part of Forensic DNA Phenotyping. Criminal cases with no suspects or with unsuccessful matches in searches on DNA databases; human identification analyses in mass disasters; anthropological studies or legal disputes; all benefit from age estimation to gain investigative leads. Several age prediction models have been developed to date based on DNA methylation. Although different DNA methylation technologies as well as diverse statistical methods have been proposed, most of them are based on blood samples and mainly restricted to adult age ranges. In the current study, we present an extended age prediction model based on 895 evenly distributed Spanish DNA blood samples from 2 to 104 years old. DNA methylation levels were detected using Agena Bioscience EpiTYPER® technology for a total of seven CpG sites located at seven genomic regions: ELOVL2, ASPA, PDE4C, FHL2, CCDC102B, MIR29B2CHG and chr16:85395429 (GRCh38). The accuracy of the age prediction system was tested by comparing three statistical methods: quantile regression (QR), quantile regression neural network (QRNN) and quantile regression support vector machine (QRSVM). The most accurate predictions were obtained when using QRNN or QRSVM (mean absolute prediction error, MAE of ± 3.36 and ± 3.41, respectively). Validation of the models with an independent Spanish testing set (N = 152) provided similar accuracies for both methods (MAE: ± 3.32 and ± 3.45, respectively). The main advantage of using quantile regression statistical tools lies in obtaining age-dependent prediction intervals, fitting the error to the estimated age. An additional analysis of dimensionality reduction shows a direct correlation of increased error and a reduction of correct classifications as the training sample size is reduced. Results indicated that a minimum sample size of six samples per year-of-age covered by the training set is recommended to efficiently capture the most inter-individual variability..

Freire-Aradas A, Girón-Santamaría L, Mosquera-Miguel A, Ambroa-Conde A, Phillips C, Casares de Cal M, Gómez-Tato A, Álvarez-Dios J, Pospiech E, Aliferi A, Syndercombe Court D, Branicki W, Lareu M V

2022-Jun-25

DNA methylation, EpiTYPER®, Forensic age estimation, Machine learning, Quantile regression

General General

A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19.

In Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie

Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.

Ahmed Faheem, Soomro Afaque Manzoor, Chethikkattuveli Salih Abdul Rahim, Samantasinghar Anupama, Asif Arun, Kang In Suk, Choi Kyung Hyun

2022-Jun-28

AI on networks, Deep learning, Drug repurposing, Machine learning, Network analysis, Network diffusion, Network proximity

General General

An end-to-end framework for intima media measurement and atherosclerotic plaque detection in the carotid artery.

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVES : The detection and delineation of atherosclerotic plaque are usually manually performed by medical experts on the carotid artery. Evidence suggests that this manual process is subject to errors and has a large variability between experts, equipment, and datasets. This paper proposes a robust end-to-end framework for automatic atherosclerotic plaque detection.

METHODS : The proposed framework is composed of: (1) a semantic segmentation model based on U-Net, with EfficientNet as the backbone, that obtains a segmentation mask with the carotid intima-media region; and (2) a convolutional neural network designed using Bayesian optimization that simultaneously performs a regression to get the average and maximum carotid intima media thickness, and a classification to determine the presence of plaque.

RESULTS : Our approach improves the state-of-the-art in both co and bulb territories in the REGICOR database, with more than 8000 images, while providing predictions in real-time. The correlation coefficient was 0.89 in the common carotid artery and 0.74 for bulb region, and the F1 score for atherosclerotic plaque detecting was 0.60 and 0.59, respectively. The experimentation carried out includes a comparison with other fully automatic methods for carotid intima media thickness estimation found in the literature. Additionally, we present an extensive experimental study to evaluate the robustness of our proposal, as well as its suitability and efficiency compared to different versions of the framework.

CONCLUSIONS : The proposed end-to-end framework significantly improves the automatic characterization of atherosclerotic plaque. The generation of the segmented mask can be helpful for practitioners since it allows them to evaluate and interpret the model's results by visual inspection. Furthermore, the proposed framework overcomes the limitations of previous research based on ad-hoc post-processing, which could lead to overestimations in the case of oblique forms of the carotid artery.

Gago Lucas, Vila Maria Del Mar, Grau Maria, Remeseiro Beatriz, Igual Laura

2022-Jun-15

Atherosclerotic plaque, CIMT estimation, Deep learning, Semantic segmentation

General General

A contrastive consistency semi-supervised left atrium segmentation model.

In Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

Accurate segmentation for the left atrium (LA) is a key process of clinical diagnosis and therapy for atrial fibrillation. In clinical, the semantic-level segmentation of LA consumes much time and labor. Although supervised deep learning methods can somewhat solve this problem, a high-efficient deep learning model requires abundant labeled data that is hard to acquire. Therefore, the research on automatic LA segmentation of leveraging unlabeled data is highly required. In this paper, we propose a semi-supervised LA segmentation framework including a segmentation model and a classification model. The segmentation model takes volumes from both labeled and unlabeled data as input and generates predictions of LAs. And then, a classification model maps these predictions to class-vectors for each input. Afterward, to leverage the class information, we construct a contrastive consistency loss function based on these class-vectors, so that the model can enlarge the discrepancy of the inter-class and compact the similarity of the intra-class for learning more distinguishable representation. Moreover, we set the class-vectors from the labeled data as references to the class-vectors from the unlabeled data to relieve the influence of the unreliable prediction for the unlabeled data. At last, we evaluate our semi-supervised LA segmentation framework on a public LA dataset using four universal metrics and compare it with recent state-of-the-art models. The proposed model achieves the best performance on all metrics with a Dice Score of 89.81 %, Jaccard of 81.64 %, 95 % Hausdorff distance of 7.15 mm, and Average Surface Distance of 1.82 mm. The outstanding performance of the proposed framework shows that it may have a significant contribution to assisting the therapy of patients with atrial fibrillation. Code is available at: https://github.com/PerceptionComputingLab/SCC.

Liu Yashu, Wang Wei, Luo Gongning, Wang Kuanquan, Li Shuo

2022-Jun-16

Contrastive learning, Left atrium segmentation, Semi-supervised learning

General General

From YouTube to the brain: Transfer learning can improve brain-imaging predictions with deep learning.

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

Deep learning has recently achieved best-in-class performance in several fields, including biomedical domains such as X-ray images. Yet, data scarcity poses a strict limit on training successful deep learning systems in many, if not most, biomedical applications, including those involving brain images. In this study, we translate state-of-the-art transfer learning techniques for single-subject prediction of simpler (sex and age) and more complex phenotypes (number of people in household, household income, fluid intelligence and smoking behavior). We fine-tuned 2D and 3D ResNet-18 convolutional neural networks for target phenotype predictions from brain images of ∼40,000 UK Biobank participants, after pretraining on YouTube videos from the Kinetics dataset and natural images from the ImageNet dataset. Transfer learning was effective on several phenotypes, especially sex and age classification. Additionally, transfer learning in particular outperformed deep learning models trained from scratch especially on smaller sample sizes. The out-of-sample performance using transfer learning from previously learned knowledge based on real-world images and videos could unlock the potential in many areas of imaging neuroscience where deep learning solutions are currently infeasible.

Malik Nahiyan, Bzdok Danilo

2022-Jun-18

Brain imaging, Convolutional neural network algorithms, Deep learning, Imaging neuroscience, Machine learning, Transfer learning

General General

Image-driven hydrological parameter coupled identification of flood plain wetland conservation and restoration sites.

In Journal of environmental management

A good many works focus on wetland vulnerability; some works also explore restoration sites at a very limited spatial extent. But the satellite image-driven hydrological data-based approach adopted in this work is absolutely new. Moreover, existing work only focused on identifying restoration sites in the present context, but for devising long-term sustainable planning, predicted hydrological parameters based on possible restoration sites may be an effective tool. Considering this, the present work focused on exploring hydrological data (water presence frequency (WPF), hydro-period (HP) and water depth (WD)) from time-series satellite images. This exploration may resolve the hydrological data scarcity of wetland over the wider geographical areas. Using these parameters, we developed wetland restoration and conservation sites for different historical years (2008, 2018) and predicted years (2028) using ensemble machine learning (EML) models. From the analysis, it was found that water depth, hydro-period and WPF became poorer over the period, and the trend may seem to continue in predicted years. Among the applied EML models, Random Subspace (RS) predicted wetland restoration and conservation sites precisely over others. The predicted area under high-priority restoration sites is 34% in 2018, which was 14% in 2008. In 2028, 12% more areas may fall in this priority level. Wetland away from main streams (mainly ortho-fluvial wetland) and fringe wetland parts should be given more priority for restoration. These present and predicted information will effectively help to frame sustainable wetland restoration planning.

Pal Swades, Singha Pankaj

2022-Jun-28

Ensemble machine learning and predicted restoration sites, Hydrological parameters, Spectral indices, Wetland restoration

General General

Machine learning predictions of chlorophyll-a in the Han river basin, Korea.

In Journal of environmental management

This study developed a model to predict concentrations of chlorophyll-a ([Chl-a]) as a proxy for algal population with data from multiple monitoring stations in the Han river basin, by using machine-learning predictive models, then analyzed the relationship between [Chl-a] and the input variables of the optimized model. Daily water quality and meteorological data from 2012 to 2020 were collected from the real-time water quality information system and the meteorological administration of Korea. To quantify model accuracy, the coefficient of determination, root mean square error, and mean absolute error were applied. Among random forest (RF), support vector machine, and artificial neural network, the RF with random dataset showed the highest accuracy. The RF was optimized when 78 trees were applied to the model. Input variables for the best RF model were total organic carbon (feature importance: 27%), total nitrogen (19%), pH (13%), water temperature (8%), total phosphorus (8%), electrical conductivity (7%), dissolved oxygen (6%), minimum air temperature (AT) (4%), mean AT (3%), and maximum AT (3%). The feature-importance analysis showed that total organic carbon was the most important variable to predict [Chl-a] in the Han river basin. Total nitrogen was a more important variable than total phosphorus.

Kim Kyung-Min, Ahn Johng-Hwa

2022-Jun-28

Artificial intelligence, Chlorophyll-a, Feature importance, Han river basin, Machine learning, Random forest

General General

Network pharmacology provides a systematic approach to understanding the treatment of ischemic heart diseases with traditional Chinese medicine.

In Phytomedicine : international journal of phytotherapy and phytopharmacology

BACKGROUND : The field of network pharmacology showed significant development. The concept of network pharmacology has many similarities to the philosophy of traditional Chinese medicine (TCM), making it suitable to understand the action mechanisms of TCM in treating complex diseases, such as ischemic heart diseases (IHDs).

PURPOSE : This review summarizes the representative applications of network pharmacology in deciphering the mechanism underlying the treatment of IHDs with TCM.

METHODS : In this report, we used "ischemic heart disease" OR "coronary heart disease" OR "coronary artery disease" OR "myocardial ischemia" AND ("network pharmacology" OR "systematic pharmacology") as keywords to search for publications from PubMed, the Web of Science, and Google Scholar databases and then analyzed the representative research reports that summarized and validated the active components and targets network of TCM in improving IHDs to show the advantages and deficiencies of network pharmacology applied in TCM research.

RESULTS : The network pharmacology research indicated that HGF, PGF, MMP3, INSR, PI3K, MAPK1, SRC, VEGF, VEGFR-1, NO, eNOS, NO3, IL-6, TNF-α, and more are the main targets of TCM. Apigenin, 25S-macrostemonoside P, ginsenosides Re, Rb3, Rg3, SheXiang XinTongNing, colchicine, dried ginger-aconite decoction, Suxiao Xintong dropping pills, Ginseng-Danshen drug pair and Shenlian and more are the active ingredients, extracts, and formulations of TCM to ameliorate IHDs. These active compounds, extract, and formulations of TCM treat IHDs by delaying ventricular remodeling, reducing myocardial fibrosis, decreasing reactive oxygen species, regulating myocardial energy metabolism, ameliorating inflammation, mitigating apoptosis, and many other aspects.

CONCLUSIONS : The network pharmacology supplies a novel research exemplification for understanding the treatment of IHDs with TCM. However, the application of network pharmacology in TCM studies is still at a superficial level. By rational combining artificial intelligence technology and network pharmacology, molecular biology, metabolomics, and other advanced theories and technologies, and systematically studying the metabolic process and the network among products, targets, and pathways of TCM from the clinical perspective may be a potential development trend in network pharmacology.

Yang Hua-Yi, Liu Men-Lan, Luo Pei, Yao Xin-Sheng, Zhou Hua

2022-Jun-12

Database, Ischemic heart diseases, Myocardial ischemia, Network pharmacology, Traditional Chinese medicine

Radiology Radiology

Association between computerized tomography (CT) study of body composition and severity of acute pancreatitis: Use of a novel Z-score supports obesity paradox.

In Clinical nutrition (Edinburgh, Scotland)

BACKGROUND & AIMS : The association between body composition parameters measured on computed tomography (CT) and severity of acute pancreatitis (AP) is conflicting because these composition parameters vary considerably by sex and age. We previously developed normative body composition data, in healthy subjects. Z-score calculated from the normative data gives age and sex adjusted body composition parameters. We studied the above association using this novel Z-score in a large cohort of patients with AP.

METHODS : Between January 2014 and March 2018, patients admitted with AP and had CT scans within a week of admission, were enrolled. Body composition data including skeletal muscle (SM), subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) were calculated from the CT scan using deep learning automated algorithm. Then we converted the value to Z-score, and then compared the same score between mild AP, moderately severe AP and severe AP defined by revised Atlanta criteria.

RESULTS : Out of 514 patients, 336 (65.4%) are mild AP, 130 (25.3%) moderately severe AP, and 48 (9.3%) severe AP. Patients with moderately severe AP had significantly lower SM-z-score than those with mild AP (1.21 vs1.73, p = 0.048) and patients with severe AP had significantly lower SAT-z-score than those with mild AP (0.70 vs.1.29, p = 0.016). VAT-z-score was not significantly different between three groups. (p = 0.76).

CONCLUSION : Lower SM-z-score and SAT-z-score were associated with moderately severe and severe types of AP, respectively. Future prospective studies in patients with AP using Z-scores, may define the association between body composition and severity of AP, and explain the inconsistencies reported in previous studies.

Horibe Masayasu, Takahashi Naoki, Weston Alexander D, Philbrick Kenneth, Yamamoto Satoshi, Takahashi Hiroaki, Vege Santhi Swaroop

2022-Jun-18

Obesity, Skeletal muscle, Subcutaneous adipose tissue and visceral adipose tissue

General General

Substantive Interpretation of Machine Learning Solutions by the Example of Determining the Activity of the Tuberculosis Process in Individuals with Minimal Tuberculosis Residual Changes.

In Studies in health technology and informatics ; h5-index 23.0

In this article is described an application of various machine learning (ML) methods to obtain decision rules and its interpretation to a problem of recognition of activity of the tuberculosis process. The research data base included 489 patients registered in anti-tuberculosis institutions in Tyumen and Yekaterinburg. The conducted modeling by machine learning methods allowed to highlight 7 most informative features (the presence of calcifications, age, the content of leukocytes, hemoglobin, eosinophils, α2-fraction of globulins, γ-fraction of globulins) together with classification accuracy of 95% for both active and inactive patients. The research result may be interesting for medical specialists, data scientists and to all those interested in problems at the intersection of medicine and machine learning.

Tyulkova Tatyana, Chernavin Pavel, Chernavin Nikolai, Chugaev Yuri, Chernyaev Igor

2022-Jun-29

committee machine, machine learning, tuberculosis

General General

PHILM2Web: A high-throughput database of macromolecular host-pathogen interactions on the Web.

In Database : the journal of biological databases and curation

During infection, the pathogen's entry into the host organism, breaching the host immune defense, spread and multiplication are frequently mediated by multiple interactions between the host and pathogen proteins. Systematic studying of host-pathogen interactions (HPIs) is a challenging task for both experimental and computational approaches and is critically dependent on the previously obtained knowledge about these interactions found in the biomedical literature. While several HPI databases exist that manually filter HPI protein-protein interactions from the generic databases and curated experimental interactomic studies, no comprehensive database on HPIs obtained from the biomedical literature is currently available. Here, we introduce a high-throughput literature-mining platform for extracting HPI data that includes the most comprehensive to date collection of HPIs obtained from the PubMed abstracts. Our HPI data portal, PHILM2Web (Pathogen-Host Interactions by Literature Mining on the Web), integrates an automatically generated database of interactions extracted by PHILM, our high-precision HPI literature-mining algorithm. Currently, the database contains 23 581 generic HPIs between 157 host and 403 pathogen organisms from 11 609 abstracts. The interactions were obtained from processing 608 972 PubMed abstracts, each containing mentions of at least one host and one pathogen organisms. In response to the coronavirus disease 2019 (COVID-19) pandemic, we also utilized PHILM to process 25 796 PubMed abstracts obtained by the same query as the COVID-19 Open Research Dataset. This COVID-19 processing batch resulted in 257 HPIs between 19 host and 31 pathogen organisms from 167 abstracts. The access to the entire HPI dataset is available via a searchable PHILM2Web interface; scientists can also download the entire database in bulk for offline processing. Database URL: http://philm2web.live.

Le Tuan-Dung, Nguyen Phuong D, Korkin Dmitry, Thieu Thanh

2022-Jun-30

Internal Medicine Internal Medicine

A novel stacking ensemble for detecting three types of diabetes mellitus using a Saudi Arabian dataset: Pre-diabetes, T1DM, and T2DM.

In Computers in biology and medicine

Glucose is the primary source of energy for cells, which are the building blocks of life. It is given to the body by insulin that carries out the metabolic tasks that keep people alive. Glucose level imbalance is a sign of diabetes mellitus (DM), a common type of chronic disease. It leads to long-term complications, such as blindness, kidney failure, and heart disease, having a negative impact on one's quality of life. In Saudi Arabia, a ten-fold increase in diabetic cases has been documented within the last three years. DM is broadly categorized as Type 1 Diabetes (T1DM), Type 2 Diabetes (T2DM), and Pre-diabetes. The diagnosis of the correct type is sometimes ambiguous to medical professionals causing difficulties in managing the illness progression. Intensive efforts have been made to predict T2DM. However, there is a lack of studies focusing on accurately identifying T1DM and Pre-diabetes. Therefore, this study aims to utilize Machine Learning (ML) to distinguish and predict the three types of diabetes based on a Saudi Arabian hospital dataset to control their progression. Four different experiments have been conducted to achieve the highest results, where several algorithms were used, including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (K-NN), Decision Tree (DT), Bagging, and Stacking. In experiments 2, 3, and 4, the Synthetic Minority Oversampling Technique (SMOTE) was applied to balance the dataset. The empirical results demonstrated promising results of the novel Stacking model that combined Bagging K-NN, Bagging DT, and K-NN, with a K-NN meta-classifier attaining an accuracy, weighted recall, weighted precision, and cohen's kappa score of 94.48%, 94.48%, 94.70%, and 0.9172, respectively. Five principal features were identified to significantly affect the model accuracy using the permutation feature importance, namely Education, AntiDiab, Insulin, Nutrition, and Sex.

Gollapalli Mohammed, Alansari Aisha, Alkhorasani Heba, Alsubaii Meelaf, Sakloua Rasha, Alzahrani Reem, Al-Hariri Mohammed, Alfares Maiadah, AlKhafaji Dania, Al Argan Reem, Albaker Waleed

2022-Jun-20

Machine learning, Permutation feature importance, Pre-diabetes, Stacking, Type 1 diabetes, Type 2 diabetes

General General

Adaptive Cross Entropy for ultrasmall object detection in Computed Tomography with noisy labels.

In Computers in biology and medicine

Conventional size object detection has been extensively studied, whereas researches concerning ultrasmall object detection are rare due to lack of dataset. Here, considering that the stapes in the ear is the smallest bone in our body, we have collected the largest stapedial otosclerosis detection dataset from 633 stapedial otosclerosis patients and 269 normal cases to promote this direction. Nevertheless, noisy classification labels in our dataset are inevitable due to various subjective and objective factors, and this situation prevails in various fields. In this paper, we propose a novel and general noise tolerant loss function named Adaptive Cross Entropy (ACE) which needs no fine-tuning of hyperparameters for training with noisy labels. We provide both theoretical and empirical analyses for the proposed ACE loss and demonstrate its effectiveness in multiple public datasets. Besides, we find high-resolution representations crucial for ultrasmall object detection and present an auxiliary backbone called W-Net to address it accordingly. Extensive experiments demonstrate that the proposed ACE loss is able to boost the diagnosis performance under noisy label setting by a large margin. Furthermore, our W-Net can help extract sufficient high-resolution representations specialized for ultrasmall objects and achieve even better results. Hopefully, our work could provide more clues for future research on ultrasmall object detection and learning with noisy labels.

Chen Hedan, Tan Weimin, Li Jichun, Guan Pengfei, Wu Lingjie, Yan Bo, Li Jian, Wang Yunfeng

2022-Jun-22

Deep learning, Label noise, Medical image diagnosis, Object detection, Otosclerosis

General General

DTITR: End-to-end drug-target binding affinity prediction with transformers.

In Computers in biology and medicine

The accurate identification of Drug-Target Interactions (DTIs) remains a critical turning point in drug discovery and understanding of the binding process. Despite recent advances in computational solutions to overcome the challenges of in vitro and in vivo experiments, most of the proposed in silico-based methods still focus on binary classification, overlooking the importance of characterizing DTIs with unbiased binding strength values to properly distinguish primary interactions from those with off-targets. Moreover, several of these methods usually simplify the entire interaction mechanism, neglecting the joint contribution of the individual units of each binding component and the interacting substructures involved, and have yet to focus on more explainable and interpretable architectures. In this study, we propose an end-to-end Transformer-based architecture for predicting drug-target binding affinity (DTA) using 1D raw sequential and structural data to represent the proteins and compounds. This architecture exploits self-attention layers to capture the biological and chemical context of the proteins and compounds, respectively, and cross-attention layers to exchange information and capture the pharmacological context of the DTIs. The results show that the proposed architecture is effective in predicting DTA, achieving superior performance in both correctly predicting the value of interaction strength and being able to correctly discriminate the rank order of binding strength compared to state-of-the-art baselines. The combination of multiple Transformer-Encoders was found to result in robust and discriminative aggregate representations of the proteins and compounds for binding affinity prediction, in which the addition of a Cross-Attention Transformer-Encoder was identified as an important block for improving the discriminative power of these representations. Overall, this research study validates the applicability of an end-to-end Transformer-based architecture in the context of drug discovery, capable of self-providing different levels of potential DTI and prediction understanding due to the nature of the attention blocks. The data and source code used in this study are available at: https://github.com/larngroup/DTITR.

Monteiro Nelson R C, Oliveira José L, Arrais Joel P

2022-Jun-21

Attention, Binding affinity, Deep learning, Drug–target interaction, Transformer

General General

CSE-GAN: A 3D conditional generative adversarial network with concurrent squeeze-and-excitation blocks for lung nodule segmentation.

In Computers in biology and medicine

Lung nodule segmentation plays a crucial role in early-stage lung cancer diagnosis, and early detection of lung cancer can improve the survival rate of the patients. The approaches based on convolutional neural networks (CNN) have outperformed the traditional image processing approaches in various computer vision applications, including medical image analysis. Although multiple techniques based on convolutional neural networks have provided state-of-the-art performances for medical image segmentation tasks, these techniques still have some challenges. Two main challenges are data scarcity and class imbalance, which can cause overfitting resulting in poor performance. In this study, we propose an approach based on a 3D conditional generative adversarial network for lung nodule segmentation, which generates better segmentation results by learning the data distribution, leading to better accuracy. The generator in the proposed network is based on the famous U-Net architecture with a concurrent squeeze & excitation module. The discriminator is a simple classification network with a spatial squeeze & channel excitation module, differentiating between ground truth and fake segmentation. To deal with the overfitting, we implement patch-based training. We have evaluated the proposed approach on two datasets, LUNA16 data and a local dataset. We achieved significantly improved performances with dice coefficients of 80.74% and 76.36% and sensitivities of 85.46% and 82.56% for the LUNA test set and local dataset, respectively.

Tyagi Shweta, Talbar Sanjay N

2022-Jun-22

CT scan, Computer-aided diagnosis, Deep learning, Generative adversarial network, Lung cancer, Squeeze & excitation

General General

How physical techniques improve the transdermal permeation of therapeutics: A review.

In Medicine

BACKGROUND : Transdermal delivery is very important in pharmaceutics. However, the barrier function of the stratum corneum hinders drugs absorption. How to improve transdermal delivery efficiency is a hot topic. The key advantages of physical technologies are their wide application for the delivery of previously nonappropriate transdermal drugs, such as proteins, peptides, and hydrophilic drugs. Based on the improved permeation of drugs delivered via multiple physical techniques, many more diseases may be treated, and transdermal vaccinations become possible. However, their wider application depends on the related convenient and portable devices. Combined products comprising medicine and devices represent future commercial directions of artificial intelligence and 3D printing.

METHODS : A comprehensive search about transdermal delivery assisted by physical techniques has been carried out on Web of Science, EMBASE database, PubMed, Wanfang Database, China National Knowledge Infrastructure, and Cochrane Library. The search identified and retrieved the study describing multiple physical technologies to promote transdermal penetration.

RESULTS : Physical technologies, including microneedles, lasers, iontophoresis, sonophoresis, electroporation, magnetophoresis, and microwaves, are summarized and compared. The characteristics, mechanism, advantages and disadvantages of physical techniques are clarified. The individual or combined applicable examples of physical techniques to improve transdermal delivery are summarized.

CONCLUSION : This review will provide more useful guidance for efficient transdermal delivery. More therapeutic agents by transdermal routes become possible with the assistance of various physical techniques.

Gao Yan, Du Lina, Li Qian, Li Qi, Zhu Lin, Yang Meiyan, Wang Xiu, Zhao Bonian, Ma Shan

2022-Jul-01

Public Health Public Health

Perspective: Leveraging the Gut Microbiota to Predict Personalized Responses to Dietary, Prebiotic, and Probiotic Interventions.

In Advances in nutrition (Bethesda, Md.)

Humans often show variable responses to dietary, prebiotic, and probiotic interventions. Emerging evidence indicates that the gut microbiota is a key determinant for this population heterogeneity. Here, we provide an overview of some of the major computational and experimental tools being applied to critical questions of microbiota-mediated personalized nutrition and health. First, we discuss the latest advances in in silico modeling of the microbiota-nutrition-health axis, including the application of statistical, mechanistic, and hybrid artificial intelligence models. Second, we address high-throughput in vitro techniques for assessing inter-individual heterogeneity, from ex vivo batch culturing of stool and continuous culturing in anaerobic bioreactors, to more sophisticated organ-on-a-chip models that integrate both host and microbial compartments. Third, we explore in vivo approaches for better understanding personalized, microbiota-mediated responses to diet, prebiotics, and probiotics, from non-human animal models and human observational studies, to human feeding trials and crossover interventions. We highlight examples of existing, consumer-facing precision nutrition platforms that are currently leveraging the gut microbiota. Furthermore, we discuss how the integration of a broader set of the tools and techniques described in this piece can generate the data necessary to support a greater diversity of precision nutrition strategies. Finally, we present a vision of a precision nutrition and healthcare future, which leverages the gut microbiota to design effective, individual-specific interventions.

Gibbons Sean M, Gurry Thomas, Lampe Johanna W, Chakrabarti Anirikh, Dam Veerle, Everard Amandine, Goas Almudena, Gabriele Gross, Kleerebez Michiel, Lane Jonathan, Maukonen Johanna, Penna Ana Lucia Barretto, Pot Bruno, Valdes Ana M, Walton Gemma, Weiss Adrienne, Zanzer Yoghatama Cindya, Venlet Naomi V, Miani Michela

2022-Jul-01

diet, microbiome, microbiota, personalized healthcare, personalized nutrition, prebiotic, precision healthcare, precision nutrition, probiotic

Public Health Public Health

Standard intensities of transcranial alternating current stimulation over the motor cortex do not entrain corticospinal inputs to motor neurons.

In The Journal of physiology ; h5-index 67.0

TACS is commonly used to entrain the communication between brain regions It is challenging to find direct evidence supporting TACS-driven neural entrainment due to the technical difficulties in recording brain activity during stimulation Computational simulations of motor neuron pools receiving common inputs in the beta (∼21 Hz) band indicate that motor neurons are highly sensitive to corticospinal beta entrainment Motor unit activity from human muscles does not support TACS-driven corticospinal entrainment ABSTRACT: Transcranial alternating current stimulation (TACS) is commonly used to synchronise a cortical area and its outputs to the stimulus waveform, but evidence for this based on brain recordings in humans is challenging. The corticospinal tract transmits beta oscillations (∼21Hz) from motor cortex to tonically contracted limb muscles linearly. Therefore, muscle activity may be used to measure the level of beta entrainment in the corticospinal tract due to TACS over motor cortex. Here, we assessed if TACS is able to modulate the neural inputs to muscles, which would provide indirect evidence for TACS-driven neural entrainment. In the first part of this study, we ran simulations of motor neuron (MN) pools receiving inputs from corticospinal neurons with different levels of beta entrainment. Results suggest that MNs are highly sensitive to changes in corticospinal beta activity. Then, we ran experiments on healthy human subjects (N = 10) in which TACS (at 1mA) was delivered over the motor cortex at 21Hz (beta stimulation), or at 7Hz or 40Hz (control conditions) while the abductor digiti minimi or the tibialis anterior muscle were tonically contracted. Muscle activity was measured using high-density electromyography, which allowed us to decompose the activity of pools of motor units innervating the muscles. By analysing motor unit pool activity, we observed that none of the TACS conditions could consistently alter the spectral contents of the common neural inputs received by the muscles. These results suggest that 1mA-TACS over motor cortex given at beta frequencies does not entrain corticospinal activity. Abstract figure legend TACS over the primary motor cortex may entrain the neural activity in the descending pathways connecting the brain with the spinal cord and muscles. If this is the case, then motor neurones innervating active muscles may reflect such entrainment given their tight connections with corticospinal neurones. We tested this by looking at how the common neural activity in pools of motor neurones projecting to tonically active muscles changed in the presence of TACS at different frequencies. Results from experiments run on humans were combined with simulations using computational models aimed to determine the expected level of sensitivity of motor neuron pools to changes in common inputs. Results indicate that TACS cannot not alter MN activity, which suggests that TACS-driven cortical and corticospinal entrainment may not be easy to achieve at standard intensities used in humans. This article is protected by copyright. All rights reserved.

Ibáñez Jaime, Zicher Blanka, Brown Katlyn E, Rocchi Lorenzo, Casolo Andrea, Del Vecchio Alessandro, Spampinato Danny, Vollette Carole-Anne, Rothwell John C, Baker Stuart N, Farina Dario

2022-Jul-01

electromyography, motor neurons, movement, neuromodulation, transcranial alternating current stimulation

Pathology Pathology

A Deep Learning Method for Breast Cancer Classification in the Pathology Images.

In IEEE journal of biomedical and health informatics

Breast cancer is the most common female cancer in the world and it poses a huge threat to women's health. There is currently promising research con- cerning its early diagnosis using deep learning methodologies. However, some commonly used Convolutional Neural Network (CNN) and their variations, such as AlexNet, VGGNet, Google- Net and so on, are prone to the overfitting in breast cancer classification, due to both small-scale breast pathology image datasets and overconfident softmax-cross-entropy loss. To alleviate the overfitting issue for better classification accuracy, we propose a novel framework for breast pathology classifi- cation, called the AlexNet-BC model. The model is pre-trained using the ImageNet dataset and fine-tuned using an augmented dataset. We also devise an improved cross-entropy loss function to penalize overconfident low-entropy output distribu- tions and make the predictions suitable for uniform distribu- tions. The proposed approach is then validated through a series of comparative experiments on BreaKHis, IDC and UCSB datasets. The experimental results show that the proposed AlexNet-BC outperforms the state-of-the-art methods at different magnifications. Its strong robustness and generali- zation capabilities make it suitable for the histopathology clinical computer-aided diagnosis systems.

Liu Min, Hu Lanlan, Tang Ying, Wang Chu, He Yu, Zeng Chunyan, Lin Kun, He Zhizi, Huo Wujie

2022-Jul-01

General General

Personalized Blood Glucose Prediction for Type 1 Diabetes Using Evidential Deep Learning and Meta-Learning.

In IEEE transactions on bio-medical engineering

The availability of large amounts of data from continuous glucose monitoring (CGM), together with the latest advances in deep learning techniques, have opened the door to a new paradigm of algorithm design for personalized blood glucose (BG) prediction in type 1 diabetes (T1D) with superior performance. However, there are several challenges that prevent the widespread implementation of deep learning algorithms in actual clinical settings, including unclear prediction confidence and limited training data for new T1D subjects. To this end, we propose a novel deep learning framework, Fast-adaptive and Confident Neural Network (FCNN), to meet these clinical challenges. In particular, an attention-based recurrent neural network is used to learn representations from CGM input and forward a weighted sum of hidden states to an evidential output layer, aiming to compute personalized BG predictions with theoretically supported model confidence. The model-agnostic meta-learning is employed to enable fast adaptation for a new T1D subject with limited training data. The proposed framework has been validated on three clinical datasets. In particular, for a dataset including 12 subjects with T1D, FCNN achieved a root mean square error of 18.64±2.60 mg/dL and 31.07±3.62 mg/dL for 30 and 60-minute prediction horizons, respectively, which outperformed all the considered baseline methods with significant improvements. These results indicate that FCNN is a viable and effective approach for predicting BG levels in T1D. The well-trained models can be implemented in smartphone apps to improve glycemic control by enabling proactive actions through real-time glucose alerts.

Zhu Taiyu, Li Kezhi, Herrero Pau, Georgiou Pantelis

2022-Jul-01

General General

DeepEZ: A Graph Convolutional Network for Automated Epileptogenic Zone Localization from Resting-State fMRI Connectivity.

In IEEE transactions on bio-medical engineering

OBJECTIVE : Epileptogenic zone (EZ) localization is a crucial step during diagnostic work up and therapeutic planning in medication refractory epilepsy. In this paper, we present the first deep learning approach to localize the EZ based on resting-state fMRI (rs-fMRI) data.

METHODS : Our network, called DeepEZ, uses a cascade of graph convolutions that emphasize signal propagation along expected anatomical pathways. We also integrate domain-specific information, such as an asymmetry term on the predicted EZ and a learned subject-specific bias to mitigate environmental confounds.

RESULTS : We validate DeepEZ on rs-fMRI collected from 14 patients with focal epilepsy at the University of Wisconsin Madison. Using cross validation, we demonstrate that DeepEZ achieves consistently high EZ localization performance (Accuracy: 0.88 ± 0.03; AUC: 0.73 ± 0.03) that far outstripped any of the baseline methods. This performance is notable given the variability in EZ locations and scanner type across the cohort.

CONCLUSION : Our results highlight the promise of using DeepEZ as an accurate and noninvasive therapeutic planning tool for medication refractory epilepsy.

SIGNIFICANCE : While prior work in EZ localization focused on identifying localized aberrant signatures, there is growing evidence that epileptic seizures affect inter-regional connectivity in the brain. DeepEZ allows clinicians to harness this information from noninvasive imaging that can easily be integrated into the existing clinical workflow.

Nandakumar Naresh, Hsu David, Ahmed Raheel, Venkataraman Archana

2022-Jul-01

General General

Music Emotion Classification Method Based on Deep Learning and Improved Attention Mechanism.

In Computational intelligence and neuroscience

Since the existing music emotion classification researches focus on the single-modal analysis of audio or lyrics, the correlation among models are neglected, which lead to partial information loss. Therefore, a music emotion classification method based on deep learning and improved attention mechanism is proposed. First, the music lyrics features are extracted by Term Frequency-Inverse Document Frequency (TF-IDF) and Word2vec method, and the term frequency weight vector and word vector are obtained. Then, by using the feature extraction ability of Convolutional Neural Network (CNN) and the ability of Long Short-Term Memory (LSTM) network to process the serialized data, and integrating the matching attention mechanism, an emotion analysis model based on CNN-LSTM is constructed. Finally, the output results of the deep neural network and CNN-LSTM model are fused, and the emotion types are obtained by Softmax classifier. The experimental analysis based on the selected data sets shows that the average classification accuracy of the proposed method is 0.848, which is better than the other comparison methods, and the classification efficiency has been greatly improved.

Jia Xiaoguang

2022

General General

Internet Digital Economy Development Forecast Based on Artificial Intelligence and SVM-KNN Network Detection.

In Computational intelligence and neuroscience

The development and spread of Internet technology have made it easier to find web servers. People can browse various websites to shop or pay for living expenses, which brings great convenience to life, but as a result, Internet security problems continue to appear. This article is based on a detailed theoretical analysis of mainstream algorithms, making an analysis of web logs which is of great significance and practical value. In addition, through reasoning analysis, technical support is provided for improving the weight factor of the KNN (K-nearest neighbor) algorithm, and the literature research method of the SVM-KNN hybrid algorithm and the KNN classifier is proposed. This paper conducts a detailed theoretical analysis based on the mainstream algorithms that are widely used in the current classification technology and integrates the mainstream classification algorithms in real-life applications and popularization, selecting the support vector machine and KNN calculation method. In the digital economy development model, although China has a large number of netizens, obvious late-comer advantages and institutional advantages as a guarantee, due to the constraints of two key factors, capital and technology, a series of social problems have also arisen. During the transformation of the digital economy, prominent digital security issues, high-risk vulnerabilities, and increasing number of cyber-attacks, along with uneven data quality levels and lagging laws and regulations, have brought many challenges and obstacles.

Fu Jianru, Zhou Xu, Mei Guoping

2022

General General

iACVP: markedly enhanced identification of anti-coronavirus peptides using a dataset-specific word2vec model.

In Briefings in bioinformatics

The COVID-19 pandemic caused several million deaths worldwide. Development of anti-coronavirus drugs is thus urgent. Unlike conventional non-peptide drugs, antiviral peptide drugs are highly specific, easy to synthesize and modify, and not highly susceptible to drug resistance. To reduce the time and expense involved in screening thousands of peptides and assaying their antiviral activity, computational predictors for identifying anti-coronavirus peptides (ACVPs) are needed. However, few experimentally verified ACVP samples are available, even though a relatively large number of antiviral peptides (AVPs) have been discovered. In this study, we attempted to predict ACVPs using an AVP dataset and a small collection of ACVPs. Using conventional features, a binary profile and a word-embedding word2vec (W2V), we systematically explored five different machine learning methods: Transformer, Convolutional Neural Network, bidirectional Long Short-Term Memory, Random Forest (RF) and Support Vector Machine. Via exhaustive searches, we found that the RF classifier with W2V consistently achieved better performance on different datasets. The two main controlling factors were: (i) the dataset-specific W2V dictionary was generated from the training and independent test datasets instead of the widely used general UniProt proteome and (ii) a systematic search was conducted and determined the optimal k-mer value in W2V, which provides greater discrimination between positive and negative samples. Therefore, our proposed method, named iACVP, consistently provides better prediction performance compared with existing state-of-the-art methods. To assist experimentalists in identifying putative ACVPs, we implemented our model as a web server accessible via the following link: http://kurata35.bio.kyutech.ac.jp/iACVP.

Kurata Hiroyuki, Tsukiyama Sho, Manavalan Balachandran

2022-Jul-01

anti-coronavirus peptide, bioinformatics, deep learning, random forest, transformer, word2vec

General General

Predicting Dynamic Riverine Nitrogen Export in Unmonitored Watersheds: Leveraging Insights of AI from Data-Rich Regions.

In Environmental science & technology ; h5-index 132.0

Terrestrial export of nitrogen is a critical Earth system process, but its global dynamics remain difficult to predict at a high spatiotemporal resolution. Here, we use deep learning (DL) to model daily riverine nitrogen export in response to hydrometeorological and anthropogenic drivers. Long short-term memory (LSTM) models for the daily concentration and flux of dissolved inorganic nitrogen (DIN) were built in a coastal watershed in southeastern China with a typical subtropical monsoon climate. The DL models exhibited excellent accuracy for both DIN concentration and flux, with Nash-Sutcliffe efficiency coefficients (NSEs) up to 0.67 and 0.92, respectively, a performance unlikely to be achieved by generic process-based models with comparable data quality. The flux model ensemble, without retraining, performed well (mean NSE = 0.32-0.84) in seven distinct watersheds in Asia, Europe, and North America, and retraining with multi-watershed data further improved the lowest NSE from 0.32 to 0.68. DL interpretation confirmed that interbasin consistency of riverine nitrogen export exists across different continents, which stems from the similarities in rainfall-runoff relationships. The multi-watershed flux model projects 0.60-12.4% increases in the nitrogen export to oceans from the studied watersheds under a 20% increase in fertilizer consumption, which rises to 6.7-20.1% with a 10% increase in runoff, indicating the synergistic effect of human activities and climate change. The DL-based method represents a successful case of explainable artificial intelligence in environmental science, providing a potential shortcut to a consistent understanding of the global daily-resolution dynamics of riverine nitrogen export under the currently limited data conditions.

Xiong Rui, Zheng Yi, Chen Nengwang, Tian Qing, Liu Wei, Han Feng, Jiang Shijie, Lu Mengqian, Zheng Yan

2022-Jun-30

LSTM, artificial intelligence, deep learning, nitrogen, nonpoint sources, riverine export, transfer learning

General General

Accelerating 3D MTC-BOOST in patients with congenital heart disease using a joint multi-scale variational neural network reconstruction.

In Magnetic resonance imaging

PURPOSE : Free-breathing Magnetization Transfer Contrast Bright blOOd phase SensiTive (MTC-BOOST) is a prototype balanced-Steady-State Free Precession sequence for 3D whole-heart imaging, that employs the endogenous magnetisation transfer contrast mechanism. This achieves reduction of flow and off-resonance artefacts, that often arise with the clinical T2prepared balanced-Steady-State Free Precession sequence, enabling high quality, contrast-agent free imaging of the thoracic cardiovascular anatomy. Fully-sampled MTC-BOOST acquisition requires long scan times (~10-24 min) and therefore acceleration is needed to permit its clinical incorporation. The aim of this study is to enable and clinically validate the 5-fold accelerated MTC-BOOST acquisition with joint Multi-Scale Variational Neural Network (jMS-VNN) reconstruction.

METHODS : Thirty-six patients underwent free-breathing, 3D whole-heart imaging with the MTC-BOOST sequence, which is combined with variable density spiral-like Cartesian sampling and 2D image navigators for translational motion estimation. This sequence acquires two differently weighted bright-blood volumes in an interleaved fashion, which are then joined in a phase sensitive inversion recovery reconstruction to obtain a complementary fully co-registered black-blood volume. Data from eighteen patients were used for training, whereas data from the remaining eighteen patients were used for testing/evaluation. The proposed deep-learning based approach adopts a supervised multi-scale variational neural network for joint reconstruction of the two differently weighted bright-blood volumes acquired with the 5-fold accelerated MTC-BOOST. The two contrast images are stacked as different channels in the network to exploit the shared information. The proposed approach is compared to the fully-sampled MTC-BOOST and 5-fold undersampled MTC-BOOST acquisition with Compressed Sensing (CS) reconstruction in terms of scan/reconstruction time and bright-blood image quality. Comparison against conventional 2-fold undersampled T2-prepared 3D bright-blood whole-heart clinical sequence (T2prep-3DWH) is also included.

RESULTS : Acquisition time was 3.0 ± 1.0 min for the 5-fold accelerated MTC-BOOST versus 9.0 ± 1.1 min for the fully-sampled MTC-BOOST and 11.1 ± 2.6 min for the T2prep-3DWH (p < 0.001 and p < 0.001, respectively). Reconstruction time was significantly lower with the jMS-VNN method compared to CS (10 ± 0.5 min vs 20 ± 2 s, p < 0.001). Image quality was higher for the proposed 5-fold undersampled jMS-VNN versus conventional CS, comparable or higher to the corresponding T2prep-3DWH dataset and similar to the fully-sampled MTC-BOOST.

CONCLUSION : The proposed 5-fold accelerated jMS-VNN MTC-BOOST framework provides efficient 3D whole-heart bright-blood imaging in fast acquisition and reconstruction time with concomitant reduction of flow and off-resonance artefacts, that are frequently encountered with the clinical sequence. Image quality of the cardiac anatomy and thoracic vasculature is comparable or superior to the clinical scan and 5-fold CS reconstruction in faster reconstruction time, promising potential clinical adoption.

Fotaki Anastasia, Fuin Niccolo, Nordio Giovanna, Jimeno Carlos Velasco, Qi Haikun, Emmanuel Yaso, Pushparajah Kuberan, Botnar René M, Prieto Claudia

2022-Jun-27

3D whole-heart imaging, Cardiac MRI, Free-breathing, Neural network

Radiology Radiology

Feasibility of accelerated whole-body diffusion-weighted imaging using a deep learning-based noise-reduction technique in patients with prostate cancer.

In Magnetic resonance imaging

PURPOSE : To assess the possibility of reducing the image acquisition time for diffusion-weighted whole-body imaging with background body signal suppression (DWIBS) by denoising with deep learning-based reconstruction (dDLR).

METHODS : Seventeen patients with prostate cancer who underwent DWIBS by 1.5 T magnetic resonance imaging with a number of excitations of 2 (NEX2) and 8 (NEX8) were prospectively enrolled. The NEX2 image data were processed by dDLR (dDLR-NEX2), and the NEX2, dDLR-NEX2, and NEX8 image data were analyzed. In qualitative analysis, two radiologists rated the perceived coarseness, conspicuity of metastatic lesions (lymph nodes and bone), and overall image quality. The contrast-to-noise ratios (CNRs), contrast ratios, and mean apparent diffusion coefficients (ADCs) of metastatic lesions were calculated in a quantitative analysis.

RESULTS : The image acquisition time of NEX2 was 2.8 times shorter than that of NEX8 (3 min 30 s vs 9 min 48 s). The perceived coarseness and overall image quality scores reported by both readers were significantly higher for dDLR-NEX2 than for NEX2 (P = 0.005-0.040). There was no significant difference between dDLR-NEX2 and NEX8 in the qualitative analysis. The CNR of bone metastasis was significantly greater for dDLR-NEX2 than for NEX2 and NEX8 (P = 0.012 for both comparisons). The contrast ratios and mean ADCs were not significantly different among the three image types.

CONCLUSIONS : dDLR improved the image quality of DWIBS with NEX2. In the context of lymph node and bone metastasis evaluation with DWIBS in patients with prostate cancer, dDLR-NEX2 has potential to be an alternative to NEX8 and reduce the image acquisition time.

Tajima Taku, Akai Hiroyuki, Sugawara Haruto, Furuta Toshihiro, Yasaka Koichiro, Kunimatsu Akira, Yoshioka Naoki, Akahane Masaaki, Abe Osamu, Ohtomo Kuni, Kiryu Shigeru

2022-Jun-27

DWIBS, Deep learning, Denoise, Fast MRI, Prostate cancer, Whole-body MRI

General General

Knowledge Graph-Enabled Text-Based Automatic Personality Prediction.

In Computational intelligence and neuroscience

How people think, feel, and behave primarily is a representation of their personality characteristics. By being conscious of the personality characteristics of individuals whom we are dealing with or deciding to deal with, one can competently ameliorate the relationship, regardless of its type. With the rise of Internet-based communication infrastructures (social networks, forums, etc.), a considerable amount of human communications takes place there. The most prominent tool in such communications is the language in written and spoken form that adroitly encodes all those essential personality characteristics of individuals. Text-based Automatic Personality Prediction (APP) is the automated forecasting of the personality of individuals based on the generated/exchanged text contents. This paper presents a novel knowledge graph-enabled approach to text-based APP that relies on the Big Five personality traits. To this end, given a text, a knowledge graph, which is a set of interlinked descriptions of concepts, was built by matching the input text's concepts with DBpedia knowledge base entries. Then, due to achieving a more powerful representation, the graph was enriched with the DBpedia ontology, NRC Emotion Intensity Lexicon, and MRC psycholinguistic database information. Afterwards, the knowledge graph, which is now a knowledgeable alternative for the input text, was embedded to yield an embedding matrix. Finally, to perform personality predictions, the resulting embedding matrix was fed to four suggested deep learning models independently, which are based on convolutional neural network (CNN), simple recurrent neural network (RNN), long short-term memory (LSTM), and bidirectional long short-term memory (BiLSTM). The results indicated considerable improvements in prediction accuracies in all of the suggested classifiers.

Ramezani Majid, Feizi-Derakhshi Mohammad-Reza, Balafar Mohammad-Ali

2022

General General

Parameter regionalization based on machine learning optimizes the estimation of reference evapotranspiration in data deficient area.

In The Science of the total environment

Reference evapotranspiration (ET0), as one important variable in climatology, hydrology, and agricultural science, plays an important role in the terrestrial hydrological cycle and agricultural irrigation. However, the ET0 estimation process is inaccurate due to the lack of weather stations and historical data. In this study, a new method of ET0 estimation was proposed to improve the ET0 estimation performance in regions with limited data. Four empirical models with different data requirements, Albrecht, Hargreaves-Samani, Priestley-Taylor, and Penman, were applied and optimized the parameters by the Shuffled Complex Evolution-University of Arizona algorithm with the ET0 calculated by the Penman-Monteith model as the reference value at 600 meteorological stations in China. Two machine learning models, Random Forest (RF) and Multiple Linear Regression (MLR) were used to establish the regionalization of the parameter of the empirical model. The result showed that parameter optimization could significantly improve ET0 estimation in different climate regions of China. The Penman model has the strongest physical foundation and the highest estimation accuracy, followed by the Hargeaves-Samani and Priestley-Taylor model. The mass-transfer-based model, Albrecht, could only estimate regional ET0 efficiently after parameter optimization. Based on the more advanced RF machine learning regionalization method that considers complex linear relationships of variables, ET0 estimation in regions lacking data could be improved efficiently. Machine learning could be used to describe the ET0 model parameters in different regions because of the similarity. The combination of machine learning and empirical model could provide a new method for ET0 estimation in data deficient regions.

Shu Zhangkang, Zhou You, Zhang Jianyun, Jin Junliang, Wang Lin, Cui Ningbo, Wang Guoqing, Zhang Jiangjiang, Wu Houfa, Wu Zongjun, Chen Xi

2022-Jun-27

Data deficient regions, Empirical model, Machine learning, Reference evapotranspiration, Regionalization

General General

Cardiac segmentation on CT Images through shape-aware contour attentions.

In Computers in biology and medicine

BACKGROUND AND OBJECTIVE : Cardiac segmentation of atriums, ventricles, and myocardium in computed tomography (CT) images is an important first-line task for presymptomatic cardiovascular disease diagnosis. In several recent studies, deep learning models have shown significant breakthroughs in medical image segmentation tasks. Unlike other organs such as the lungs and liver, the cardiac organ consists of multiple substructures, i.e., ventricles, atriums, aortas, arteries, veins, and myocardium. These cardiac substructures are proximate to each other and have indiscernible boundaries (i.e., homogeneous intensity values), making it difficult for the segmentation network focus on the boundaries between the substructures.

METHODS : In this paper, to improve the segmentation accuracy between proximate organs, we introduce a novel model to exploit shape and boundary-aware features. We primarily propose a shape-aware attention module, that exploits distance regression, which can guide the model to focus on the edges between substructures so that it can outperform the conventional contour-based attention method.

RESULTS : In the experiments, we used the Multi-Modality Whole Heart Segmentation dataset that has 20 CT cardiac images for training and validation, and 40 CT cardiac images for testing. The experimental results show that the proposed network produces more accurate results than state-of-the-art networks by improving the Dice similarity coefficient score by 4.97%.

CONCLUSION : Our proposed shape-aware contour attention mechanism demonstrates that distance transformation and boundary features improve the actual attention map to strengthen the responses in the boundary area. Moreover, our proposed method significantly reduces the false-positive responses of the final output, resulting in accurate segmentation.

Park Sanguk, Chung Minyoung

2022-Jun-21

Cardiac CT segmentation, Contour attention map, Distance transform-based segmentation, Shape-aware contour attention

General General

Interpretable machine learning identification of arginine methylation sites.

In Computers in biology and medicine

Protein methylation is one of the most prominent posttranslation modifications that essentially regulates several biological processes in eukaryotes. Therefore, identification of the arginine methylation site is crucial in deciphering its characteristics and functions in cell biology, disease mechanisms, and guided drug development. The computation methods address the long-term bottleneck together with the cost, time, and labor required in experimental methods for large-scale identification of protein arginine methylation sites. In this study, we proposed a robust machine learning-based computational tool known as iIRMethyl, employing the primary sequence and physicochemical properties of protein along with a two-step feature selection method for optimal selection of feature descriptors. Moreover, the performance of iIRMethyl was comprehensively evaluated via k-fold cross-validation on a benchmark dataset and independent test dataset. iIRMethyl demonstrated a remarkably greater performance than the state-of-the-art method and achieved an average area under the curve value of 0.99 for both k-fold cross-validation and an independent test set in the identification of protein arginine methylation sites. Furthermore, the outcomes reveal that iIRMethyl is a robust and accurate computational tool for large-scale identification of arginine methylation sites and would facilitate the understanding of their functional mechanisms and accelerating their application in drug development and clinical therapy. Additionally, the prediction mechanism of the proposed model iIRMethyl is interpreted using the SHapley Additive exPlanation algorithm.

Ali Syed Danish, Tayara Hilal, Chong Kil To

2022-Jun-21

Arginine methylation, Cross-validation, Machine learning, Two-step feature selection

Radiology Radiology

Fully Automated 3D Segmentation and Diffeomorphic Medial Modeling of the Left Ventricle Mitral Valve Complex in Ischemic Mitral Regurgitation.

In Medical image analysis

There is an urgent unmet need to develop a fully-automated image-based left ventricle mitral valve analysis tool to support surgical decision making for ischemic mitral regurgitation patients. This requires an automated tool for segmentation and modeling of the left ventricle and mitral valve from immediate pre-operative 3D transesophageal echocardiography. Previous works have presented methods for semi-automatically segmenting and modeling the mitral valve, but do not include the left ventricle and do not avoid self-intersection of the mitral valve leaflets during shape modeling. In this study, we develop and validate a fully automated algorithm for segmentation and shape modeling of the left ventricular mitral valve complex from pre-operative 3D transesophageal echocardiography. We performed a 3-fold nested cross validation study on two datasets from separate institutions to evaluate automated segmentations generated by nnU-net with the expert manual segmentation which yielded average overall Dice scores of 0.82±0.03 (set A), 0.87±0.08 (set B) respectively. A deformable medial template was subsequently fitted to the segmentation to generate shape models. Comparison of shape models to the manual and automatically generated segmentations resulted in an average Dice score of 0.93-0.94 and 0.75-0.81 for the left ventricle and mitral valve, respectively. This is a substantial step towards automatically analyzing the left ventricle mitral valve complex in the operating room.

Aly Ahmed H, Khandelwal Pulkit, Aly Abdullah H, Kawashima Takayuki, Mori Kazuki, Saito Yoshiaki, Hung Judy, Gorman Joseph H, Pouch Alison M, Gorman Robert C, Yushkevich Paul A

2022-Jun-12

3D transesophageal echocardiography, Deep learning, Diffeomorphic medial modeling, Ischemic mitral regurgitation

General General

Application of data-mining technique and hydro-chemical data for evaluating vulnerability of groundwater in Indo-Gangetic Plain.

In Journal of environmental management

Vulnerability of groundwater is critical for the sustainable development of groundwater resources, especially in freshwater-limited coastal Indo-Gangetic plains. Here, we intend to develop an integrated novel approach for delineating groundwater vulnerability using hydro-chemical analysis and data-mining methods, i.e., Decision Tree (DT) and K-Nearest Neighbor (KNN) via k-fold cross-validation (CV) technique. A total of 110 of groundwater samples were obtained during the dry and wet seasons to generate an inventory map. Four K-fold CV approach was used to delineate the vulnerable region from sixteen vulnerability causal factors. The statistical error metrics i.e., receiver operating characteristic-area under the curve (AUC-ROC) and other advanced metrices were adopted to validate model outcomes. The results demonstrated the excellent ability of the proposed models to recognize the vulnerability of groundwater zones in the Indo-Gangetic plain. The DT model revealed higher performance (AUC = 0.97) followed by KNN model (AUC = 0.95). The north-central and north-eastern parts are more vulnerable due to high salinity, Nitrate (NO3-), Fluoride (F-) and Arsenic (As) concentrations. Policy-makers and groundwater managers can utilize the proposed integrated novel approach and the outcome of groundwater vulnerability maps to attain sustainable groundwater development and safeguard human-induced activities at the regional level.

Chandra Pal Subodh, Towfiqul Islam Abu Reza Md, Chakrabortty Rabin, Islam Md Saiful, Saha Asish, Shit Manisa

2022-Jun-27

Groundwater development, Indo-gangetic plain, Machine learning, Vulnerability, Water resource

Radiology Radiology

Efficient Axillary Lymph Node Detection Via Two-stage Spatial-information-fusion-based CNN.

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVE : Preoperative imaging diagnosis of axillary lymph node (ALN) metastasis is particularly important for breast cancer patients. This paper focuses on developing non-invasive and automatic schemes for accurate localization and classification (metastasis prediction) of ALN via contrast-enhanced computed tomography (CECT) image and deep learning models.

METHODS : Based on a two-stage strategy, a novel detection neural network is proposed, where the convolutional block attention module is utilized to extract spacial information and the bottleneck feature fusion module is designed for feature fusion in different scales.

RESULTS : Owing to the two embedded modules, the proposed convolutional neural network (CNN) model outperforms Faster R-CNN, YOLOv3, and EfficientDet in the sense that the achieved mAP is 0.454, higher than 0.247, 0.335, and 0.329, respectively. In particular, considering the function of classification only, the proposed model reaches the best performance on most indices (accuracy of 0.968, positive predictive value of 0.972, negative predictive value of 0.966, specificity of 0.983), compared with the methods that have been frequently adopted to predict ALN. In addition, the proposed CNN model has the function of locating ALN, which is lacking in existing models.

CONCLUSIONS : In this paper, a supervised deep learning method is proposed to detect ALN in CECT images. The positive effect of new added modules are verified, and the benefits of spatial information in ALN detection are confirmed. Further, the two subtasks called localization and classification are evaluated separately, where the proposed model achieves the best performance on most indices. The source code mentioned in this article will be released later.

Liu Ziyi, Huang Deqing, Yang Chunmei, Shu Jian, Li Jinhan, Qin Na

2022-Jun-14

Axillary lymph node metastasis, CECT image, Convolutional neural network, Lesion location

Cardiology Cardiology

Explainable deep learning algorithm for distinguishing incomplete Kawasaki disease by coronary artery lesions on echocardiographic imaging.

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVE : Incomplete Kawasaki disease (KD) has often been misdiagnosed due to a lack of the clinical manifestations of classic KD. However, it is associated with a markedly higher prevalence of coronary artery lesions. Identifying coronary artery lesions by echocardiography is important for the timely diagnosis of and favorable outcomes in KD. Moreover, similar to KD, coronavirus disease 2019, currently causing a worldwide pandemic, also manifests with fever; therefore, it is crucial at this moment that KD should be distinguished clearly among the febrile diseases in children. In this study, we aimed to validate a deep learning algorithm for classification of KD and other acute febrile diseases.

METHODS : We obtained coronary artery images by echocardiography of children (n = 138 for KD; n = 65 for pneumonia). We trained six deep learning networks (VGG19, Xception, ResNet50, ResNext50, SE-ResNet50, and SE-ResNext50) using the collected data.

RESULTS : SE-ResNext50 showed the best performance in terms of accuracy, specificity, and precision in the classification. SE-ResNext50 offered a precision of 81.12%, a sensitivity of 84.06%, and a specificity of 58.46%.

CONCLUSIONS : The results of our study suggested that deep learning algorithms have similar performance to an experienced cardiologist in detecting coronary artery lesions to facilitate the diagnosis of KD.

Lee Haeyun, Eun Yongsoon, Hwang Jae Youn, Eun Lucy Youngmin

2022-Jun-21

Coronary artery lesion, Deep learning, Explanable AI, Kawasaki disease, Ultrasound Image

General General

Early diagnosis of amyotrophic lateral sclerosis based on fasciculations in muscle ultrasonography: A machine learning approach.

In Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology

OBJECTIVE : Although fasciculation on muscle ultrasonography (MUS) is useful in diagnosing amyotrophic lateral sclerosis (ALS), its applicability to early diagnosis remains unclear. We aimed to develop and validate diagnostic models especially beneficial to early-stage ALS via machine learning.

METHODS : We investigated 100 patients with ALS, including 50 with early-stage ALS within 9 months from onset, and 100 without ALS. Fifteen muscles were bilaterally observed for 10 s each and the presence of fasciculations was recorded. Hierarchical clustering and nominal logistic regression, neural network, or ensemble learning were applied to the training cohort comprising the early-stage ALS to develop MUS-based diagnostic models, and they were tested in the validation cohort comprising the later-stage ALS.

RESULTS : Fasciculations on MUS in the brainstem or thoracic region had high specificity but limited sensitivities and predictive profiles for diagnosis of ALS. A machine learning-based model comprising eight muscles in the four body regions had a high sensitivity (recall), specificity, and positive predictive value (precision) for both early- and later-stage ALS patients.

CONCLUSIONS : We developed and validated MUS-fasciculation-based diagnostic models for early- and later-stage ALS.

SIGNIFICANCE : Fasciculation detected in relevant muscles on MUS can contribute to the diagnosis of ALS from the early stage.

Fukushima Koji, Takamatsu Naoko, Yamamoto Yuki, Yamazaki Hiroki, Yoshida Takeshi, Osaki Yusuke, Haji Shotaro, Fujita Koji, Sugie Kazuma, Izumi Yuishin

2022-Jun-17

Amyotrophic lateral sclerosis, Early diagnosis, Fasciculation, Machine learning, Muscle ultrasonography

Ophthalmology Ophthalmology

Patients Perceptions of Artificial Intelligence in Diabetic Eye Screening.

In Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)

PURPOSE : Artificial intelligence (AI) technology is poised to revolutionize modern delivery of health care services. We set to evaluate the patient perspective of AI use in diabetic retinal screening.

DESIGN : Survey.

METHODS : Four hundred thirty-eight patients undergoing diabetic retinal screening across New Zealand participated in a survey about their opinion of AI technology in retinal screening. The survey consisted of 13 questions covering topics of awareness, trust, and receptivity toward AI systems.

RESULTS : The mean age was 59 years. The majority of participants identified as New Zealand European (50%), followed by Asian (31%), Pacific Islander (10%), and Maori (5%). Whilst 73% of participants were aware of AI, only 58% have heard of it being implemented in health care. Overall, 78% of respondents were comfortable with AI use in their care, with 53% saying they would trust an AI-assisted screening program as much as a health professional. Despite having a higher awareness of AI, younger participants had lower trust in AI systems. A higher proportion of Maori and Pacific participants indicated a preference toward human-led screening. The main perceived benefits of AI included faster diagnostic speeds and greater accuracy.

CONCLUSIONS : There is low awareness of clinical AI applications among our participants. Despite this, most are receptive toward the implementation of AI in diabetic eye screening. Overall, there was a strong preference toward continual involvement of clinicians in the screening process. There are key recommendations to enhance the receptivity of the public toward incorporation of AI into retinal screening programs.

Yap Aaron, Wilkinson Benjamin, Chen Eileen, Han Lydia, Vaghefi Ehsan, Galloway Chris, Squirrell David

2022-May-01

Ophthalmology Ophthalmology

Metaverse and Virtual Health Care in Ophthalmology: Opportunities and Challenges.

In Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)

The outbreak of the coronavirus disease 2019 has further increased the urgent need for digital transformation within the health care settings, with the use of artificial intelligence/deep learning, internet of things, telecommunication network/virtual platform, and blockchain. The recent advent of metaverse, an interconnected online universe, with the synergistic combination of augmented, virtual, and mixed reality described several years ago, presents a new era of immersive and real-time experiences to enhance human-to-human social interaction and connection. In health care and ophthalmology, the creation of virtual environment with three-dimensional (3D) space and avatar, could be particularly useful in patient-fronting platforms (eg, telemedicine platforms), operational uses (eg, meeting organization), digital education (eg, simulated medical and surgical education), diagnostics, and therapeutics. On the other hand, the implementation and adoption of these emerging virtual health care technologies will require multipronged approaches to ensure interoperability with real-world virtual clinical settings, user-friendliness of the technologies and clinical efficiencies while complying to the clinical, health economics, regulatory, and cybersecurity standards. To serve the urgent need, it is important for the eye community to continue to innovate, invent, adapt, and harness the unique abilities of virtual health care technology to provide better eye care worldwide.

Tan Ting Fang, Li Yong, Lim Jane Sujuan, Gunasekeran Dinesh Visva, Teo Zhen Ling, Ng Wei Yan, Ting Daniel Sw

2022-May-01

General General

Study on the Application of Visual Communication Design in APP Interface Design in the Context of Deep Learning.

In Computational intelligence and neuroscience

Visual communication concepts enable linguistics or semiotics to the teaching of visual communication designs, creating graphic designs into an innovative and scientific discipline. The use of storyline techniques in visual communication not only inspires the imagination of designer but also arouses the visual memory of the audience. Besides, improving cultural heritage such as historical images is important to protect cultural diversity. Recently, the developments of deep learning (DL) and computer vision (CV) approaches make it possible for the automatic colorization of grayscale images into color images. Also, the usage of visual communication design in APP interface design has increased. With this motivation, this work introduces the enhanced deep learning-based automated historical image colorization (EDL-AHIC) technique for wireless network-enabled visual communication. The proposed EDL-AHIC technique intends to effectually convert the grayscale images into color images. The presented EDL-AHIC technique extracts the local as well as global features. For global feature extraction, the enhanced capsule network (ECN) model is applied. Finally, the fusion layer and decoding unit are employed to determine the output, i.e., chrominance component of the input image. A comprehensive experimental validation process is performed to ensure the betterment of the EDL-AHIC technique. The comparison study reported the supremacy of the EDL-AHIC technique over the other recent methods.

Luo Hui, Zeng Qiang

2022

General General

Proposal of Smith-Waterman algorithm on FPGA to accelerate the forward and backtracking steps.

In PloS one ; h5-index 176.0

In bioinformatics, alignment is an essential technique for finding similarities between biological sequences. Usually, the alignment is performed with the Smith-Waterman (SW) algorithm, a well-known sequence alignment technique of high-level precision based on dynamic programming. However, given the massive data volume in biological databases and their continuous exponential increase, high-speed data processing is necessary. Therefore, this work proposes a parallel hardware design for the SW algorithm with a systolic array structure to accelerate the forward and backtracking steps. For this purpose, the architecture calculates and stores the paths in the forward stage for pre-organizing the alignment, which reduces the complexity of the backtracking stage. The backtracking starts from the maximum score position in the matrix and generates the optimal SW sequence alignment path. The architecture was validated on Field-Programmable Gate Array (FPGA), and synthesis analyses have shown that the proposed design reaches up to 79.5 Giga Cell Updates per Second (GCPUS).

Oliveira Fabio F de, Dias Leonardo A, Fernandes Marcelo A C

2022

General General

Correlation networks of spinal motor neurons that innervate lower limb muscles during a multi-joint isometric task.

In The Journal of physiology ; h5-index 67.0

KEY POINTS : A central and unresolved question is how spinal motor neurons are controlled to generate movement; We decoded the spiking activities of dozens of spinal motor neurons innervating six muscles during a multi-joint task, and we used a purely data-driven method grounded on graph theory to extract networks of motor neurons based on their correlated activity (considered as common input); The vast majority of the identified motor neurons shared common inputs with other motor neuron(s); Groups of motor neurons were partly decoupled from their innervated muscle, such that motor neurons innervating the same muscle did not necessarily receive common inputs. Conversely, some motor neurons from different muscles - including distant muscles - received common inputs. It supports the theory that movement is produced through the control of groups of motor neurons via common inputs and that there is a partial mismatch between these groups of motor neurons and muscle anatomy.

ABSTRACT : Movements are reportedly controlled through the combination of synergies that generate specific motor outputs by imposing an activation pattern on a group of muscles. To date, the smallest unit of analysis of these synergies has been the muscle through the measurement of its activation. However, the muscle is not the lowest neural level of movement control. In this human study (n = 10), we used a purely data-driven method grounded on graph theory to extract networks of motor neurons based on their correlated activity during an isometric multi-joint task. Specifically, high-density surface electromyography recordings from six lower limb muscles were decomposed into motor neurons spiking activity. We analyzed these activities by identifying their common low-frequency components, from which networks of correlated activity to the motor neurons were derived and interpreted as networks of common synaptic inputs. The vast majority of the identified motor neurons shared common inputs with other motor neuron(s). In addition, groups of motor neurons were partly decoupled from their innervated muscle, such that motor neurons innervating the same muscle did not necessarily receive common inputs. Conversely, some motor neurons from different muscles - including distant muscles - received common inputs. Our study supports the theory that movements are produced through the control of small numbers of groups of motor neurons via common inputs and that there is a partial mismatch between these groups of motor neurons and muscle anatomy. We provide a new neural framework for a deeper understanding of the structure of common inputs to motor neurons. Abstract figure legend Ten participants performed an isometric multi-joint task, which consisted in producing force on an instrumented pedal. Adhesive grids of 64 electrodes were placed over six lower limb muscles (gastrocnemius medialis [GM] and lateralis [GL], vastus lateralis [VL] and medialis [VM], biceps femoris [BF], semitendinosus [ST]). The high-density EMG signals were decomposed into motor unit spike trains. For each pair of motor neurons, we assessed the correlation between their smoothed discharge rates to determine whether they shared common input. Then, we used a purely data-driven method grounded on graph theory to extract networks of common inputs and we applied a clustering procedure to group the motor neurons according to their positions in the graph (i.e., their correlated activity). Results support the theory that movement is produced through the control of small numbers of groups of motor neurons via common inputs and that there is a partial mismatch between these groups of motor neurons and muscle anatomy. This article is protected by copyright. All rights reserved.

Hug François, Avrillon Simon, Sarcher Aurélie, Del Vecchio Alessandro, Farina Dario

2022-Jun-30

coherence, common drive, common input, electromyography, motor units, muscle synergies

General General

Leveraging Algorithms to Improve Decision-Making Workflows for Genomic Data Access and Management.

In Biopreservation and biobanking

Studies on the ethics of automating clinical or research decision making using artificial intelligence and other algorithmic tools abound. Less attention has been paid, however, to the scope for, and ethics of, automating decision making within regulatory apparatuses governing the access, use, and exchange of data involving humans for research. In this article, we map how the binary logic flows and real-time capabilities of automated decision support (ADS) systems may be leveraged to accelerate one rate-limiting step in scientific discovery: data access management. We contend that improved auditability, consistency, and efficiency of the data access request process using ADS systems have the potential to yield fairer outcomes in requests for data largely sourced from biospecimens and biobanked samples. This procedural justice rationale reinforces a broader set of participant and data subject rights that data access committees (DACs) indirectly protect. DACs protect the rights of citizens to benefit from science by bringing researchers closer to the data they need to advance that science. DACs also protect the informational dignities of individuals and communities by ensuring the data being accessed are used in ways consistent with participant values. We discuss the development of the Global Alliance for Genomics and Health Data Use Ontology standard as a test case of ADS for genomic data access management specifically, and we synthesize relevant ethical, legal, and social challenges to its implementation in practice. We conclude with an agenda of future research needed to thoughtfully advance strategies for computational governance that endeavor to instill public trust in, and maximize the scientific value of, health-related human data across data types, environments, and user communities.

Rahimzadeh Vasiliki, Lawson Jonathan, Rushton Greg, Dove Edward S

2022-Jun-30

automated decision system, data access committee, ethics, genomics, health

General General

Effectiveness of artificial intelligence-assisted decision-making to improve vulnerable women's participation in cervical cancer screening in France: a cluster randomized controlled trial (AppDate-You).

In JMIR research protocols ; h5-index 26.0

BACKGROUND : The French organized population-based cervical cancer screening (CCS) programme shifted from a cytology-based to a human papillomavirus (HPV)-based screening strategy in August 2020. HPV testing is offered every 5 years, starting at age 30 years. In the new programme, women are invited to have the HPV test at a gynaecologist's, primary care physician's, or midwife's office, a private clinic or health centre, family planning centre, or hospital. HPV self-sampling (HPVss) was also made available as an additional approach. However, French studies reported that less than 20% of non-compliant women performed vaginal self-sampling when a kit was sent to their home. Women with lower income and educational levels participate less in CCS. Lack of information about the disease and the benefits of CCS was reported as one of the major barriers among non-compliant women. This barrier could be addressed by overcoming disparities in HPV- and CC-related knowledge and perceptions about CCS.

OBJECTIVE : The main objective of this study is to assess the effectiveness of a chatbot-based decision aid to improve women's participation in the HPVss detection-based CCS care pathway.

METHODS : AppDate-You is a two-arm cluster randomized controlled trial (cRCT) nested within the French organized CCS programme. Eligible women are those aged 30-65 years who have not been screened for CC for more than 4 years and live in the disadvantaged clusters in the Occitanie Region, France. Thirty-two clusters will be allocated to the intervention and control arms, 16 in each arm (approximately 4000 women). Eligible women living in randomly selected disadvantaged clusters will be identified using the Regional Cancer Screening Coordinating Centre of Occitanie (CRCDC-OC) database. Women in the experimental group will receive screening reminder letters and HPVss kits, combined with access to a chatbot-based decision aid tailored to women with lower educational attainment. Women in the control group will receive the reminder letters and HPVss kits (standard of care). The CRCDC-OC database will be used to check the progress of the trial and assess the impact of the intervention. The trial has two primary outcomes: 1) the proportion of screening participation within 12 months among women recalled for CCS, and 2) the proportion of HPVss-positive women who are "well-managed" as stipulated in the French guidelines.

RESULTS : To date, the AppDate-You study group is preparing and developing the chatbot-based decision aid (intervention). The cRCT will be conducted once the decision aid has been completed and validated. Recruitment of women is expected to begin in January 2023.

CONCLUSIONS : This study is the first to evaluate the impact of a chatbot-based decision aid to promote the CCS programme and increase its performance. The study results will inform policy-makers and health professionals as well as the research community.

CLINICALTRIAL : The study was registered on clinicaltrials.gov: NCT05286034, on March 18, 2022.

Selmouni Farida, Guy Marine, Muwonge Richard, Nassiri Abdelhak, Lucas Eric, Basu Partha, Sauvaget Catherine

2022-Jun-03

General General

Machine learning-based classification of the movements of children with profound or severe intellectual or multiple disabilities using environment data features.

In PloS one ; h5-index 176.0

Communication interventions have broadened from dialogical meaning-making, assessment approaches, to remote-controlled interactive objects. Yet, interpretation of the mostly pre-or protosymbolic, distinctive, and idiosyncratic movements of children with intellectual disabilities (IDs) or profound intellectual and multiple disabilities (PIMD) using computer-based assistive technology (AT), machine learning (ML), and environment data (ED: location, weather indices and time) remain insufficiently unexplored. We introduce a novel behavior inference computer-based communication-aid AT system structured on machine learning (ML) framework to interpret the movements of children with PIMD/IDs using ED. To establish a stable system, our study aimed to train, cross-validate (10-fold), test and compare the classification accuracy performance of ML classifiers (eXtreme gradient boosting [XGB], support vector machine [SVM], random forest [RF], and neural network [NN]) on classifying the 676 movements to 2, 3, or 7 behavior outcome classes using our proposed dataset recalibration (adding ED to movement datasets) with or without Boruta feature selection (53 child characteristics and movements, and ED-related features). Natural-child-caregiver-dyadic interactions observed in 105 single-dyad video-recorded (30-hour) sessions targeted caregiver-interpreted facial, body, and limb movements of 20 8-to 16-year-old children with PIMD/IDs and simultaneously app-and-sensor-collected ED. Classification accuracy variances and the influences of and the interaction among recalibrated dataset, feature selection, classifiers, and classes on the pooled classification accuracy rates were evaluated using three-way ANOVA. Results revealed that Boruta and NN-trained dataset in class 2 and the non-Boruta SVM-trained dataset in class 3 had >76% accuracy rates. Statistically significant effects indicating high classification rates (>60%) were found among movement datasets: with ED, non-Boruta, class 3, SVM, RF, and NN. Similar trends (>69%) were found in class 2, NN, Boruta-trained movement dataset with ED, and SVM and RF, and non-Boruta-trained movement dataset with ED in class 3. These results support our hypotheses that adding environment data to movement datasets, selecting important features using Boruta, using NN, SVM and RF classifiers, and classifying movements to 2 and 3 behavior outcomes can provide >73.3% accuracy rates, a promising performance for a stable ML-based behavior inference communication-aid AT system for children with PIMD/IDs.

Herbuela Von Ralph Dane Marquez, Karita Tomonori, Furukawa Yoshiya, Wada Yoshinori, Toya Akihiro, Senba Shuichiro, Onishi Eiko, Saeki Tatsuo

2022

General General

Altruistic Collaborative Learning.

In IEEE transactions on neural networks and learning systems

This article proposes a new learning paradigm based on the concept of concordant gradients for ensemble learning strategies. In this paradigm, learners update their weights if and only if the gradients of their cost functions are mutually concordant in a sense given by paper. The objective of the proposed concordant optimization framework is robustness against uncertainties by postponing to a later epoch, the consideration of examples associated with discordant directions during a training phase. Concordance constrained collaboration is shown to be relevant, especially in intricate classification issues where exclusive class labeling involves information bias due to correlated disturbances affecting almost all training examples. The first learning paradigm applies on a gradient descent strategy based on allied agents, subjected to concordance checking before moving forward in training epochs. The second learning paradigm is related to multivariate dense neural matrix fusion, where the fusion operator is itself a learnable neural operator. In addition to these paradigms, this article proposes a new categorical probability transform to enrich the existing collection and propose an alternative scenario for integrating penalized SoftMax information. Finally, this article assesses the relevance of the above contributions with respect to several deep learning frameworks and a collaborative classification involving dependent classes.

Atto Abdourrahmane Mahamane

2022-Jun-30

General General

Deep Learning Is Singular, and That's Good.

In IEEE transactions on neural networks and learning systems

In singular models, the optimal set of parameters forms an analytic set with singularities, and a classical statistical inference cannot be applied to such models. This is significant for deep learning as neural networks are singular, and thus", dividing" by the determinant of the Hessian or employing the Laplace approximation is not appropriate. Despite its potential for addressing fundamental issues in deep learning, a singular learning theory appears to have made little inroads into the developing canon of a deep learning theory. Via a mix of theory and experiment, we present an invitation to the singular learning theory as a vehicle for understanding deep learning and suggest an important future work to make the singular learning theory directly applicable to how deep learning is performed in practice.

Wei Susan, Murfet Daniel, Gong Mingming, Li Hui, Gell-Redman Jesse, Quella Thomas

2022-Jun-30

General General

Significance Tests of Feature Relevance for a Black-Box Learner.

In IEEE transactions on neural networks and learning systems

An exciting recent development is the uptake of deep neural networks in many scientific fields, where the main objective is outcome prediction with a black-box nature. Significance testing is promising to address the black-box issue and explore novel scientific insights and interpretations of the decision-making process based on a deep learning model. However, testing for a neural network poses a challenge because of its black-box nature and unknown limiting distributions of parameter estimates while existing methods require strong assumptions or excessive computation. In this article, we derive one-split and two-split tests relaxing the assumptions and computational complexity of existing black-box tests and extending to examine the significance of a collection of features of interest in a dataset of possibly a complex type, such as an image. The one-split test estimates and evaluates a black-box model based on estimation and inference subsets through sample splitting and data perturbation. The two-split test further splits the inference subset into two but requires no perturbation. Also, we develop their combined versions by aggregating the p -values based on repeated sample splitting. By deflating the bias-sd-ratio, we establish asymptotic null distributions of the test statistics and the consistency in terms of Type 2 error. Numerically, we demonstrate the utility of the proposed tests on seven simulated examples and six real datasets. Accompanying this article is our python library dnn-inference (https://dnn-inference.readthedocs.io/en/latest/) that implements the proposed tests.

Dai Ben, Shen Xiaotong, Pan Wei

2022-Jun-30

General General

Artificial intelligence in differentiating tropical infections: A step ahead.

In PLoS neglected tropical diseases ; h5-index 79.0

BACKGROUND AND OBJECTIVE : Differentiating tropical infections are difficult due to its homogenous nature of clinical and laboratorial presentations among them. Sophisticated differential tests and prediction tools are better ways to tackle this issue. Here, we aimed to develop a clinician assisted decision making tool to differentiate the common tropical infections.

METHODOLOGY : A cross sectional study through 9 item self-administered questionnaire were performed to understand the need of developing a decision making tool and its parameters. The most significant differential parameters among the identified infections were measured through a retrospective study and decision tree was developed. Based on the parameters identified, a multinomial logistic regression model and a machine learning model were developed which could better differentiate the infection.

RESULTS : A total of 40 physicians involved in the management of tropical infections were included for need analysis. Dengue, malaria, leptospirosis and scrub typhus were the common tropical infections in our settings. Sodium, total bilirubin, albumin, lymphocytes and platelets were the laboratory parameters; and abdominal pain, arthralgia, myalgia and urine output were the clinical presentation identified as better predictors. In multinomial logistic regression analysis with dengue as a reference revealed a predictability of 60.7%, 62.5% and 66% for dengue, malaria and leptospirosis, respectively, whereas, scrub typhus showed only 38% of predictability. The multi classification machine learning model observed to have an overall predictability of 55-60%, whereas a binary classification machine learning algorithms showed an average of 79-84% for one vs other and 69-88% for one vs one disease category.

CONCLUSION : This is a first of its kind study where both statistical and machine learning approaches were explored simultaneously for differentiating tropical infections. Machine learning techniques in healthcare sectors will aid in early detection and better patient care.

Shenoy Shreelaxmi, Rajan Asha K, Rashid Muhammed, Chandran Viji Pulikkel, Poojari Pooja Gopal, Kunhikatta Vijayanarayana, Acharya Dinesh, Nair Sreedharan, Varma Muralidhar, Thunga Girish

2022-Jun

General General

Predicting terrorist attacks in the United States using localized news data.

In PloS one ; h5-index 176.0

Terrorism is a major problem worldwide, causing thousands of fatalities and billions of dollars in damage every year. To address this threat, we propose a novel feature representation method and evaluate machine learning models that learn from localized news data in order to predict whether a terrorist attack will occur on a given calendar date and in a given state. The best model (a Random Forest aided by a novel variable-length moving average method) achieved area under the receiver operating characteristic (AUROC) of ≥ 0.667 (statistically significant w.r.t. random guessing with p ≤ .0001) on four of the five states that were impacted most by terrorism between 2015 and 2018. These results demonstrate that treating terrorism as a set of independent events, rather than as a continuous process, is a fruitful approach-especially when historical events are sparse and dissimilar-and that large-scale news data contains information that is useful for terrorism prediction. Our analysis also suggests that predictive models should be localized (i.e., state models should be independently designed, trained, and evaluated) and that the characteristics of individual attacks (e.g., responsible group or weapon type) were not correlated with prediction success. These contributions provide a foundation for the use of machine learning in efforts against terrorism in the United States and beyond.

Krieg Steven J, Smith Christian W, Chatterjee Rusha, Chawla Nitesh V

2022

General General

Rapid prediction of protein natural frequencies using graph neural networks.

In Digital discovery

Natural vibrational frequencies of proteins help to correlate functional shifts with sequence or geometric variations that lead to negligible changes in protein structures, such as point mutations related to disease lethality or medication effectiveness. Normal mode analysis is a well-known approach to accurately obtain protein natural frequencies. However, it is not feasible when high-resolution protein structures are not available or time consuming to obtain. Here we provide a machine learning model to directly predict protein frequencies from primary amino acid sequences and low-resolution structural features such as contact or distance maps. We utilize a graph neural network called principal neighborhood aggregation, trained with the structural graphs and normal mode frequencies of more than 34 000 proteins from the protein data bank. combining with existing contact/distance map prediction tools, this approach enables an end-to-end prediction of the frequency spectrum of a protein given its primary sequence.

Guo Kai, Buehler Markus J

2022-Jun-13

Surgery Surgery

Dissecting Self-Supervised Learning Methods for Surgical Computer Vision

ArXiv Preprint

The field of surgical computer vision has undergone considerable breakthroughs in recent years with the rising popularity of deep neural network-based methods. However, standard fully-supervised approaches for training such models require vast amounts of annotated data, imposing a prohibitively high cost; especially in the clinical domain. Self-Supervised Learning (SSL) methods, which have begun to gain traction in the general computer vision community, represent a potential solution to these annotation costs, allowing to learn useful representations from only unlabeled data. Still, the effectiveness of SSL methods in more complex and impactful domains, such as medicine and surgery, remains limited and unexplored. In this work, we address this critical need by investigating four state-of-the-art SSL methods (MoCo v2, SimCLR, DINO, SwAV) in the context of surgical computer vision. We present an extensive analysis of the performance of these methods on the Cholec80 dataset for two fundamental and popular tasks in surgical context understanding, phase recognition and tool presence detection. We examine their parameterization, then their behavior with respect to training data quantities in semi-supervised settings. Correct transfer of these methods to surgery, as described and conducted in this work, leads to substantial performance gains over generic uses of SSL - up to 7% on phase recognition and 20% on tool presence detection - as well as state-of-the-art semi-supervised phase recognition approaches by up to 14%. The code will be made available at https://github.com/CAMMA-public/SelfSupSurg.

Sanat Ramesh, Vinkle Srivastav, Deepak Alapatt, Tong Yu, Aditya Murali, Luca Sestini, Chinedu Innocent Nwoye, Idris Hamoud, Antoine Fleurentin, Georgios Exarchakis, Alexandros Karargyris, Nicolas Padoy

2022-07-01

oncology Oncology

Artificial intelligence in clinical applications for lung cancer: diagnosis, treatment and prognosis.

In Clinical chemistry and laboratory medicine ; h5-index 46.0

Artificial Intelligence (AI) is a branch of computer science that includes research in robotics, language recognition, image recognition, natural language processing, and expert systems. AI is poised to change medical practice, and oncology is not an exception to this trend. As the matter of fact, lung cancer has the highest morbidity and mortality worldwide. The leading cause is the complexity of associating early pulmonary nodules with neoplastic changes and numerous factors leading to strenuous treatment choice and poor prognosis. AI can effectively enhance the diagnostic efficiency of lung cancer while providing optimal treatment and evaluating prognosis, thereby reducing mortality. This review seeks to provide an overview of AI relevant to all the fields of lung cancer. We define the core concepts of AI and cover the basics of the functioning of natural language processing, image recognition, human-computer interaction and machine learning. We also discuss the most recent breakthroughs in AI technologies and their clinical application regarding diagnosis, treatment, and prognosis in lung cancer. Finally, we highlight the future challenges of AI in lung cancer and its impact on medical practice.

Pei Qin, Luo Yanan, Chen Yiyu, Li Jingyuan, Xie Dan, Ye Ting

2022-Jun-30

artificial intelligence, diagnosis, lung cancer, prognosis, treatment

Radiology Radiology

TP53 Mutation Estimation Based on MRI Radiomics Analysis for Breast Cancer.

In Journal of magnetic resonance imaging : JMRI

BACKGROUND : Noninvasive detection of TP53 mutations is useful for the molecular stratification of breast cancer.

PURPOSE : To explore MRI radiomics features reflecting TP53 mutations in breast cancer and propose a classifier for detecting such mutations.

STUDY TYPE : Retrospective.

POPULATION/SUBJECTS : A total of 139 breast cancer patients with TP53 expression profiling (98 with TP53 mutations and 41 without TP53 mutations).

FIELD STRENGTH/SEQUENCE : 1.5 T, T1-weighted (T1W) DCE-MRI.

ASSESSMENT : Lesions were manually segmented using subtracted T1WI. A total of 944 radiomics features (including 744 wavelet-related features) and 7 clinicopathological features were extracted from each lesion. Principal component analysis and Pearson's correlation analysis were used to preprocess the features. Linear discriminant analysis, logistic regression (LR), support vector machine (SVM), and random forest (RF) were used as the classifiers.

STATISTICAL TESTS : Analysis of variance, Kruskal-Wallis and recursive features elimination were used to select features. Receiver operating characteristic (ROC) analysis was performed to compare the diagnostic accuracy.

RESULTS : For the radiomics model, the validation cohorts AUCs of the four classifiers ranged from 0.69 (RF) to 0.74 (LR), and LR (0.74) attained the highest AUCs. For the clinicopathological-radiomics combined model, the validation AUCs of the four classifiers ranged from 0.68 (RF) to 0.86 (SVM), and SVM (0.86) attained highest AUCs. In the subgroup analysis of triple-negative (TN) and luminal type breast cancer, RF achieved the highest AUCs (0.83 and 0.94).

DATA CONCLUSION : Clinicopathological-radiomics combined model with SVM could be used as noninvasive biomarkers for predicting TP53 mutations. RF was recommended for the detection of TP53 mutations in TN and luminal type breast cancer.

LEVEL OF EVIDENCE : 3 TECHNICAL EFFICACY: Stage 2.

Sun Kun, Zhu Hong, Chai Weimin, Yan Fuhua

2022-Jun-30

breast cancer, machine learning, radiomics

General General

Cell-Free DNA as Biomarker for Sepsis by Integration of Microbial and Host Information.

In Clinical chemistry ; h5-index 61.0

BACKGROUND : Cell-free DNA (cfDNA) is emerging as a biomarker for sepsis. Previous studies have been focused mainly on identifying blood infections or simply quantifying cfDNA. We propose that by characterizing multifaceted unexplored components, cfDNA could be more informative for assessing this complex syndrome.

METHODS : We explored multiple aspects of cfDNA in septic and nonseptic intensive care unit (ICU) patients by metagenomic sequencing, with longitudinal measurement and integrative assessment of plasma cfDNA quantity, human cfDNA fragmentation patterns, infecting pathogens, and overall microbial composition.

RESULTS : Septic patients had significantly increased cfDNA quantity and altered human cfDNA fragmentation pattern. Moreover, human cfDNA fragments appeared to comprise information about cellular oxidative stress and could indicate disease severity. Metagenomic sequencing was more sensitive than blood culture in detecting bacterial infections and allowed for simultaneous detection of viral pathogens. We found differences in microbial composition between septic and nonseptic patients and between survivors and nonsurvivors by 28-day mortality, both on the first day of ICU admission and across the study period. By integrating all the information into a machine learning model, we achieved improved performance in identifying sepsis and prediction of clinical outcome for ICU patients with areas under the curve of 0.992 (95% CI 0.969-1.000) and 0.802 (95% CI 0.605-0.999), respectively.

CONCLUSIONS : We were able to diagnose sepsis and predict mortality as soon as the first day of ICU admission by integrating multifaceted cfDNA information obtained in a single metagenomic assay; this approach could provide important advantages for clinical management and for improving outcomes in ICU patients.

Jing Qiuyu, Leung Chi Hung Czarina, Wu Angela Ruohao

2022-Jun-30

biomarker, cell-free DNA, diagnosis, metagenomics, prognosis, sepsis

General General

PScL-DDCFPred: an ensemble deep learning-based approach for characterizing multiclass subcellular localization of human proteins from bioimage data.

In Bioinformatics (Oxford, England)

MOTIVATION : Characterization of protein subcellular localization has become an important and long-standing task in bioinformatics and computational biology, which provides valuable information for elucidating various cellular functions of proteins and guiding drug design.

RESULTS : Here, we develop a novel bioimage-based computational approach, termed PScL-DDCFPred, to accurately predict protein subcellular localizations in human tissues. PScL-DDCFPred first extracts multiview image features, including global and local features, as base or pure features; Next, it applies a new integrative feature selection method based on stepwise discriminant analysis and generalized discriminant analysis to identify the optimal feature sets from the extracted pure features; Finally, a classifier based on deep neural network (DNN) and deep-cascade forest (DCF) is established. Stringent ten-fold cross-validation tests on the new protein subcellular localization training dataset, constructed from the human protein atlas databank, illustrates that PScL-DDCFPred achieves a better performance than several existing state-of-the-art methods. Moreover, the independent test set further illustrates the generalization capability and superiority of PScL-DDCFPred over existing predictors. In-depth analysis shows that the excellent performance of PScL-DDCFPred can be attributed to three critical factors, namely the effective combination of the DNN and DCF models, complementarity of global and local features, and use of the optimal feature sets selected by the integrative feature selection algorithm.

AVAILABILITY : https://github.com/csbio-njust-edu/PScL-DDCFPred.

SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.

Ullah Matee, Hadi Fazal, Song Jiangning, Yu Dong-Jun

2022-Jun-30

Public Health Public Health

ResPAN: a powerful batch correction model for scRNA-seq data through residual adversarial networks.

In Bioinformatics (Oxford, England)

MOTIVATION : With the advancement of technology, we can generate and access large-scale, high dimensional and diverse genomics data, especially through single-cell RNA sequencing (scRNA-seq). However, integrative downstream analysis from multiple scRNA-seq datasets remains challenging due to batch effects.

RESULTS : In this paper, we propose a light-structured deep learning framework called ResPAN for scRNA-seq data integration. ResPAN is based on Wasserstein Generative Adversarial Network (WGAN) combined with random walk mutual nearest neighbor pairing and fully skip-connected autoencoders to reduce the differences among batches. We also discuss the limitations of existing methods and demonstrate the advantages of our model over seven other methods through extensive benchmarking studies on both simulated data under various scenarios and real datasets across different scales. Our model achieves leading performance on both batch correction and biological information conservation and maintains scalable to datasets with over half a million cells.

AVAILABILITY : An open-source implementation of ResPAN and scripts to reproduce the results can be downloaded from: https://github.com/AprilYuge/ResPAN.

SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.

Wang Yuge, Liu Tianyu, Zhao Hongyu

2022-Jun-30

General General

RAPPPID: Towards Generalisable Protein Interaction Prediction with AWD-LSTM Twin Networks.

In Bioinformatics (Oxford, England)

MOTIVATION : Computational methods for the prediction of protein-protein interactions, while important tools for researchers, are plagued by challenges in generalising to unseen proteins. Datasets used for modelling protein-protein predictions are particularly predisposed to information leakage and sampling biases.

RESULTS : In this study, we introduce RAPPPID, a method for the Regularised Automatic Prediction of Protein-Protein Interactions using Deep Learning. RAPPPID is a twin AWD-LSTM network which employs multiple regularisation methods during training time to learn generalised weights. Testing on stringent interaction datasets composed of proteins not seen during training, RAPPPID outperforms state-of-the-art methods. Further experiments show that RAPPPID's performance holds regardless of the particular proteins in the testing set and its performance is higher for experimentally supported edges. This study serves to demonstrate that appropriate regularisation is an important component of overcoming the challenges of creating models for protein-protein interaction prediction that generalise to unseen proteins. Additionally, as part of this study, we provide datasets corresponding to several data splits of various strictness, in order to facilitate assessment of PPI reconstruction methods by others in the future.

AVAILABILITY : Code and datasets are freely available at https://github.com/jszym/rapppid and https://zenodo.org.

SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.

Szymborski Joseph, Emad Amin

2022-Jun-30

General General

Separating clinical antibodies from repertoire antibodies, a path to in silico developability assessment.

In mAbs

Approaches for antibody discovery have seen substantial improvement and success in recent years. Yet, advancing antibodies into the clinic remains difficult because therapeutic developability concerns are challenging to predict. We developed a computational model to simplify antibody developability assessment and enable accelerated early-stage screening. To this end, we quantified the ability of hundreds of sequence- and structure-based descriptors to differentiate clinical antibodies that have undergone rigorous screening and characterization for drug-like properties from antibodies in the human repertoire that are not natively paired. This analysis identified 144 descriptors capable of distinguishing clinical from repertoire antibodies. Five descriptors were selected and combined based on performance and orthogonality into a single model referred to as the Therapeutic Antibody Developability Analysis (TA-DA). On a hold-out test set, this tool separated clinical antibodies from repertoire antibodies with an AUC = 0.8, demonstrating the ability to identify developability attributes unique to clinical antibodies. Based on our results, the TA-DA score may serve as an approach for selecting lead antibodies for further development.Abbreviations: Affinity-Capture Self-Interaction Nanoparticle Spectroscopy (AC-SINS), Area Under the Curve (AUC), Complementary-Determining Region (CDR), Clinical-Stage Therapeutics (CST), Framework (FR), Monoclonal Antibodies (mAbs), Observed Antibody Space (OAS), Receiver Operating Characteristic (ROC), Size-Exclusion Chromatography (SEC), Structural Aggregation Propensity (SAP), Therapeutic Antibody Developability Analysis (TA-DA), Therapeutic Antibody Profiler (TAP), Therapeutic Structural Antibody Database (Thera-SAbDab), Variable Heavy (VH), Variable Light (VL).

Negron Christopher, Fang Joyce, McPherson Michael J, Stine W Blaine, McCluskey Andrew J

Developability, drug discovery, machine learning, repertoire antibodies, therapeutic antibodies

oncology Oncology

Development and Validation of a Machine Learning Algorithm Predicting Emergency Department Use and Unplanned Hospitalization in Patients With Head and Neck Cancer.

In JAMA otolaryngology-- head & neck surgery

Importance : Patient-reported symptom burden was recently found to be associated with emergency department use and unplanned hospitalization (ED/Hosp) in patients with head and neck cancer. It was hypothesized that symptom scores could be combined with administrative health data to accurately risk stratify patients.

Objective : To develop and validate a machine learning approach to predict future ED/Hosp in patients with head and neck cancer.

Design, Setting, and Participants : This was a population-based predictive modeling study of patients in Ontario, Canada, diagnosed with head and neck cancer from January 2007 through March 2018. All outpatient clinical encounters were identified. Edmonton Symptom Assessment System (ESAS) scores and clinical and demographic factors were abstracted. Training and test cohorts were randomly generated in a 4:1 ratio. Various machine learning algorithms were explored, including (1) logistic regression using a least absolute shrinkage and selection operator, (2) random forest, (3) gradient boosting machine, (4) k-nearest neighbors, and (5) an artificial neural network. Data analysis was performed from September 2021 to January 2022.

Main Outcomes and Measures : The main outcome was any 14-day ED/Hosp event following symptom assessment. The performance of each model was assessed on the test cohort using the area under the receiver operator characteristic (AUROC) curve and calibration plots. Shapley values were used to identify the variables with greatest contribution to the model.

Results : The training cohort consisted of 9409 patients (mean [SD] age, 63.3 [10.9] years) undergoing 59 089 symptom assessments (80%). The remaining 2352 patients (mean [SD] age, 63.3 [11] years) and 14 193 symptom assessments were set aside as the test cohort (20%). Several models had high predictive accuracy, particularly the gradient boosting machine (validation AUROC, 0.80 [95% CI, 0.78-0.81]). A Youden-based cutoff corresponded to a validation sensitivity of 0.77 and specificity of 0.66. Patient-reported symptom scores were consistently identified as being the most predictive features within models. A second model built only with symptom severity data had an AUROC of 0.72 (95% CI, 0.70-0.74).

Conclusions and Relevance : In this study, machine learning approaches predicted with a high degree of accuracy ED/Hosp in patients with head and neck cancer. These tools could be used to accurately risk stratify patients and may help direct targeted intervention.

Noel Christopher W, Sutradhar Rinku, Gotlib Conn Lesley, Forner David, Chan Wing C, Fu Rui, Hallet Julie, Coburn Natalie G, Eskander Antoine

2022-Jun-30

Radiology Radiology

Ada-GridRF: A Fast and Automated Adaptive Boost Based Grid Search Optimized Random Forest Ensemble model for Lung Cancer Detection.

In Physical and engineering sciences in medicine

Lung cancer is considered one of the leading causes of death all across the world. Various radiology-related fields increasingly have used Computer-aided diagnosis (CAD) systems. It just has already become a part of clinical work for lung cancer detection. In this article, we proposed an Adaptive Boost-based Grid Search Optimized Random Forest (Ada-GridRF) classifier that best optimized the hyperparameters of the base random forest model to identify the malignant and non-malignant nodules from the trained CT images. Improved performance speed and reduced computational complexity were the advantages of the proposed method. The proposed methodology was compared with other hyperparameter optimization techniques and also with different conventional approaches. It even outperformed the popular state-of-the-art deep learning techniques such as transfer learning and convolutional neural network. The experimental results proved that the proposed method yielded the best performance metrics of 97.97% accuracy, 100% sensitivity, 96% specificity, 96.08% precision, 98% F1-score, 4% False positives rate, and 99.8% Area under the ROC curve (AUC). It took only 8 msec to train the model. Thus, the proposed Ada-GridRF model can aid radiologists in fast lung cancer detection.

Bhattacharjee Ananya, Murugan R, Soni Badal, Goel Tripti

2022-Jun-30

Ada Boost, Computer-aided diagnosis, Grid search, Hyperparameter optimization, Lung cancer

Radiology Radiology

A narrative review on current imaging applications of artificial intelligence and radiomics in oncology: focus on the three most common cancers.

In La Radiologia medica

The use of artificial intelligence (AI) and radiomics in the healthcare setting to advance disease diagnosis and management and facilitate the creation of new therapeutics is gaining popularity. Given the vast amount of data collected during cancer therapy, there is significant concern in leveraging the algorithms and technologies available with the underlying goal of improving oncologic care. Radiologists will attain better precision and effectiveness with the advent of AI technology, making machine-assisted medical services a valuable and important option for future oncologic medical care. As a result, it is critical to figure out which specific radiology activities are best positioned to gain from AI and radiomics models and methods of oncologic imaging, while also considering the algorithms' capabilities and constraints. Our purpose is to overview the current evidence and future prospects of AI and radiomics algorithms used in oncologic imaging efforts with an emphasis on the three most frequent cancers worldwide, i.e., lung cancer, breast cancer and colorectal cancer. We discuss how AI and radiomics could be used to detect and characterize cancers and assess therapy response.

Vicini Simone, Bortolotto Chandra, Rengo Marco, Ballerini Daniela, Bellini Davide, Carbone Iacopo, Preda Lorenzo, Laghi Andrea, Coppola Francesca, Faggioni Lorenzo

2022-Jun-30

Artificial intelligence, Cancer imaging, Deep learning, Machine learning, Oncology, Radiomics

General General

Genomics enters the deep learning era.

In PeerJ

The tremendous amount of biological sequence data available, combined with the recent methodological breakthrough in deep learning in domains such as computer vision or natural language processing, is leading today to the transformation of bioinformatics through the emergence of deep genomics, the application of deep learning to genomic sequences. We review here the new applications that the use of deep learning enables in the field, focusing on three aspects: the functional annotation of genomes, the sequence determinants of the genome functions and the possibility to write synthetic genomic sequences.

Routhier Etienne, Mozziconacci Julien

2022

Bioinformatics, Deep learning, Epigenomics, Genetics, Genomics, Metagenomics, Neural networks, Personalized medecine, Review, Synthetic genomes

General General

CEDAR: Communication Efficient Distributed Analysis for Regressions

ArXiv Preprint

Electronic health records (EHRs) offer great promises for advancing precision medicine and, at the same time, present significant analytical challenges. Particularly, it is often the case that patient-level data in EHRs cannot be shared across institutions (data sources) due to government regulations and/or institutional policies. As a result, there are growing interests about distributed learning over multiple EHRs databases without sharing patient-level data. To tackle such challenges, we propose a novel communication efficient method that aggregates the local optimal estimates, by turning the problem into a missing data problem. In addition, we propose incorporating posterior samples of remote sites, which can provide partial information on the missing quantities and improve efficiency of parameter estimates while having the differential privacy property and thus reducing the risk of information leaking. The proposed approach, without sharing the raw patient level data, allows for proper statistical inference and can accommodate sparse regressions. We provide theoretical investigation for the asymptotic properties of the proposed method for statistical inference as well as differential privacy, and evaluate its performance in simulations and real data analyses in comparison with several recently developed methods.

Changgee Chang, Zhiqi Bu, Qi Long

2022-07-01

General General

Application of machine learning to pretherapeutically estimate dosimetry in men with advanced prostate cancer treated with 177Lu-PSMA I&T therapy.

In European journal of nuclear medicine and molecular imaging ; h5-index 66.0

PURPOSE : Although treatment planning and individualized dose application for emerging prostate-specific membrane antigen (PSMA)-targeted radioligand therapy (RLT) are generally recommended, it is still difficult to implement in practice at the moment. In this study, we aimed to prove the concept of pretherapeutic prediction of dosimetry based on imaging and laboratory measurements before the RLT treatment.

METHODS : Twenty-three patients with metastatic castration-resistant prostate cancer (mCRPC) treated with 177Lu-PSMA I&T RLT were included retrospectively. They had available pre-therapy 68 Ga-PSMA-HEBD-CC PET/CT and at least 3 planar and 1 SPECT/CT imaging for dosimetry. Overall, 43 cycles of 177Lu-PSMA I&T RLT were applied. Organ-based standard uptake values (SUVs) were obtained from pre-therapy PET/CT scans. Patient dosimetry was calculated for the kidney, liver, spleen, and salivary glands using Hermes Hybrid Dosimetry 4.0 from the planar and SPECT/CT images. Machine learning methods were explored for dose prediction from organ SUVs and laboratory measurements. The uncertainty of these dose predictions was compared with the population-based dosimetry estimates. Mean absolute percentage error (MAPE) was used to assess the prediction uncertainty of estimated dosimetry.

RESULTS : An optimal machine learning method achieved a dosimetry prediction MAPE of 15.8 ± 13.2% for the kidney, 29.6% ± 13.7% for the liver, 23.8% ± 13.1% for the salivary glands, and 32.1 ± 31.4% for the spleen. In contrast, the prediction based on literature population mean has significantly larger MAPE (p < 0.01), 25.5 ± 17.3% for the kidney, 139.1% ± 111.5% for the liver, 67.0 ± 58.3% for the salivary glands, and 54.1 ± 215.3% for the spleen.

CONCLUSION : The preliminary results confirmed the feasibility of pretherapeutic estimation of treatment dosimetry and its added value to empirical population-based estimation. The exploration of dose prediction may support the implementation of treatment planning for RLT.

Xue Song, Gafita Andrei, Dong Chao, Zhao Yu, Tetteh Giles, Menze Bjoern H, Ziegler Sibylle, Weber Wolfgang, Afshar-Oromieh Ali, Rominger Axel, Eiber Matthias, Shi Kuangyu

2022-Jun-30

177Lu-PSMA I&T, Dosimetry, Machine learning, Radioligand therapy, Treatment planning

Radiology Radiology

Combined diagnosis of multiparametric MRI-based deep learning models facilitates differentiating triple-negative breast cancer from fibroadenoma magnetic resonance BI-RADS 4 lesions.

In Journal of cancer research and clinical oncology

PURPOSE : To investigate the value of the combined diagnosis of multiparametric MRI-based deep learning models to differentiate triple-negative breast cancer (TNBC) from fibroadenoma magnetic resonance Breast Imaging-Reporting and Data System category 4 (BI-RADS 4) lesions and to evaluate whether the combined diagnosis of these models could improve the diagnostic performance of radiologists.

METHODS : A total of 319 female patients with 319 pathologically confirmed BI-RADS 4 lesions were randomly divided into training, validation, and testing sets in this retrospective study. The three models were established based on contrast-enhanced T1-weighted imaging, diffusion-weighted imaging, and T2-weighted imaging using the training and validation sets. The artificial intelligence (AI) combination score was calculated according to the results of three models. The diagnostic performances of four radiologists with and without AI assistance were compared with the AI combination score on the testing set. The area under the curve (AUC), sensitivity, specificity, accuracy, and weighted kappa value were calculated to assess the performance.

RESULTS : The AI combination score yielded an excellent performance (AUC = 0.944) on the testing set. With AI assistance, the AUC for the diagnosis of junior radiologist 1 (JR1) increased from 0.833 to 0.885, and that for JR2 increased from 0.823 to 0.876. The AUCs of senior radiologist 1 (SR1) and SR2 slightly increased from 0.901 and 0.950 to 0.925 and 0.975 after AI assistance, respectively.

CONCLUSION : Combined diagnosis of multiparametric MRI-based deep learning models to differentiate TNBC from fibroadenoma magnetic resonance BI-RADS 4 lesions can achieve comparable performance to that of SRs and improve the diagnostic performance of JRs.

Yin Hao-Lin, Jiang Yu, Xu Zihan, Jia Hui-Hui, Lin Guang-Wu

2022-Jun-30

Breast MRI, Breast cancer, Deep learning, Neural network, Triple-negative breast cancer

Radiology Radiology

Non-invasive prediction of microsatellite instability in colorectal cancer by a genetic algorithm-enhanced artificial neural network-based CT radiomics signature.

In European radiology ; h5-index 62.0

OBJECTIVE : The stratification of microsatellite instability (MSI) status assists clinicians in making treatment decisions for colorectal cancer (CRC) patients. This study aimed to establish a CT-based radiomics signature to predict MSI status in patients with CRC.

METHODS : A total of 837 CRC patients who underwent preoperative enhanced CT and had available MSI status data were recruited from two hospitals. Radiomics features were extracted from segmented tumours, and a series of data balancing and feature selection strategies were used to select MSI-related features. Finally, an MSI-related radiomics signature was constructed using a genetic algorithm-enhanced artificial neural network model. Combined and clinical models were constructed using multivariate logistic regression analyses by integrating the clinical factors with or without the signature. A Kaplan-Meier survival analysis was conducted to explore the prognostic information of the signature in patients with CRC.

RESULTS : Ten features were selected to construct a signature which showed robust performance in both the internal and external validation cohorts, with areas under the curves (AUC) of 0.788 and 0.775, respectively. The performance of the signature was comparable to that of the combined model (AUCs of 0.777 and 0.767, respectively) and it outperformed the clinical model constituting age and tumour location (AUCs of 0.768 and 0.623, respectively). Survival analysis demonstrated that the signature could stratify patients with stage II CRC according to prognosis (HR: 0.402, p = 0.029).

CONCLUSIONS : This study built a robust radiomics signature for identifying the MSI status of CRC patients, which may assist individualised treatment decisions.

KEY POINTS : • Our well-designed modelling strategies helped overcome the problem of data imbalance caused by the low incidence of MSI. • Genetic algorithm-enhanced artificial neural network-based CT radiomics signature can effectively distinguish the MSI status of CRC patients. • Kaplan-Meier survival analysis demonstrated that our signature could significantly stratify stage II CRC patients into high- and low-risk groups.

Chen Xiaobo, He Lan, Li Qingshu, Liu Liu, Li Suyun, Zhang Yuan, Liu Zaiyi, Huang Yanqi, Mao Yun, Chen Xin

2022-Jun-30

Colorectal neoplasms, Microsatellite instability, Neural networks, Survival analysis

General General

Data-Driven Simulation of Fisher-Kolmogorov Tumor Growth Models Using Dynamic Mode Decomposition.

In Journal of biomechanical engineering ; h5-index 32.0

The computer simulation of organ-scale biomechanistic models of cancer personalized via routinely collected clinical and imaging data enables to obtain patient-specific predictions of tumor growth and treatment response over the anatomy of the patient's affected organ. However, the simulation of the underlying spatiotemporal models can entail a prohibitive computational cost, which constitutes a barrier to the successful development of clinically-actionable computational technologies for personalized tumor forecasting. Here we propose to utilize Dynamic-Mode Decomposition (DMD), an unsupervised machine learning method, to construct a low-dimensional representation of cancer models and accelerate their simulation. We show that DMD may be applied to Fisher-Kolmogorov models, which constitute an established formulation to represent untreated solid tumor growth that can further accommodate other relevant cancer phenomena. Our results show that a DMD implementation of this model over a clinically-relevant parameter space can yield impressive predictions, with short to medium-term errors remaining under 1% and long-term errors remaining under 20%, despite very short training periods. In particular, we have found that, for moderate to high tumor cell diffusivity and low to moderate tumor cell proliferation rate, DMD reconstructions provide accurate, bounded-error reconstructions for all tested training periods. We posit that this data-driven approach has the potential to greatly reduce the computational overhead of personalized simulations of cancer models, thereby facilitating tumor forecasting, parameter identification, uncertainty quantification, and treatment optimization.

Viguerie Alex, Grave Malú, Barros Gabriel F, Lorenzo Guillermo, Reali Alessandro, Coutinho Alvaro

2022-Jun-30

Pathology Pathology

Diagnostic Performance of Machine Learning Models Based on 18F-FDG PET/CT Radiomic Features in the Classification of Solitary Pulmonary Nodules.

In Molecular imaging and radionuclide therapy

Objectives : This study aimed to evaluate the ability of 18fluorine-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) radiomic features combined with machine learning methods to distinguish between benign and malignant solitary pulmonary nodules (SPN).

Methods : Data of 48 patients with SPN detected on 18F-FDG PET/CT scan were evaluated retrospectively. The texture feature extraction from PET/CT images was performed using an open-source application (LIFEx). Deep learning and classical machine learning algorithms were used to build the models. Final diagnosis was confirmed by pathology and follow-up was accepted as the reference. The performances of the models were assessed by the following metrics: Sensitivity, specificity, accuracy, and area under the receiver operator characteristic curve (AUC).

Results : The predictive models provided reasonable performance for the differential diagnosis of SPNs (AUCs ~0.81). The accuracy and AUC of the radiomic models were similar to the visual interpretation. However, when compared to the conventional evaluation, the sensitivity of the deep learning model (88% vs. 83%) and specificity of the classic learning model were higher (86% vs. 79%).

Conclusion : Machine learning based on 18F-FDG PET/CT texture features can contribute to the conventional evaluation to distinguish between benign and malignant lung nodules.

Salihoğlu Yavuz Sami, Uslu Erdemir Rabiye, Aydur Püren Büşra, Özdemir Semra, Uyulan Çağlar, Ergüzel Türker Tekin, Tekin Hüseyin Ozan

2022-Jun-27

PET/CT, Solitary pulmonary nodule, machine learning, radiomic

General General

Transfreq: A Python package for computing the theta-to-alpha transition frequency from resting state electroencephalographic data.

In Human brain mapping

A classic approach to estimate individual theta-to-alpha transition frequency (TF) requires two electroencephalographic (EEG) recordings, one acquired in a resting state condition and one showing alpha desynchronisation due, for example, to task execution. This translates into long recording sessions that may be cumbersome in studies involving patients. Moreover, an incomplete desynchronisation of the alpha rhythm may compromise TF estimates. Here we present transfreq, a publicly available Python library that allows TF computation from resting state data by clustering the spectral profiles associated to the EEG channels based on their content in alpha and theta bands. A detailed overview of transfreq core algorithm and software architecture is provided. Its effectiveness and robustness across different experimental setups are demonstrated on a publicly available EEG data set and on in-house recordings, including scenarios where the classic approach fails to estimate TF. We conclude with a proof of concept of the predictive power of transfreq TF as a clinical marker. Specifically, we present a scenario where transfreq TF shows a stronger correlation with the mini mental state examination score than other widely used EEG features, including individual alpha peak and median/mean frequency. The documentation of transfreq and the codes for reproducing the analysis of the article with the open-source data set are available online at https://elisabettavallarino.github.io/transfreq/. Motivated by the results showed in this article, we believe our method will provide a robust tool for discovering markers of neurodegenerative diseases.

Vallarino Elisabetta, Sommariva Sara, Famà Francesco, Piana Michele, Nobili Flavio, Arnaldi Dario

2022-Jun-30

clustering, machine learning, neurodegenerative diseases, power spectrum, quantitative EEG, transition frequency

General General

Deep Learning for FAST Quality Assessment.

In Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine

OBJECTIVES : To determine the feasibility of using a deep learning (DL) algorithm to assess the quality of focused assessment with sonography in trauma (FAST) exams.

METHODS : Our dataset consists of 441 FAST exams, classified as good-quality or poor-quality, with 3161 videos. We first used convolutional neural networks (CNNs), pretrained on the Imagenet dataset and fine-tuned on the FAST dataset. Second, we trained a CNN autoencoder to compress FAST images, with a 20-1 compression ratio. The compressed codes were input to a two-layer classifier network. To train the networks, each video was labeled with the quality of the exam, and the frames were labeled with the quality of the video. For inference, a video was classified as poor-quality if half the frames were classified as poor-quality by the network, and an exam was classified as poor-quality if half the videos were classified as poor-quality.

RESULTS : The results with the encoder-classifier networks were much better than the transfer learning results with CNNs. This was primarily because the Imagenet dataset is not a good match for the ultrasound quality assessment problem. The DL models produced video sensitivities and specificities of 99% and 98% on held-out test sets.

CONCLUSIONS : Using an autoencoder to compress FAST images is a very effective way to obtain features that can be used to predict exam quality. These features are more suitable than those obtained from CNNs pretrained on Imagenet.

Taye Mesfin, Morrow Dustin, Cull John, Smith Dane Hudson, Hagan Martin

2022-Jun-30

FAST, autoencoder, convolutional neural network, deep learning, ultrasound

General General

A novel end-to-end deep learning solution for automatic coronary artery segmentation from CCTA.

In Medical physics ; h5-index 59.0

PURPOSE : Coronary computed tomographic angiography (CCTA) plays a vital role in the diagnosis of cardiovascular diseases, among which automatic coronary artery segmentation (CAS) serves as one of the most challenging tasks. To computationally assist the task, this paper proposes a novel end-to-end deep learning-based (DL) solution for automatic CAS.

METHODS : Inspired by the Di-Vnet network, a fully automatic multistage DL solution is proposed. The new solution aims to preserve the integrity of blood vessels in terms of both their shape details and continuity. The solution is developed using 338 CCTA cases, among which 133 cases (33865 axial images) have their ground-truth cardiac masks pre-annotated and 205 cases (53365 axial images) have their ground-truth coronary artery (CA) masks pre-annotated. The solution's accuracy is measured using dice similarity coefficient (DSC), 95th percentile Hausdorff Distance (95% HD), Recall, and Precision scores for CAS.

RESULTS : The proposed solution attains 90.29% in DSC, 2.11 mm in 95% HD, 97.02% in Recall, and 92.17% in Precision, respectively, which consumes 0.112 seconds per image and 30 seconds per case on average. Such performance of our method is superior to other state-of-the-art segmentation methods.

CONCLUSIONS : The novel DL solution is able to automatically learn to perform CAS in an end-to-end fashion, attaining a high accuracy, efficiency and robustness simultaneously. This article is protected by copyright. All rights reserved.

Dong Caixia, Xu Songhua, Li Zongfang

2022-Jun-30

blood vessel integrity, coronary artery segmentation, deep learning

General General

Combining dynamic Monte Carlo with machine learning to study nanoparticle translocation.

In Soft matter

Resistive pulse sensing (RPS) measurements of nanoparticle translocation have the ability to provide information on single-particle level characteristics, such as diameter or mobility, as well as ensemble averages. However, interpreting these measurements is complex and requires an understanding of nanoparticle dynamics in confined spaces as well as the ways in which nanoparticles disrupt ion transport while inside a nanopore. Here, we combine Dynamic Monte Carlo (DMC) simulations with Machine Learning (ML) and Poisson-Nernst-Planck calculations to simultaneously simulate nanoparticle dynamics and ion transport during hundreds of independent particle translocations as a function of nanoparticle size, electrophoretic mobility, and nanopore length. The use of DMC simulations allowed us to explicitly investigate the effects of Brownian motion and nanoparticle/nanopore characteristics on the amplitude and duration of translocation signals. Simulation results were verified with experimental RPS measurements and found to be in quantitative agreement.

Vieira Luiz Fernando, Weinhofer Alexandra C, Oltjen William C, Yu Cindy, de Souza Mendes Paulo Roberto, Hore Michael J A

2022-Jun-30

General General

Artificial intelligence in gastrointestinal endoscopy: evolution to a new era.

In Revista espanola de enfermedades digestivas : organo oficial de la Sociedad Espanola de Patologia Digestiva

Artificial intelligence (AI) systems based on machine learning have evolved in the last few years with an increasing applicability in gastrointestinal endoscopy. Thanks to AI, an image (input) can be transformed into a clinical decision (output). Although AI systems have been initially studied to improve detection (CADe) and characterization of colorectal lesions (CADx), other indications are being currently investigated as detection of blind spots, scope guidance, or delineation/measurement of lesions. The objective of these review is to summarize the current evidence on applicability of AI systems in gastrointestinal endoscopy, highlight strengths and limitations of the technology and review regulatory and ethical aspects for its general implementation in gastrointestinal endoscopy.

Ortiz Zúñiga Oswaldo, Fernández Esparrach María Glòria, Daca María, Pellisé María

2022-Jun-30

General General

Structure-based virtual screening for PDL1 dimerizers: Evaluating generic scoring functions.

In Current research in structural biology

The interaction between PD1 and its ligand PDL1 has been shown to render tumor cells resistant to apoptosis and promote tumor progression. An innovative mechanism to inhibit the PD1/PDL1 interaction is PDL1 dimerization induced by small-molecule PDL1 binders. Structure-based virtual screening is a promising approach to discovering such small-molecule PD1/PDL1 inhibitors. Here we investigate which type of generic scoring functions is most suitable to tackle this problem. We consider CNN-Score, an ensemble of convolutional neural networks, as the representative of machine-learning scoring functions. We also evaluate Smina, a commonly used classical scoring function, and IFP, a top structural fingerprint similarity scoring function. These three types of scoring functions were evaluated on two test sets sharing the same set of small-molecule PD1/PDL1 inhibitors, but using different types of inactives: either true inactives (molecules with no in vitro PD1/PDL1 inhibition activity) or assumed inactives (property-matched decoy molecules generated from each active). On both test sets, CNN-Score performed much better than Smina, which in turn strongly outperformed IFP. The fact that the latter was the case, despite precluding any possibility of exploiting decoy bias, demonstrates the predictive value of CNN-Score for PDL1. These results suggest that re-scoring Smina-docked molecules with CNN-Score is a promising structure-based virtual screening method to discover new small-molecule inhibitors of this therapeutic target.

Tran-Nguyen Viet-Khoa, Simeon Saw, Junaid Muhammad, Ballester Pedro J

2022

General General

Effect of Homomorphic Encryption on the Performance of Training Federated Learning Generative Adversarial Networks

ArXiv Preprint

A Generative Adversarial Network (GAN) is a deep-learning generative model in the field of Machine Learning (ML) that involves training two Neural Networks (NN) using a sizable data set. In certain fields, such as medicine, the training data may be hospital patient records that are stored across different hospitals. The classic centralized approach would involve sending the data to a centralized server where the model would be trained. However, that would involve breaching the privacy and confidentiality of the patients and their data, which would be unacceptable. Therefore, Federated Learning (FL), an ML technique that trains ML models in a distributed setting without data ever leaving the host device, would be a better alternative to the centralized option. In this ML technique, only parameters and certain metadata would be communicated. In spite of that, there still exist attacks that can infer user data using the parameters and metadata. A fully privacy-preserving solution involves homomorphically encrypting (HE) the data communicated. This paper will focus on the performance loss of training an FL-GAN with three different types of Homomorphic Encryption: Partial Homomorphic Encryption (PHE), Somewhat Homomorphic Encryption (SHE), and Fully Homomorphic Encryption (FHE). We will also test the performance loss of Multi-Party Computations (MPC), as it has homomorphic properties. The performances will be compared to the performance of training an FL-GAN without encryption as well. Our experiments show that the more complex the encryption method is, the longer it takes, with the extra time taken for HE is quite significant in comparison to the base case of FL.

Ignjat Pejic, Rui Wang, Kaitai Liang

2022-07-01

General General

Can artificial intelligence increase the efficiency in referrals from primary to specialized care?

In Revista espanola de enfermedades digestivas : organo oficial de la Sociedad Espanola de Patologia Digestiva

The overload of the current healthcare model makes the search for strategies to improve process efficiency essential. An artificial intelligence (AI) program based on Natural Language Processing pipelines (1-5) was used. It analyzed the referrals from primary care to Gastroenterology in the health area corresponding to our hospital in order to identify the most frequent reasons for consultation and to assign them a protocol for the performance of complementary tests before being seen for the first time in specialized care. We compared all referrals received in the first half of 2018, prior to the implementation of the AI pathway July 2018, with those received in the first half of 2019. Our aim was to evaluate the efficiency of this program in terms of discharges, need for additional tests and the number of follow-up visits required (number of follow-up visits/number of first visits in a given time period, FU/F index). In 2018, 1799 referrals were received, 1309 within our health area and 490 from outside the area. In 2019, 2261 referrals were received, 1392 from our area and 869 out-of-area. The AI pathway was applied to 31.4% of the area-referred patients. Overall, in 2019, the number of blood tests and CT scans requested at the first visit decreased (55.3 vs 61.4% and 4.4 vs 7.4% respectively, p<0.05 for both comparisons). The FU/F index in 2019 was 1.9 ± 0.04 vs 2.26 ± 0.07 in 2018 (p<0.05). When analysing patients from our health area, a higher number of discharges at the first consultation was observed during 2019 The number of requested supplementary exams among patients referred using the AI pathway was reduced compared to 2018. The FU/F index in patients referred using the AI pathway was 1.72 ± 0.08 vs 2.25 ± 0.08 in 2018 (p<0.05) and 1.93 ± 0.07 in those referred through the standard pathway in 2019 (p=0.07). Among patients referred from outside our health area, the number of endoscopies requested in 2019 was higher. The FU/F index improved in 2019 (1.95 ± 0.06 vs 2.29 ± 0.13, p<0.05). The number of patients referred using the AI pathway remains low, which could explain the lack of differences observed in the number of discharges or tests requested compared to patients referred via the standard pathway. However, the number of endoscopies and follow up visits requested for these patients did decrease.

Tejedor Marta, Herrero Antonio, Castresana Carlos, Mesón Raúl, Taracido Juan Carlos, Sánchez Marta, Delgado María

2022-Jun-30

General General

Genetics and Epigenetics in Personalized Nutrition: Evidence, Expectations and Experiences.

In Molecular nutrition & food research ; h5-index 56.0

With the presentation of the blueprint of the first human genome in 2001 and the advent of technologies for high-throughput genetic analysis, personalized nutrition (PN) became a new scientific field and the first commercial offerings of genotype-based nutrition advice emerged at the same time. Here, we summarize the state of evidence for the effect of genetic and epigenetic factors in the development of obesity, the metabolic syndrome and resulting illnesses such as non-insulin-dependent diabetes mellitus and cardiovascular diseases. We also critically value the concepts of PN that were built around the new genetic avenue from both the academic and a commercial perspective and their effectiveness in causing sustained changes in diet, lifestyle and for improving health. Despite almost 20 years of research and commercial direct-to-consumer offerings, evidence for the success of gene-based dietary recommendations is still generally lacking. This calls for new concepts of future PN solutions that incorporate more phenotypic measures and provide a panel of instruments (e.g., self- and bio-monitoring tools, feedback systems, algorithms based on artificial intelligence) that increases compliance based on the individual´s physical and social environment and value system. This article is protected by copyright. All rights reserved.

Holzapfel Christina, Waldenberger Melanie, Lorkowski Stefan, Daniel Hannelore

2022-Jun-29

Obesity, cardiovascular diseases, diabetes, methylation, single nucleotide polymorphism

Ophthalmology Ophthalmology

Investigation of the Role of Convolutional Neural Network Architectures in the Diagnosis of Glaucoma using Color Fundus Photography.

In Turkish journal of ophthalmology

Objectives : To evaluate the performance of convolutional neural network (CNN) architectures to distinguish eyes with glaucoma from normal eyes.

Materials and Methods : A total of 9,950 fundus photographs of 5,388 patients from the database of Eskişehir Osmangazi University Faculty of Medicine Ophthalmology Clinic were labelled as glaucoma, glaucoma suspect, or normal by three different experienced ophthalmologists. The categorized fundus photographs were evaluated using a state-of-the-art two-dimensional CNN and compared with deep residual networks (ResNet) and very deep neural networks (VGG). The accuracy, sensitivity, and specificity of glaucoma detection with the different algorithms were evaluated using a dataset of 238 normal and 320 glaucomatous fundus photographs. For the detection of suspected glaucoma, ResNet-101 architectures were tested with a data set of 170 normal, 170 glaucoma, and 167 glaucoma-suspect fundus photographs.

Results : Accuracy, sensitivity, and specificity in detecting glaucoma were 96.2%, 99.5%, and 93.7% with ResNet-50; 97.4%, 97.8%, and 97.1% with ResNet-101; 98.9%, 100%, and 98.1% with VGG-19, and 99.4%, 100%, and 99% with the 2D CNN, respectively. Accuracy, sensitivity, and specificity values in distinguishing glaucoma suspects from normal eyes were 62%, 68%, and 56% and those for differentiating glaucoma from suspected glaucoma were 92%, 81%, and 97%, respectively. While 55 photographs could be evaluated in 2 seconds with CNN, a clinician spent an average of 24.2 seconds to evaluate a single photograph.

Conclusion : An appropriately designed and trained CNN was able to distinguish glaucoma with high accuracy even with a small number of fundus photographs.

Atalay Eray, Özalp Onur, Devecioğlu Özer Can, Erdoğan Hakika, İnce Türker, Yıldırım Nilgün

2022-Jun-29

Glaucoma, artificial intelligence, convolutional neural network, telemedicine

General General

SASDL and RBATQ: Sparse Autoencoder With Swarm Based Deep Learning and Reinforcement Based Q-Learning for EEG Classification.

In IEEE open journal of engineering in medicine and biology

The most vital information about the electrical activities of the brain can be obtained with the help of Electroencephalography (EEG) signals. It is quite a powerful tool to analyze the neural activities of the brain and various neurological disorders like epilepsy, schizophrenia, sleep related disorders, parkinson disease etc. can be investigated well with the help of EEG signals. Goal: In this paper, two versatile deep learning methods are proposed for the efficient classification of epilepsy and schizophrenia from EEG datasets. Methods: The main advantage of using deep learning when compared to other machine learning algorithms is that it has the capability to accomplish feature engineering on its own. Swarm intelligence is also a highly useful technique to solve a wide range of real-world, complex, and non-linear problems. Therefore, taking advantage of these factors, the first method proposed is a Sparse Autoencoder (SAE) with swarm based deep learning method and it is named as (SASDL) using Particle Swarm Optimization (PSO) technique, Cuckoo Search Optimization (CSO) technique and Bat Algorithm (BA) technique; and the second technique proposed is the Reinforcement Learning based on Bidirectional Long-Short Term Memory (BiLSTM), Attention Mechanism, Tree LSTM and Q learning, and it is named as (RBATQ) technique. Results and Conclusions: Both these two novel deep learning techniques are tested on epilepsy and schizophrenia EEG datasets and the results are analyzed comprehensively, and a good classification accuracy of more than 93% is obtained for all the datasets.

Prabhakar Sunil Kumar, Lee Seong-Whan

2022

Deep learning, EEG, PSO, Q-learning, reinforcement learning

oncology Oncology

Pursuing Connectivity in Cardio-Oncology Care-The Future of Telemedicine and Artificial Intelligence in Providing Equity and Access to Rural Communities.

In Frontiers in cardiovascular medicine

The aim of this review is to discuss the current health disparities in rural communities and to explore the potential role of telehealth and artificial intelligence in providing cardio-oncology care to underserviced communities. With advancements in early detection and cancer treatment, survivorship has increased. The interplay between cancer and cardiovascular disease, which are the leading causes of morbidity and mortality in this population, has been increasingly recognized. Worldwide, cardio-oncology clinics (COCs) have emerged to deliver a multidisciplinary approach to the care of patients with cancer to mitigate cardiovascular risks while minimizing interruptions in cancer treatment. Despite the value of COCs, the accessibility gap between urban and rural communities in both oncology and cardio-oncology contributes to health care disparities and may be an underrecognized determinant of health globally. Telehealth and artificial intelligence offer opportunities to provide timely care irrespective of rurality. We therefore explore current developments within this sphere and propose a novel model of care to address the disparity in urban vs. rural cardio-oncology using the experience in Canada, a geographically large country with many rural communities.

Kappel Coralea, Rushton-Marovac Moira, Leong Darryl, Dent Susan

2022

artificial intelligence, cardio-oncology, care delivery model, innovation, telehealth

General General

Corrigendum: Diagnostic Accuracy and Generalizability of a Deep Learning-Based Fully Automated Algorithm for Coronary Artery Stenosis Detection on CCTA: A Multi-Centre Registry Study.

In Frontiers in cardiovascular medicine

[This corrects the article DOI: 10.3389/fcvm.2021.707508.].

Xu Lixue, He Yi, Luo Nan, Guo Ning, Hong Min, Jia Xibin, Wang Zhenchang, Yang Zhenghan

2022

computed tomographic angiography, coronary artery disease, deep learning, diagnostic test, invasive coronary angiography (ICA)

General General

Assessment of Blood Pressure Using Only a Smartphone and Machine Learning Techniques: A Systematic Review.

In Frontiers in cardiovascular medicine

Regular monitoring of blood pressure (BP) allows for early detection of hypertension and symptoms related to cardiovascular disease. Measuring BP with a cuff requires equipment that is not always readily available and it may be impractical for some patients. Smartphones are an integral part of the lives of most people; thus, detecting and monitoring hypertension with a smartphone is likely to increase the ability to monitor BP due to the convenience of use for many patients. Smartphones lend themselves to assessing cardiovascular health because their built-in sensors and cameras provide a means of detecting arterial pulsations. To this end, several image processing and machine learning (ML) techniques for predicting BP using a smartphone have been developed. Several ML models that utilize smartphones are discussed in this literature review. Of the 53 papers identified, seven publications were evaluated. The performance of the ML models was assessed based on their accuracy for classification, the mean error measure, and the standard deviation of error for regression. It was found that artificial neural networks and support vector machines were often used. Because a variety of influencing factors determines the performance of an ML model, no clear preference could be determined. The number of input features ranged from five to 233, with the most commonly used being demographic data and the features extracted from photoplethysmogram signals. Each study had a different number of participants, ranging from 17 to 5,992. Comparisons of the cuff-based measures were mostly used to validate the results. Some of these ML models are already used to detect hypertension and BP but, to satisfy possible regulatory demands, improved reliability is needed under a wider range of conditions, including controlled and uncontrolled environments. A discussion of the advantages of various ML techniques and the selected features is offered at the end of this systematic review.

Haugg Fridolin, Elgendi Mohamed, Menon Carlo

2022

blood pressure, digital health, hypertension, machine learning, smartphone

Cardiology Cardiology

Artificial Intelligence-Enabled Electrocardiography Detects Hypoalbuminemia and Identifies the Mechanism of Hepatorenal and Cardiovascular Events.

In Frontiers in cardiovascular medicine

Background : Albumin, an important component of fluid balance, is associated with kidney, liver, nutritional, and cardiovascular diseases (CVD) and is measured by blood tests. Since fluid balance is associated with electrocardiography (ECG) changes, we established a deep learning model (DLM) to estimate albumin via ECG.

Objective : This study aimed to develop a DLM to estimate albumin via ECG and explored its contribution to future complications.

Materials and Methods : A DLM was trained for estimating ECG-based albumin (ECG-Alb) using 155,078 ECGs corresponding to albumin from 79,111 patients, and another independent 13,335 patients from an academic medical center and 11,370 patients from a community hospital were used for internal and external validation. The primary analysis focused on distinguishing patients with mild to severe hypoalbuminemia, and the secondary analysis aimed to provide additional prognostic value from ECG-Alb for future complications, which included mortality, new-onset hypoalbuminemia, chronic kidney disease (CKD), new onset hepatitis, CVD mortality, new-onset acute myocardial infarction (AMI), new-onset stroke (STK), new-onset coronary artery disease (CAD), new-onset heart failure (HF), and new-onset atrial fibrillation (Afib).

Results : The AUC to identify hypoalbuminemia was 0.8771 with a sensitivity of 56.0% and a specificity of 90.7% in the internal validation set, and the Pearson correlation coefficient was 0.69 in the continuous analysis. The most important ECG features contributing to ECG-Alb were ordered in terms of heart rate, corrected QT interval, T wave axis, sinus rhythm, P wave axis, etc. The group with severely low ECG-Alb had a higher risk of all-cause mortality [hazard ratio (HR): 2.45, 95% CI: 1.81-3.33] and the other hepatorenal and cardiovascular events in the internal validation set. The external validation set yielded similar results.

Conclusion : Hypoalbuminemia and its complications can be predicted using ECG-Alb as a novel biomarker, which may be a non-invasive tool to warn asymptomatic patients.

Lee Yung-Tsai, Lin Chin-Sheng, Fang Wen-Hui, Lee Chia-Cheng, Ho Ching-Liang, Wang Chih-Hung, Tsai Dung-Jang, Lin Chin

2022

artificial intelligence, deep learning, electrocardiogram, hypoalbuminemia, liver failure events, previvor

General General

Short-Term Nationwide Airport Throughput Prediction With Graph Attention Recurrent Neural Network.

In Frontiers in artificial intelligence

With the dynamic air traffic demand and the constrained capacity resources, accurately predicting airport throughput is essential to ensure the efficiency and resilience of air traffic operations. Many research efforts have been made to predict traffic throughputs or flight delays at an airport or over a network. However, it is still a challenging problem due to the complex spatiotemporal dynamics of the highly interacted air transportation systems. To address this challenge, we propose a novel deep learning model, graph attention neural network stacking with a Long short-term memory unit (GAT-LSTM), to predict the short-term airport throughput over a national air traffic network. LSTM layers are included to extract the temporal correlations in the data, while the graph attention mechanism is used to capture the spatial dependencies. For the graph attention mechanism, two graph modeling methods, airport-based graph and OD-pair graph are explored in this study. We tested the proposed model using real-world air traffic data involving 65 major airports in China over 3 months in 2017 and compared its performance with other state-of-the-art models. Results showed that the temporal pattern was the dominate factor, compared to the spatial pattern, in predicting airport throughputs over an air traffic network. Among the prediction models that we compared, both the proposed model and LSTM performed well on prediction accuracy over the entire network. Better performance of the proposed model was observed when focusing on airports with larger throughputs. We also conducted an analysis on model interpretability. We found that spatiotemporal correlations in the data were learned and shown via the model parameters, which helped us to gain insights into the topology and the dynamics of the air traffic network.

Zhu Xinting, Lin Yu, He Yuxin, Tsui Kwok-Leung, Chan Pak Wai, Li Lishuai

2022

air traffic network, airport network, complex network, deep learning, graph neural network, throughput prediction

General General

You Can't Have AI Both Ways: Balancing Health Data Privacy and Access Fairly.

In Frontiers in genetics ; h5-index 62.0

Artificial intelligence (AI) in healthcare promises to make healthcare safer, more accurate, and more cost-effective. Public and private actors have been investing significant amounts of resources into the field. However, to benefit from data-intensive medicine, particularly from AI technologies, one must first and foremost have access to data. It has been previously argued that the conventionally used "consent or anonymize approach" undermines data-intensive medicine, and worse, may ultimately harm patients. Yet, this is still a dominant approach in European countries and framed as an either-or choice. In this paper, we contrast the different data governance approaches in the EU and their advantages and disadvantages in the context of healthcare AI. We detail the ethical trade-offs inherent to data-intensive medicine, particularly the balancing of data privacy and data access, and the subsequent prioritization between AI and other effective health interventions. If countries wish to allocate resources to AI, they also need to make corresponding efforts to improve (secure) data access. We conclude that it is unethical to invest significant amounts of public funds into AI development whilst at the same time limiting data access through strict privacy measures, as this constitutes a waste of public resources. The "AI revolution" in healthcare can only realise its full potential if a fair, inclusive engagement process spells out the values underlying (trans) national data governance policies and their impact on AI development, and priorities are set accordingly.

Bak Marieke, Madai Vince Istvan, Fritzsche Marie-Christine, Mayrhofer Michaela Th, McLennan Stuart

2022

artificial intelligence, data access, data privacy, digital health, ethics, fairness, resource allocation

oncology Oncology

Pan-Cancer Detection and Typing by Mining Patterns in Large Genome-Wide Cell-Free DNA Sequencing Datasets.

In Clinical chemistry ; h5-index 61.0

BACKGROUND : Cell-free DNA (cfDNA) analysis holds great promise for non-invasive cancer screening, diagnosis, and monitoring. We hypothesized that mining the patterns of cfDNA shallow whole-genome sequencing datasets from patients with cancer could improve cancer detection.

METHODS : By applying unsupervised clustering and supervised machine learning on large cfDNA shallow whole-genome sequencing datasets from healthy individuals (n = 367) and patients with different hematological (n = 238) and solid malignancies (n = 320), we identified cfDNA signatures that enabled cancer detection and typing.

RESULTS : Unsupervised clustering revealed cancer type-specific sub-grouping. Classification using a supervised machine learning model yielded accuracies of 96% and 65% in discriminating hematological and solid malignancies from healthy controls, respectively. The accuracy of disease type prediction was 85% and 70% for the hematological and solid cancers, respectively. The potential utility of managing a specific cancer was demonstrated by classifying benign from invasive and borderline adnexal masses with an area under the curve of 0.87 and 0.74, respectively.

CONCLUSIONS : This approach provides a generic analytical strategy for non-invasive pan-cancer detection and cancer type prediction.

Che Huiwen, Jatsenko Tatjana, Lenaerts Liesbeth, Dehaspe Luc, Vancoillie Leen, Brison Nathalie, Parijs Ilse, Van Den Bogaert Kris, Fischerova Daniela, Heremans Ruben, Landolfo Chiara, Testa Antonia Carla, Vanderstichele Adriaan, Liekens Lore, Pomella Valentina, Wozniak Agnieszka, Dooms Christophe, Wauters Els, Hatse Sigrid, Punie Kevin, Neven Patrick, Wildiers Hans, Tejpar Sabine, Lambrechts Diether, Coosemans An, Timmerman Dirk, Vandenberghe Peter, Amant Frédéric, Vermeesch Joris Robert

2022-Jun-30

cfDNA, ctDNA, hematological malignancies, liquid biopsy, machine learning, ovarian tumors, solid tumors

Pathology Pathology

How can spherical CNNs benefit ML-based diffusion MRI parameter estimation?

ArXiv Preprint

This paper demonstrates spherical convolutional neural networks (S-CNN) offer distinct advantages over conventional fully-connected networks (FCN) at estimating scalar parameters of tissue microstructure from diffusion MRI (dMRI). Such microstructure parameters are valuable for identifying pathology and quantifying its extent. However, current clinical practice commonly acquires dMRI data consisting of only 6 diffusion weighted images (DWIs), limiting the accuracy and precision of estimated microstructure indices. Machine learning (ML) has been proposed to address this challenge. However, existing ML-based methods are not robust to differing dMRI gradient sampling schemes, nor are they rotation equivariant. Lack of robustness to sampling schemes requires a new network to be trained for each scheme, complicating the analysis of data from multiple sources. A possible consequence of the lack of rotational equivariance is that the training dataset must contain a diverse range of microstucture orientations. Here, we show spherical CNNs represent a compelling alternative that is robust to new sampling schemes as well as offering rotational equivariance. We show the latter can be leveraged to decrease the number of training datapoints required.

Tobias Goodwin-Allcock, Jason McEwen, Robert Gray, Parashkev Nachev, Hui Zhang

2022-07-01

General General

Uncertainty quantification in variable selection for genetic fine-mapping using bayesian neural networks.

In iScience

In this paper, we propose a new approach for variable selection using a collection of Bayesian neural networks with a focus on quantifying uncertainty over which variables are selected. Motivated by fine-mapping applications in statistical genetics, we refer to our framework as an "ensemble of single-effect neural networks" (ESNN) which generalizes the "sum of single effects" regression framework by both accounting for nonlinear structure in genotypic data (e.g., dominance effects) and having the capability to model discrete phenotypes (e.g., case-control studies). Through extensive simulations, we demonstrate our method's ability to produce calibrated posterior summaries such as credible sets and posterior inclusion probabilities, particularly for traits with genetic architectures that have significant proportions of non-additive variation driven by correlated variants. Lastly, we use real data to demonstrate that the ESNN framework improves upon the state of the art for identifying true effect variables underlying various complex traits.

Cheng Wei, Ramachandran Sohini, Crawford Lorin

2022-Jul-15

Artificial intelligence, Bioinformatics, Genetics

General General

Integrating Multiculturalism Into Artificial Intelligence-Assisted Programming Lessons: Examining Inter-Ethnicity Differences in Learning Expectancy, Motivation, and Effectiveness.

In Frontiers in psychology ; h5-index 92.0

Given the current popularization of computer programming and the trends of informatization and digitization, colleges have actively responded by making programming lessons compulsory for students of all disciplines. However, students from different ethnic groups often have different learning responses to such lessons due to their respective cultural backgrounds, the environment in which they grew up, and their consideration for future employment. In this study, an AI-assisted programming module was developed and used to compare the differences between multi-ethnic college students in terms of their theoretical and actual learning expectancy, motivation, and effectiveness. The module conducted analysis through the deep learning network and examined the relevant processes that the students underwent during programming lessons, as well as the types of errors they had committed. Their learning motivation for and actual learning performance in programming were then examined based on the cognitive learning theory. The results of the experiment, which involved 96 multi-ethnic college students, indicated that the two groups had dissimilar theoretical performance in terms of their expectancy and motivation for learning programming. The indigenous students' main concern was whether programming would affect their families or tribes, and this concern affected and was reflected in their learning outcomes. In contrast, the learning motivation and goals of Han Chinese students were driven by the cognition of the value of programming to themselves. The research findings can contribute toward the cognition and understanding of multi-ethnic students when learning computer programming and development of the appropriate teaching methods, and serve as a reference for subsequent research on integrating multiculturalism into computer programming lessons.

Tsai Chia-Wei, Ma Yi-Wei, Chang Yao-Chung, Lai Ying-Hsun

2022

AI-assisted program, expectancy-value theory, learning effectiveness, multiculturalism, program education

General General

Employing Natural Language Processing as Artificial Intelligence for Analyzing Consumer Opinion Toward Advertisement.

In Frontiers in psychology ; h5-index 92.0

With the advent and integration of technology in business, marketers started investing in numerous media platforms to influence the consumer's sentiments. Artificial intelligence has been proved as one of the innovative tools of digitalization to change consumer's media habits. Owing to the growing trends of e-commerce, the traditional advertising model is insufficient. Therefore, advertisers are taking advantage of artificial intelligence technology to meet current requirements. Thus, a deeper understanding of product advertisement with reference to consumer sentiments and its implications need to be established. The current research depicts the contribution of artificial intelligence to analyze the consumers' attention, cognition, and emotion. The target product was Samsung Galaxy. Researcher of the current study has employed Think-aloud procedure for data analysis. Tweets dataset was divided into 2 categories. For international consumers' sentiments 30,877 tweets whereas for Pakistani consumers' sentiments tweets dataset was 26,834. For data analysis, authors used Nvivo for generating theme. The Nvivo produced word cloud. The word cloud generated with Pakistani tweets revealed that consumer attachment with Samsung product is based on emotional and attention and the preferred features of Samsung products are linked with emotional and attention. In contrary to that rest of the world tweets unfolded that emotion, attention, and cognition make consumer preferences while selecting Samsung products. This study is useful to the cellular companies for targeting across the world population. The consumer preference varies while selecting cell phones. This study will provide a better idea to cell phone companies for manufacturing consumer oriented cell phones to get better results. Moreover, future research should add more countries separate data and generate a comparative study between developed countries consumer and developing countries consumer preferences. In addition to companies with better insights of consumer can highlight the most attractive features of cell phone in their advertisements.

Sun Huilin, Zafar Muhammad Zeeshan, Hasan Naveed

2022

Pakistan, Think-aloud, attention, cognition, emotion, product advertisement, qualitative research, tweets

General General

Semiautomated analysis of an optical ATP indicator in neurons.

In Neurophotonics

Significance: The firefly enzyme luciferase has been used in a wide range of biological assays, including bioluminescence imaging of adenosine triphosphate (ATP). The biosensor Syn-ATP utilizes subcellular targeting of luciferase to nerve terminals for optical measurement of ATP in this compartment. Manual analysis of Syn-ATP signals is challenging due to signal heterogeneity and cellular motion in long imaging sessions. Here, we have leveraged machine learning tools to develop a method for analysis of bioluminescence images. Aim: Our goal was to create a semiautomated pipeline for analysis of bioluminescence imaging to improve measurements of ATP content in nerve terminals. Approach: We developed an image analysis pipeline that applies machine learning toolkits to distinguish neurons from background signals and excludes neural cell bodies, while also incorporating user input. Results: Side-by-side comparison of manual and semiautomated image analysis demonstrated that the latter improves precision and accuracy of ATP measurements. Conclusions: Our method streamlines data analysis and reduces user-introduced bias, thus enhancing the reproducibility and reliability of quantitative ATP imaging in nerve terminals.

Dehkharghanian Taher, Hashemiaghdam Arsalan, Ashrafi Ghazaleh

2022-Oct

ATP, bioluminescence, image analysis, machine learning, nerve terminals

General General

Combined Channel Attention and Spatial Attention Module Network for Chinese Herbal Slices Automated Recognition.

In Frontiers in neuroscience ; h5-index 72.0

Chinese Herbal Slices (CHS) are critical components of Traditional Chinese Medicine (TCM); the accurate recognition of CHS is crucial for applying to medicine, production, and education. However, existing methods to recognize the CHS are mainly performed by experienced professionals, which may not meet vast CHS market demand due to time-consuming and the limited number of professionals. Although some automated CHS recognition approaches have been proposed, the performance still needs further improvement because they are primarily based on the traditional machine learning with hand-crafted features, resulting in relatively low accuracy. Additionally, few CHS datasets are available for research aimed at practical application. To comprehensively address these problems, we propose a combined channel attention and spatial attention module network (CCSM-Net) for efficiently recognizing CHS with 2-D images. The CCSM-Net integrates channel and spatial attentions, focusing on the most important information as well as the position of the information of CHS image. Especially, pairs of max-pooling and average pooling operations are used in the CA and SA module to aggregate the channel information of the feature map. Then, a dataset of 14,196 images with 182 categories of commonly used CHS is constructed. We evaluated our framework on the constructed dataset. Experimental results show that the proposed CCSM-Net indicates promising performance and outperforms other typical deep learning algorithms, achieving a recognition rate of 99.27%, a precision of 99.33%, a recall of 99.27%, and an F1-score of 99.26% with different numbers of CHS categories.

Wang Jianqing, Mo Weitao, Wu Yan, Xu Xiaomei, Li Yi, Ye Jianming, Lai Xiaobo

2022

artificial intelligence, automated recognition, computational intelligence, intelligent data analysis, spatial attention module

Surgery Surgery

A Deep Learning Model for Three-Dimensional Nystagmus Detection and Its Preliminary Application.

In Frontiers in neuroscience ; h5-index 72.0

Symptoms of vertigo are frequently reported and are usually accompanied by eye-movements called nystagmus. In this article, we designed a three-dimensional nystagmus recognition model and a benign paroxysmal positional vertigo automatic diagnosis system based on deep neural network architectures (Chinese Clinical Trials Registry ChiCTR-IOR-17010506). An object detection model was constructed to track the movement of the pupil centre. Convolutional neural network-based models were trained to detect nystagmus patterns in three dimensions. Our nystagmus detection models obtained high areas under the curve; 0.982 in horizontal tests, 0.893 in vertical tests, and 0.957 in torsional tests. Moreover, our automatic benign paroxysmal positional vertigo diagnosis system achieved a sensitivity of 0.8848, specificity of 0.8841, accuracy of 0.8845, and an F1 score of 0.8914. Compared with previous studies, our system provides a clinical reference, facilitates nystagmus detection and diagnosis, and it can be applied in real-world medical practices.

Lu Wen, Li Zhuangzhuang, Li Yini, Li Jie, Chen Zhengnong, Feng Yanmei, Wang Hui, Luo Qiong, Wang Yiqing, Pan Jun, Gu Lingyun, Yu Dongzhen, Zhang Yudong, Shi Haibo, Yin Shankai

2022

benign paroxysmal positional vertigo, deep learning, neural network, nystagmus detection, vertigo

General General

Pooling Operations in Deep Learning: From "Invariable" to "Variable".

In BioMed research international ; h5-index 102.0

Deep learning has become a research hotspot in multimedia, especially in the field of image processing. Pooling operation is an important operation in deep learning. Pooling operation can reduce the feature dimension, the number of parameters, the complexity of computation, and the complexity of time. With the development of deep learning models, pooling operation has made great progress. The main contributions of this paper on pooling operation are as follows: firstly, the steps of the pooling operation are summarized as the pooling domain, pooling kernel, step size, activation value, and response value. Secondly, the expression form of pooling operation is standardized. From the perspective of "invariable" to "variable," this paper analyzes the pooling domain and pooling kernel in the pooling operation. Pooling operation can be classified into four categories: invariable of pooling domain, variable of pooling domain, variable of pooling kernel, and the pooling of invariable "+" variable. Finally, the four types of pooling operation are summarized and discussed with their advantages and disadvantages. There is great significance to the research of pooling operations and the iterative updating of deep learning models.

Tao Zhou, XiaoYu Chang, HuiLing Lu, XinYu Ye, YunCan Liu, XiaoMin Zheng

2022

Radiology Radiology

A Multi-Atlas-Based [18F]9-Fluoropropyl-(+)-Dihydrotetrabenazine Positron Emission Tomography Image Segmentation Method for Parkinson's Disease Quantification.

In Frontiers in aging neuroscience ; h5-index 64.0

Objectives : [18F]9-fluoropropyl-(+)-dihydrotetrabenazine ([18F]-FP-DTBZ) positron emission tomography (PET) provides reliable information for the diagnosis of Parkinson's disease (PD). In this study, we proposed a multi-atlas-based [18F]-FP-DTBZ PET image segmentation method for PD quantification assessment.

Methods : A total of 99 subjects from Xuanwu Hospital of Capital Medical University were included in this study, and both brain PET and magnetic resonance (MR) scans were conducted. Data from 20 subjects were used to generate atlases, based on which a multi-atlas-based [18F]-FP-DTBZ PET segmentation method was developed especially for striatum and its subregions. The proposed method was compared with the template-based method through striatal subregion parcellation performance and the standard uptake value ratio (SUVR) quantification accuracy. Discriminant analysis between healthy controls (HCs) and PD patients was further performed.

Results : Segmentation results of the multi-atlas-based method showed better consistency than the template-based method with the ground truth, yielding a dice coefficient of 0.81 over 0.73 on the full striatum. The SUVRs calculated by the multi-atlas-based method had an average interclass correlation coefficient (ICC) of 0.953 with the standardized result, whereas the template-based method only reached 0.815. The SUVRs of HCs were generally higher than that of patients with PD and showed significant differences in all of the striatal subregions (all p < 0.001). The median and posterior putamen performed best in discriminating patients with PD from HCs.

Conclusion : The proposed multi-atlas-based [18F]-FP-DTBZ PET image segmentation method achieved better performance than the template-based method, indicating great potential in improving accuracy and efficiency for PD diagnosis in clinical routine.

Pan Yiwei, Liu Shuying, Zeng Yao, Ye Chenfei, Qiao Hongwen, Song Tianbing, Lv Haiyan, Chan Piu, Lu Jie, Ma Ting

2022

Parkinson’s disease, SUVR quantification, [18F]-FP-DTBZ, image segmentation, striatum subregion

Surgery Surgery

Single-Cell Sequencing Analysis and Multiple Machine Learning Methods Identified G0S2 and HPSE as Novel Biomarkers for Abdominal Aortic Aneurysm.

In Frontiers in immunology ; h5-index 100.0

Identifying biomarkers for abdominal aortic aneurysms (AAA) is key to understanding their pathogenesis, developing novel targeted therapeutics, and possibly improving patients outcomes and risk of rupture. Here, we identified AAA biomarkers from public databases using single-cell RNA-sequencing, weighted co-expression network (WGCNA), and differential expression analyses. Additionally, we used the multiple machine learning methods to identify biomarkers that differentiated large AAA from small AAA. Biomarkers were validated using GEO datasets. CIBERSORT was used to assess immune cell infiltration into AAA tissues and investigate the relationship between biomarkers and infiltrating immune cells. Therefore, 288 differentially expressed genes (DEGs) were screened for AAA and normal samples. The identified DEGs were mostly related to inflammatory responses, lipids, and atherosclerosis. For the large and small AAA samples, 17 DEGs, mostly related to necroptosis, were screened. As biomarkers for AAA, G0/G1 switch 2 (G0S2) (Area under the curve [AUC] = 0.861, 0.875, and 0.911, in GSE57691, GSE47472, and GSE7284, respectively) and for large AAA, heparinase (HPSE) (AUC = 0.669 and 0.754, in GSE57691 and GSE98278, respectively) were identified and further verified by qRT-PCR. Immune cell infiltration analysis revealed that the AAA process may be mediated by T follicular helper (Tfh) cells and the large AAA process may also be mediated by Tfh cells, M1, and M2 macrophages. Additionally, G0S2 expression was associated with neutrophils, activated and resting mast cells, M0 and M1 macrophages, regulatory T cells (Tregs), resting dendritic cells, and resting CD4 memory T cells. Moreover, HPSE expression was associated with M0 and M1 macrophages, activated and resting mast cells, Tregs, and resting CD4 memory T cells. Additional, G0S2 may be an effective diagnostic biomarker for AAA, whereas HPSE may be used to confer risk of rupture in large AAAs. Immune cells play a role in the onset and progression of AAA, which may improve its diagnosis and treatment.

Xiong Tao, Lv Xiao-Shuo, Wu Gu-Jie, Guo Yao-Xing, Liu Chang, Hou Fang-Xia, Wang Jun-Kui, Fu Yi-Fan, Liu Fu-Qiang

2022

abdominal aortic aneurysm, differentially expressed genes, multiple machine learning methods, single-cell sequencing, weighted co-expression network analysis

General General

A Whole Exon Screening-Based Score Model Predicts Prognosis and Immune Checkpoint Inhibitor Therapy Effects in Low-Grade Glioma.

In Frontiers in immunology ; h5-index 100.0

Objective : This study aims to identify prognostic factors for low-grade glioma (LGG) via different machine learning methods in the whole genome and to predict patient prognoses based on these factors. We verified the results through in vitro experiments to further screen new potential therapeutic targets.

Method : A total of 940 glioma patients from The Cancer Genome Atlas (TCGA) and The Chinese Glioma Genome Atlas (CGGA) were included in this study. Two different feature extraction algorithms - LASSO and Random Forest (RF) - were used to jointly screen genes significantly related to the prognosis of patients. The risk signature was constructed based on these screening genes, and the K-M curve and ROC curve evaluated it. Furthermore, we discussed the differences between the high- and low-risk groups distinguished by the signature in detail, including differential gene expression (DEG), single-nucleotide polymorphism (SNP), copy number variation (CNV), immune infiltration, and immune checkpoint. Finally, we identified the function of a novel molecule, METTL7B, which was highly correlated with PD-L1 expression on tumor cell, as verified by in vitro experiments.

Results : We constructed an accurate prediction model based on seven genes (AUC at 1, 3, 5 years= 0.91, 0.85, 0.74). Further analysis showed that extracellular matrix remodeling and cytokine and chemokine release were activated in the high-risk group. The proportion of multiple immune cell infiltration was upregulated, especially macrophages, accompanied by the high expression of most immune checkpoints. According to the in vitro experiment, we preliminarily speculate that METTL7B affects the stability of PD-L1 mRNA by participating in the modification of m6A.

Conclusion : The seven gene signatures we constructed can predict the prognosis of patients and identify the potential benefits of immune checkpoint inhibitors (ICI) therapy for LGG. More importantly, METTL7B, one of the risk genes, is a crucial molecule that regulates PD-L1 and could be used as a new potential therapeutic target.

Luo Cheng, Wang Songmao, Shan Wenjie, Liao Weijie, Zhang Shikuan, Wang Yanzhi, Xin Qilei, Yang Tingpeng, Hu Shaoliang, Xie Weidong, Xu Naihan, Zhang Yaou

2022

METTL7B, PD-L1, RNA stability, glioma, m6A (N6-methyladenose), prognosis prediction

General General

Accurate identification of bacteriophages from metagenomic data using Transformer.

In Briefings in bioinformatics

MOTIVATION : Bacteriophages are viruses infecting bacteria. Being key players in microbial communities, they can regulate the composition/function of microbiome by infecting their bacterial hosts and mediating gene transfer. Recently, metagenomic sequencing, which can sequence all genetic materials from various microbiome, has become a popular means for new phage discovery. However, accurate and comprehensive detection of phages from the metagenomic data remains difficult. High diversity/abundance, and limited reference genomes pose major challenges for recruiting phage fragments from metagenomic data. Existing alignment-based or learning-based models have either low recall or precision on metagenomic data.

RESULTS : In this work, we adopt the state-of-the-art language model, Transformer, to conduct contextual embedding for phage contigs. By constructing a protein-cluster vocabulary, we can feed both the protein composition and the proteins' positions from each contig into the Transformer. The Transformer can learn the protein organization and associations using the self-attention mechanism and predicts the label for test contigs. We rigorously tested our developed tool named PhaMer on multiple datasets with increasing difficulty, including quality RefSeq genomes, short contigs, simulated metagenomic data, mock metagenomic data and the public IMG/VR dataset. All the experimental results show that PhaMer outperforms the state-of-the-art tools. In the real metagenomic data experiment, PhaMer improves the F1-score of phage detection by 27%.

Shang Jiayu, Tang Xubo, Guo Ruocheng, Sun Yanni

2022-Jun-30

deep learning, phage identification, protein cluster-based token, transformer

General General

iACVP: markedly enhanced identification of anti-coronavirus peptides using a dataset-specific word2vec model.

In Briefings in bioinformatics

The COVID-19 pandemic caused several million deaths worldwide. Development of anti-coronavirus drugs is thus urgent. Unlike conventional non-peptide drugs, antiviral peptide drugs are highly specific, easy to synthesize and modify, and not highly susceptible to drug resistance. To reduce the time and expense involved in screening thousands of peptides and assaying their antiviral activity, computational predictors for identifying anti-coronavirus peptides (ACVPs) are needed. However, few experimentally verified ACVP samples are available, even though a relatively large number of antiviral peptides (AVPs) have been discovered. In this study, we attempted to predict ACVPs using an AVP dataset and a small collection of ACVPs. Using conventional features, a binary profile and a word-embedding word2vec (W2V), we systematically explored five different machine learning methods: Transformer, Convolutional Neural Network, bidirectional Long Short-Term Memory, Random Forest (RF) and Support Vector Machine. Via exhaustive searches, we found that the RF classifier with W2V consistently achieved better performance on different datasets. The two main controlling factors were: (i) the dataset-specific W2V dictionary was generated from the training and independent test datasets instead of the widely used general UniProt proteome and (ii) a systematic search was conducted and determined the optimal k-mer value in W2V, which provides greater discrimination between positive and negative samples. Therefore, our proposed method, named iACVP, consistently provides better prediction performance compared with existing state-of-the-art methods. To assist experimentalists in identifying putative ACVPs, we implemented our model as a web server accessible via the following link: http://kurata35.bio.kyutech.ac.jp/iACVP.

Kurata Hiroyuki, Tsukiyama Sho, Manavalan Balachandran

2022-Jul-01

anti-coronavirus peptide, bioinformatics, deep learning, random forest, transformer, word2vec

Ophthalmology Ophthalmology

SD-LayerNet: Semi-supervised retinal layer segmentation in OCT using disentangled representation with anatomical priors

ArXiv Preprint

Optical coherence tomography (OCT) is a non-invasive 3D modality widely used in ophthalmology for imaging the retina. Achieving automated, anatomically coherent retinal layer segmentation on OCT is important for the detection and monitoring of different retinal diseases, like Age-related Macular Disease (AMD) or Diabetic Retinopathy. However, the majority of state-of-the-art layer segmentation methods are based on purely supervised deep-learning, requiring a large amount of pixel-level annotated data that is expensive and hard to obtain. With this in mind, we introduce a semi-supervised paradigm into the retinal layer segmentation task that makes use of the information present in large-scale unlabeled datasets as well as anatomical priors. In particular, a novel fully differentiable approach is used for converting surface position regression into a pixel-wise structured segmentation, allowing to use both 1D surface and 2D layer representations in a coupled fashion to train the model. In particular, these 2D segmentations are used as anatomical factors that, together with learned style factors, compose disentangled representations used for reconstructing the input image. In parallel, we propose a set of anatomical priors to improve network training when a limited amount of labeled data is available. We demonstrate on the real-world dataset of scans with intermediate and wet-AMD that our method outperforms state-of-the-art when using our full training set, but more importantly largely exceeds state-of-the-art when it is trained with a fraction of the labeled data.

Botond Fazekas, Guilherme Aresta, Dmitrii Lachinov, Sophie Riedl, Julia Mai, Ursula Schmidt-Erfurth, Hrvoje Bogunovic

2022-07-01

General General

Automated brain tumor identification using magnetic resonance imaging: A systematic review and meta-analysis.

In Neuro-oncology advances

Background : Automated brain tumor identification facilitates diagnosis and treatment planning. We evaluate the performance of traditional machine learning (TML) and deep learning (DL) in brain tumor detection and segmentation, using MRI.

Methods : A systematic literature search from January 2000 to May 8, 2021 was conducted. Study quality was assessed using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Detection meta-analysis was performed using a unified hierarchical model. Segmentation studies were evaluated using a random effects model. Sensitivity analysis was performed for externally validated studies.

Results : Of 224 studies included in the systematic review, 46 segmentation and 38 detection studies were eligible for meta-analysis. In detection, DL achieved a lower false positive rate compared to TML; 0.018 (95% CI, 0.011 to 0.028) and 0.048 (0.032 to 0.072) (P < .001), respectively. In segmentation, DL had a higher dice similarity coefficient (DSC), particularly for tumor core (TC); 0.80 (0.77 to 0.83) and 0.63 (0.56 to 0.71) (P < .001), persisting on sensitivity analysis. Both manual and automated whole tumor (WT) segmentation had "good" (DSC ≥ 0.70) performance. Manual TC segmentation was superior to automated; 0.78 (0.69 to 0.86) and 0.64 (0.53 to 0.74) (P = .014), respectively. Only 30% of studies reported external validation.

Conclusions : The comparable performance of automated to manual WT segmentation supports its integration into clinical practice. However, manual outperformance for sub-compartmental segmentation highlights the need for further development of automated methods in this area. Compared to TML, DL provided superior performance for detection and sub-compartmental segmentation. Improvements in the quality and design of studies, including external validation, are required for the interpretability and generalizability of automated models.

Kouli Omar, Hassane Ahmed, Badran Dania, Kouli Tasnim, Hossain-Ibrahim Kismet, Steele J Douglas

artificial intelligence, brain tumor, machine learning, meta-analysis, segmentation

General General

Artificial Intelligence-Based Cyber-Physical System for Severity Classification of Chikungunya Disease.

In IEEE journal of translational engineering in health and medicine

BACKGROUND : Artificial intelligence techniques are widely used in solving medical problems. Recently, researchers have used various deep learning techniques for the severity classification of Chikungunya disease. But these techniques suffer from overfitting and hyper-parameters tuning problems.

METHODS : In this paper, an artificial intelligence-based cyber-physical system (CPS) is proposed for the severity classification of Chikungunya disease. In CPS system, the physical components are integrated with computational algorithms to provide better results. Random forest (RF) is used to design the severity classification model for Chikungunya disease. However, RF suffers from overfitting and poor computational speed problems due to complex architectures and large amounts of connection weights. Therefore, an evolving RF model is proposed using the adaptive crossover-based genetic algorithm (ACGA).

RESULTS : ACGA can efficiently optimize the architecture of RF to achieve better results with better computational speed. Extensive experiments are performed by utilizing the Chikungunya disease dataset.

CONCLUSION : Performance analysis demonstrates that ACGA-RF achieves higher performance as compared to the competitive models in terms of F-measure, accuracy, sensitivity, and specificity. The proposed CPS system can prevent users from visiting hospitals and can render services to patients living far away from hospitals.

Singh Dilbag, Kaur Manjit, Kumar Vijay, Jabarulla Mohamed Yaseen, Lee Heung-No

2022

Artificial intelligence, Chikungunya disease, adaptive crossover, automated diagnosis, cyber-physical system, genetic algorithm, random forest, severity classification

General General

Prediction of Dental Implants Using Machine Learning Algorithms.

In Journal of healthcare engineering

It has been claimed that artificial intelligence (AI) has transformative potential for the healthcare sector by enabling increased productivity and creative methods of delivering healthcare services. Recently, there has been a major shift to artificial intelligence by businesses, government, and private sectors in general and the health sector in particular. Many studies have proven that artificial intelligence is contributing greatly to the health sector by discovering diseases and determining the best treatments for patients. Dentistry requires new innovative methods that serve both the patient and the service provider in obtaining the best and appropriate medical services. Artificial intelligence has the ability to develop the field of dentistry through early diagnosis and prediction of dental implant cases. This research develops a set of four machine learning algorithms to predict when a patient might need dental implants. These models are the Bayesian network, random forest, AdaBoost algorithm, and improved AdaBoost algorithm. This work shows that the developed algorithms can predict when a patient needs dental implants. Also, we believe that this proposal will advise managers and decision-makers in targeting patients with particular diagnoses. Analysis of the obtained results indicates good performance of the developed machine learning. As a result of this research, we note that the proposed improved AdaBoost algorithm increases the level of prediction accuracy and gives significantly higher performance than the other studied methods with the accuracy for the improved AdaBoost algorithm reaching 91.7%.

Alharbi Mafawez T, Almutiq Mutiq M

2022

Radiology Radiology

GAN augmentation for multiclass image classification using hemorrhage detection as a case-study.

In Journal of medical imaging (Bellingham, Wash.)

Purpose: In recent years, the development and exploration of deeper and more complex deep learning models has been on the rise. However, the availability of large heterogeneous datasets to support efficient training of deep learning models is lacking. While linear image transformations for augmentation have been used traditionally, the recent development of generative adversarial networks (GANs) could theoretically allow us to generate an infinite amount of data from the real distribution to support deep learning model training. Recently, the Radiological Society of North America (RSNA) curated a multiclass hemorrhage detection challenge dataset that includes over 800,000 images for hemorrhage detection, but all high-performing models were trained using traditional data augmentation techniques. Given a wide variety of selections, the augmentation for image classification often follows a trial-and-error policy. Approach: We designed conditional DCGAN (cDCGAN) and in parallel trained multiple popular GAN models to use as online augmentations and compared them to traditional augmentation methods for the hemorrhage case study. Results: Our experimentations show that the super-minority, epidural hemorrhages with cDCGAN augmentation presented a minimum of 2 × improvement in their performance against the traditionally augmented model using the same classifier configuration. Conclusion: This shows that for complex and imbalanced datasets, traditional data imbalancing solutions may not be sufficient and require more complex and diverse data augmentation methods such as GANs to solve.

Jason Jeong Jiwoong, Patel Bhavik, Banerjee Imon

2022-May

CT, data augmentation, generative adversarial networks, intracranial hemorrhage, multiclass classification

General General

Forestry Digital Twin With Machine Learning in Landsat 7 Data.

In Frontiers in plant science

Forest succession analysis can predict forest change trends in the study area, which provides an important basis for other studies. Remote sensing is a recognized and effective tool in forestry succession analysis. Many forest modeling studies use statistic values, but only a few uses remote sensing images. In this study, we propose a machine learning-based digital twin approach for forestry. A data processing algorithm was designed to process Landsat 7 remote sensing data as model's input. An LSTM-based model was constructed to fit historical image data of the study area. The experimental results show that this study's digital twin method can effectively forecast the study area's future image.

Jiang Xuetao, Jiang Meiyu, Gou YuChun, Li Qian, Zhou Qingguo

2022

Landsat 7, digital twin, machine learning, remote sensing, spatial temporal prediction

General General

College Organizational Innovation Performance-Oriented Internal Mechanism Analysis Using Lightweight Deep Learning under Health Psychology.

In Computational intelligence and neuroscience

The purpose is to improve employees' initiative and innovation performance and further improve the overall organizational efficiency of colleges. From the perspective of health psychology, this work analyzes the internal mechanism between leadership empowerment behavior and employee innovation performance at China Agricultural University. By introducing two intermediate variables: task-based psychological capital (PsyCap) and innovative PsyCap, this work puts forward a lightweight deep learning (DL) model. It constructs the college organizational innovation performance (OIP)-oriented internal evaluation system from four dimensions. They are personal development support, power appointment, participation in decision-making, and work guidance. Then, the proposed lightweight DL model reveals the internal relationship between employees' innovation performance and innovation factors using the questionnaire survey method. Overall, 360 questionnaires are distributed. The results show that the average values of the four dimensions (S, P, D, and G) of leadership empowerment are greater than 3, which are 4.3144, 4.3493, 4.4253, and 4.5286, respectively. S, P, D, and G represent empowerment, decision-making, communication, and innovation, respectively. The results show a high level of innovation performance in all dimensions. The finding proves that the influencing factors of employee innovation performance mainly include personal development support, empowerment, participation in decision-making, and work guidance. The effects of different dimensions vary significantly. Finally, the lightweight DL model can improve the analysis accuracy of the college OIP-oriented internal evaluation system. Therefore, college leaders should use the DL model and empowerment behavior to improve employees' psychological quality, innovation enthusiasm, and work efficiency, ultimately benefiting employees.

Liu Mengqiong

2022

General General

Extraction of Intangible Cultural Heritage Visual Elements by Deep Learning and Its Application in Grassland Tourism of the Silk Road Culture.

In Computational intelligence and neuroscience

Inner Mongolia is rich in grassland tourism resources, and the development of grassland tourism is of great significance to Inner Mongolia tourism and promotion of grassland protection. To better promote the grassland tourism of the Silk Road culture, the Conditional Global Area Network (CGAN) and Morphology Connected Component Chan-Vase (MCC-CV) algorithm are used to enhance and segment the traditional embroidery patterns in Inner Mongolia. Firstly, the generative adversarial network (GAN) is optimized, and a new GAN is proposed with the feature vector extracted from the convolutional neural network (CNN) as the constraint condition. Secondly, the automatic segmentation algorithm of embroidery based on the MCC-CV model is proposed, and finally, the proposed algorithm is tested. The test results demonstrate that after 8000 iterations of the proposed image-enhancement algorithm, its personalized features are enhanced, and the segmentation accuracy of the proposed image segmentation algorithm is 60%. The proposed algorithm provides some ideas for the application of deep learning (DL) technology in the grassland tourism of the Silk Road culture and also helps operators to accurately grasp the market and make tourists more comfortable and pleasant.

Bu Xiangwei, Jiang Mingyang

2022

General General

Design of Digital and Intelligent Financial Decision Support System Based on Artificial Intelligence.

In Computational intelligence and neuroscience

The quality of financial decision-making is very important to the future development of an enterprise, but it is often affected by the completeness of useful information for decision-making and the subjective factors of decision makers, and is often unstable. With the development of computer technology, the financial decision support system came into being, which improved the quality of financial decision to some extent. However, although the existing financial decision support system has achieved dataization to a certain extent, it still faces problems such as artificial leadership, insufficient intelligence, and poor decision-making efficiency, and cannot fully meet the needs of decision-makers. The explosion of artificial intelligence technology in recent years has provided potential improvements to financial decision support systems. In this article, we conduct a detailed analysis of the deficiencies in the current financial decision support system, build the mechanism and implementation path of the financial decision support system under artificial intelligence, and design a digital and intelligent financial decision support system. At the same time, we apply the proposed financial decision support system to the financial practice of X enterprise. Through the questionnaire survey, it is found that through the comprehensive application of artificial intelligence technology, the new system has a higher degree of intelligence than the existing system, and its construction can effectively improve the timeliness and accuracy of financial decision-making, while reducing the cost of financial decision-making. It is conducive to promoting the integration of management accounting and financial accounting.

Jia Tiejun, Wang Cheng, Tian Zhiqiang, Wang Bingyin, Tian Feng

2022

General General

Sustainable Smart Industry: A Secure and Energy Efficient Consensus Mechanism for Artificial Intelligence Enabled Industrial Internet of Things.

In Computational intelligence and neuroscience

In recent years, the Internet of Things (IoT) has been industrializing in various real-world applications, including smart industry and smart grids, to make human existence more reliable. An overwhelming volume of sensing data is produced from numerous sensor devices as the Industrial IoT (IIoT) becomes more industrialized. Artificial Intelligence (AI) plays a vital part in big data analyses as a powerful analytic tool that provides flexible and reliable information insights in real-time. However, there are some difficulties in designing and developing a useful big data analysis tool using machine learning, such as a centralized approach, security, privacy, resource limitations, and a lack of sufficient training data. On the other hand, Blockchain promotes a decentralized architecture for IIoT applications. It encourages the secure data exchange and resources among the various nodes of the IoT network, removing centralized control and overcoming the industry's current challenges. Our proposed approach goal is to design and implement a consensus mechanism that incorporates Blockchain and AI to allow successful big data analysis. This work presents an improved Delegated Proof of Stake (DPoS) algorithm-based IIoT network that combines Blockchain and AI for real-time data transmission. To accelerate IIoT block generation, nodes use an improved DPoS to reach a consensus for selecting delegates and store block information in the trading node. The proposed approach is evaluated regarding energy consumption and transaction efficiency compared with the exciting consensus mechanism. The evaluation results reveal that the proposed consensus algorithm reduces energy consumption and addresses current security issues.

Sasikumar A, Ravi Logesh, Kotecha Ketan, Saini Jatinderkumar R, Varadarajan Vijayakumar, Subramaniyaswamy V

2022

General General

The Navigation of Mobile Robot in the Indoor Dynamic Unknown Environment Based on Decision Tree Algorithm.

In Computational intelligence and neuroscience

This study proposes an optimized algorithm for the navigation of the mobile robot in the indoor and dynamic unknown environment based on the decision tree algorithm. Firstly, the error of the yaw value outputted from IMU sensor fusion module is analyzed in the indoor environment; then, the adaptive FAST SLAM is proposed to optimize the yaw value from the odometer; in the next, a decision tree algorithm is applied which predicts the correct moving direction of the mobile robot through the outputted yaw value from the IMU sensor fusion module and adaptive FAST SLAM of the odometer data in the indoor and dynamic environment; the following is the navigation algorithm proposed for the mobile robot in the dynamic and unknown environment; finally, a real mobile robot is designed to verify the proposed algorithm.The final result shows the proposed algorithms are valid and effective.

Yan Yupei, Ma Weimin, Li Yangmin, Wong Sengfat, He Ping, Zhu Shaoping, Yin Xuemei

2022

Public Health Public Health

COVID-19 severity detection using machine learning techniques from CT-images.

In Evolutionary intelligence

COVID-19 has spread worldwide and the World Health Organization was forced to list it as a Public Health Emergency of International Concern. The disease has severely impacted most of the people because it affects the lung and causes severe breathing problems and lung infections. Differentiating other lung ailments from COVID-19 infection and determining the severity is a challenging process. Doctors can give vital life-saving services and support patients' lives only if the severity of their condition is determined. This work proposed a two-step approach for detecting the COVID-19 infection from the lung CT images and determining the severity of the patient's illness. To extract the features, pre-trained models are used, and by analyzing them, integrated the features from AlexNet, DenseNet-201, and ResNet-50. The COVID-19 detection is carried out by using an Artificial Neural Network(ANN) model. After the COVID-19 infection has been identified, severity detection is performed. For that, image features are combined with the clinical data and is classified as High, Moderate, Low with the help of Cubic Support Vector Machine(SVM). By considering three severity levels, patients with high risk can be given more attention. The method was tested on a publicly available dataset and obtained an accuracy of 92.0%, sensitivity of 96.0%, and an F1-Score of 91.44% for COVID-19 detection and got overall accuracy of 90.0% for COVID-19 severity detection for three classes.

Aswathy A L, Anand Hareendran S, Chandra S S Vinod

2022-Jun-24

AlexNet, Computed tomography, DenseNet-201, Neural network, ResNet-50, Transfer learning

General General

Novel extreme regression-voting classifier to predict death risk in vaccinated people using VAERS data.

In PloS one ; h5-index 176.0

COVID-19 vaccination raised serious concerns among the public and people are mind stuck by various rumors regarding the resulting illness, adverse reactions, and death. Such rumors are dangerous to the campaign against the COVID-19 and should be dealt with accordingly and timely. One prospective solution is to use machine learning-based models to predict the death risk for vaccinated people and clarify people's perceptions regarding death risk. This study focuses on the prediction of the death risks associated with vaccinated people followed by a second dose for two reasons; first to build consensus among people to get the vaccines; second, to reduce the fear regarding vaccines. Given that, this study utilizes the COVID-19 VAERS dataset that records adverse events after COVID-19 vaccination as 'recovered', 'not recovered', and 'survived'. To obtain better prediction results, a novel voting classifier extreme regression-voting classifier (ER-VC) is introduced. ER-VC ensembles extra tree classifier and logistic regression using soft voting criterion. To avoid model overfitting and get better results, two data balancing techniques synthetic minority oversampling (SMOTE) and adaptive synthetic sampling (ADASYN) have been applied. Moreover, three feature extraction techniques term frequency-inverse document frequency (TF-IDF), bag of words (BoW), and global vectors (GloVe) have been used for comparison. Both machine learning and deep learning models are deployed for experiments. Results obtained from extensive experiments reveal that the proposed model in combination with TF-TDF has shown robust results with a 0.85 accuracy when trained on the SMOTE-balanced dataset. In line with this, validation of the proposed voting classifier on binary classification shows state-of-the-art results with a 0.98 accuracy. Results show that machine learning models can predict the death risk with high accuracy and can assist the authors in taking timely measures.

Saad Eysha, Sadiq Saima, Jamil Ramish, Rustam Furqan, Mehmood Arif, Choi Gyu Sang, Ashraf Imran

2022

General General

Shifts 2.0: Extending The Dataset of Real Distributional Shifts

ArXiv Preprint

Distributional shift, or the mismatch between training and deployment data, is a significant obstacle to the usage of machine learning in high-stakes industrial applications, such as autonomous driving and medicine. This creates a need to be able to assess how robustly ML models generalize as well as the quality of their uncertainty estimates. Standard ML baseline datasets do not allow these properties to be assessed, as the training, validation and test data are often identically distributed. Recently, a range of dedicated benchmarks have appeared, featuring both distributionally matched and shifted data. Among these benchmarks, the Shifts dataset stands out in terms of the diversity of tasks as well as the data modalities it features. While most of the benchmarks are heavily dominated by 2D image classification tasks, Shifts contains tabular weather forecasting, machine translation, and vehicle motion prediction tasks. This enables the robustness properties of models to be assessed on a diverse set of industrial-scale tasks and either universal or directly applicable task-specific conclusions to be reached. In this paper, we extend the Shifts Dataset with two datasets sourced from industrial, high-risk applications of high societal importance. Specifically, we consider the tasks of segmentation of white matter Multiple Sclerosis lesions in 3D magnetic resonance brain images and the estimation of power consumption in marine cargo vessels. Both tasks feature ubiquitous distributional shifts and a strict safety requirement due to the high cost of errors. These new datasets will allow researchers to further explore robust generalization and uncertainty estimation in new situations. In this work, we provide a description of the dataset and baseline results for both tasks.

Andrey Malinin, Andreas Athanasopoulos, Muhamed Barakovic, Meritxell Bach Cuadra, Mark J. F. Gales, Cristina Granziera, Mara Graziani, Nikolay Kartashev, Konstantinos Kyriakopoulos, Po-Jui Lu, Nataliia Molchanova, Antonis Nikitakis, Vatsal Raina, Francesco La Rosa, Eli Sivena, Vasileios Tsarsitalidis, Efi Tsompopoulou, Elena Volf

2022-06-30

Public Health Public Health

Emission Sector Impacts on Air Quality and Public Health in China From 2010 to 2020.

In GeoHealth

Anthropogenic emissions and ambient fine particulate matter (PM2.5) concentrations have declined in recent years across China. However, PM2.5 exposure remains high, ozone (O3) exposure is increasing, and the public health impacts are substantial. We used emulators to explore how emission changes (averaged per sector over all species) have contributed to changes in air quality and public health in China over 2010-2020. We show that PM2.5 exposure peaked in 2012 at 52.8 μg m-3, with contributions of 31% from industry and 22% from residential emissions. In 2020, PM2.5 exposure declined by 36% to 33.5 μg m-3, where the contributions from industry and residential sources reduced to 15% and 17%, respectively. The PM2.5 disease burden decreased by only 9% over 2012 where the contributions from industry and residential sources reduced to 15% and 17%, respectively 2020, partly due to an aging population with greater susceptibility to air pollution. Most of the reduction in PM2.5 exposure and associated public health benefits occurred due to reductions in industrial (58%) and residential (29%) emissions. Reducing national PM2.5 exposure below the World Health Organization Interim Target 2 (25 μg m-3) would require a further 80% reduction in residential and industrial emissions, highlighting the challenges that remain to improve air quality in China.

Conibear Luke, Reddington Carly L, Silver Ben J, Chen Ying, Arnold Stephen R, Spracklen Dominick V

2022-Jun

China, air quality, emissions, emulators, health impact assessment, machine learning

Pathology Pathology

A Self Supervised StyleGAN for Image Annotation and Classification with Extremely Limited Labels.

In IEEE transactions on medical imaging ; h5-index 74.0

The recent success of learning-based algorithms can be greatly attributed to the immense amount of annotated data used for training. Yet, many datasets lack annotations due to the high costs associated with labeling, resulting in degraded performances of deep learning methods. Self-supervised learning is frequently adopted to mitigate the reliance on massive labeled datasets since it exploits unlabeled data to learn relevant feature representations. In this work, we propose SS-StyleGAN, a self-supervised approach for image annotation and classification suitable for extremely small annotated datasets. This novel framework adds self-supervision to the StyleGAN architecture by integrating an encoder that learns the embedding to the StyleGAN latent space, which is well-known for its disentangled properties. The learned latent space enables the smart selection of representatives from the data to be labeled for improved classification performance. We show that the proposed method attains strong classification results using small labeled datasets of sizes 50 and even 10. We demonstrate the superiority of our approach for the tasks of COVID-19 and liver tumor pathology identification.

Hochberg Dana Cohen, Greenspan Hayit, Giryes Raja

2022-Jun-29

General General

Personalized Detection of Cognitive Biases in Actions of Users from Their Logs: Anchoring and Recency Biases

ArXiv Preprint

Cognitive biases are mental shortcuts humans use in dealing with information and the environment, and which result in biased actions and behaviors (or, actions), unbeknownst to themselves. Biases take many forms, with cognitive biases occupying a central role that inflicts fairness, accountability, transparency, ethics, law, medicine, and discrimination. Detection of biases is considered a necessary step toward their mitigation. Herein, we focus on two cognitive biases - anchoring and recency. The recognition of cognitive bias in computer science is largely in the domain of information retrieval, and bias is identified at an aggregate level with the help of annotated data. Proposing a different direction for bias detection, we offer a principled approach along with Machine Learning to detect these two cognitive biases from Web logs of users' actions. Our individual user level detection makes it truly personalized, and does not rely on annotated data. Instead, we start with two basic principles established in cognitive psychology, use modified training of an attention network, and interpret attention weights in a novel way according to those principles, to infer and distinguish between these two biases. The personalized approach allows detection for specific users who are susceptible to these biases when performing their tasks, and can help build awareness among them so as to undertake bias mitigation.

Atanu R Sinha, Navita Goyal, Sunny Dhamnani, Tanay Asija, Raja K Dubey, M V Kaarthik Raja, Georgios Theocharous

2022-06-30

General General

Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation.

In GeoHealth

Machine learning models can emulate chemical transport models, reducing computational costs and enabling more experimentation. We developed emulators to predict annual-mean fine particulate matter (PM2.5) and ozone (O3) concentrations and their associated chronic health impacts from changes in five major emission sectors (residential, industrial, land transport, agriculture, and power generation) in China. The emulators predicted 99.9% of the variance in PM2.5 and O3 concentrations. We used these emulators to estimate how emission reductions can attain air quality targets. In 2015, we estimate that PM2.5 exposure was 47.4 μg m-3 and O3 exposure was 43.8 ppb, associated with 2,189,700 (95% uncertainty interval, 95UI: 1,948,000-2,427,300) premature deaths per year, primarily from PM2.5 exposure (98%). PM2.5 exposure and the associated disease burden were most sensitive to industry and residential emissions. We explore the sensitivity of exposure and health to different combinations of emission reductions. The National Air Quality Target (35 μg m-3) for PM2.5 concentrations can be attained nationally with emission reductions of 72% in industrial, 57% in residential, 36% in land transport, 35% in agricultural, and 33% in power generation emissions. We show that complete removal of emissions from these five sectors does not enable the attainment of the WHO Annual Guideline (5 μg m-3) due to remaining air pollution from other sources. Our work provides the first assessment of how air pollution exposure and disease burden in China varies as emissions change across these five sectors and highlights the value of emulators in air quality research.

Conibear Luke, Reddington Carly L, Silver Ben J, Chen Ying, Knote Christoph, Arnold Stephen R, Spracklen Dominick V

2022-Jun

China, air quality, emulator, health impact assessment, machine learning, particulate matter

General General

Source code Optimized Parallel Inception: A fast COVID-19 screening software.

In Software impacts

COVID-19 and swine-origin influenza A (H1N1) are both pandemics that sparked significant concern worldwide. These two viruses have the same symptoms and occur at a collision timeline. Optimized Parallel Inception (OPI) presents a new strategy to screen the COVID-19 from H1N1 with use of only symptoms. In this paper the process of preprocessing, screening, and specifying feature importance by OPI and particle swarm optimization is presented. Experimental results indicate 98.88 accuracy for screening COVID-19, H1N1, and Neither COVID-19 Nor H1N1.

Tavakolian Alireza, Hajati Farshid, Rezaee Alireza, Fasakhodi Amirhossein Oliaei, Uddin Shahadat

2022-Jun-22

COVID-19, Coronavirus, Deep learning, H1N1 virus, Outbreak, Screening

Pathology Pathology

Asymmetry Disentanglement Network for Interpretable Acute Ischemic Stroke Infarct Segmentation in Non-Contrast CT Scans

ArXiv Preprint

Accurate infarct segmentation in non-contrast CT (NCCT) images is a crucial step toward computer-aided acute ischemic stroke (AIS) assessment. In clinical practice, bilateral symmetric comparison of brain hemispheres is usually used to locate pathological abnormalities. Recent research has explored asymmetries to assist with AIS segmentation. However, most previous symmetry-based work mixed different types of asymmetries when evaluating their contribution to AIS. In this paper, we propose a novel Asymmetry Disentanglement Network (ADN) to automatically separate pathological asymmetries and intrinsic anatomical asymmetries in NCCTs for more effective and interpretable AIS segmentation. ADN first performs asymmetry disentanglement based on input NCCTs, which produces different types of 3D asymmetry maps. Then a synthetic, intrinsic-asymmetry-compensated and pathology-asymmetry-salient NCCT volume is generated and later used as input to a segmentation network. The training of ADN incorporates domain knowledge and adopts a tissue-type aware regularization loss function to encourage clinically-meaningful pathological asymmetry extraction. Coupled with an unsupervised 3D transformation network, ADN achieves state-of-the-art AIS segmentation performance on a public NCCT dataset. In addition to the superior performance, we believe the learned clinically-interpretable asymmetry maps can also provide insights towards a better understanding of AIS assessment. Our code is available at https://github.com/nihaomiao/MICCAI22_ADN.

Haomiao Ni, Yuan Xue, Kelvin Wong, John Volpi, Stephen T. C. Wong, James Z. Wang, Xiaolei Huang

2022-06-30

Radiology Radiology

A Deep Learning Approach for Automated Segmentation of Kidneys and Exophytic Cysts in Individuals with Autosomal Dominant Polycystic Kidney Disease.

In Journal of the American Society of Nephrology : JASN

BACKGROUND : Total kidney volume (TKV) is an important imaging biomarker in autosomal dominant polycystic kidney disease (ADPKD). Manual computation of TKV, particularly with the exclusion of exophytic cysts, is laborious and time consuming.

METHODS : We developed a fully automated segmentation method for TKV using a deep learning network to selectively segment kidney regions while excluding exophytic cysts. We used abdominal T2 -weighted magnetic resonance images from 210 individuals with ADPKD who were divided into two groups: one group of 157 to train the network and a second group of 53 to test it. With a 3D U-Net architecture using dataset fingerprints, the network was trained by K-fold cross-validation, in that 80% of 157 cases were for training and the remaining 20% were for validation. We used Dice similarity coefficient, intraclass correlation coefficient, and Bland-Altman analysis to assess the performance of the automated segmentation method compared with the manual method.

RESULTS : The automated and manual reference methods exhibited excellent geometric concordance (Dice similarity coefficient: mean±SD, 0.962±0.018) on the test datasets, with kidney volumes ranging from 178.9 to 2776.0 ml (mean±SD, 1058.5±706.8 ml) and exophytic cysts ranging from 113.4 to 2497.6 ml (mean±SD, 549.0±559.1 ml). The intraclass correlation coefficient was 0.9994 (95% confidence interval, 0.9991 to 0.9996; P<0.001) with a minimum bias of -2.424 ml (95% limits of agreement, -49.80 to 44.95).

CONCLUSIONS : We developed a fully automated segmentation method to measure TKV that excludes exophytic cysts and has an accuracy similar to that of a human expert. This technique may be useful in clinical studies that require automated computation of TKV to evaluate progression of ADPKD and response to treatment.

Kim Youngwoo, Tao Cheng, Kim Hyungchan, Oh Geum-Yoon, Ko Jeongbeom, Bae Kyongtae T

2022-Jun-29

ADPKD, chronic kidney disease, chronic kidney failure, cystic kidney, deep learning, exophytic cyst, image segmentation, kidney volume, risk factors

General General

Integrating crop growth model and radiative transfer model to improve estimation of crop traits based on deep learning.

In Journal of experimental botany

A major challenge for the estimation of crop traits (biophysical variables) from canopy reflectance is the creation of a high-quality training dataset. To address this problem, this research investigated a conceptual framework by integrating a crop growth model with a radiative transfer model to introduce biological constraints in synthetic training dataset. In addition to the comparison of two datasets without and with biological constraints, we also investigated the effects of observation geometry, retrieval method, and wavelength range on estimation accuracy of four crop traits (leaf area index, leaf chlorophyll content, leaf dry matter, and leaf water content) of wheat. The theoretical analysis demonstrated potential advantages of adding biological constraints in synthetic training datasets as well as the capability of deep learning. Additionally, the predictive models were validated on real unmanned aerial vehicle-based multi-spectral images collected from wheat plots contrasting in canopy structure. The predictive model trained over a synthetic dataset with biological constraints enabled the prediction of leaf water content from visible to near infrared range based on the correlations between crop traits. Our findings presented the potential of proposed conceptual framework in simultaneously retrieving multiple crop traits from canopy reflectance for applications in precision agriculture and plant breeding.

Chen Qiaomin, Zheng Bangyou, Chen Tong, Chapman Scott C

2022-Jun-30

APSIM, PROSAIL, biological constraints, canopy and leaf biophysical traits, canopy reflectance, unmanned aerial vehicle

Radiology Radiology

Towards PErsonalised PRognosis for children with traumatic brain injury: the PEPR study protocol.

In BMJ open

INTRODUCTION : Traumatic brain injury (TBI) in children can be associated with poor outcome in crucial functional domains, including motor, neurocognitive and behavioural functioning. However, outcome varies between patients and is mediated by complex interplay between demographic factors, premorbid functioning and (sub)acute clinical characteristics. At present, methods to understand let alone predict outcome on the basis of these variables are lacking, which contributes to unnecessary follow-up as well as undetected impairments in children. Therefore, this study aims to develop prognostic models for the individual outcome of children with TBI in a range of important developmental domains. In addition, the potential added value of advanced neuroimaging data and the use of machine learning algorithms in the development of prognostic models will be assessed.

METHODS AND ANALYSIS : 210 children aged 4-18 years diagnosed with mild-to-severe TBI will be prospectively recruited from a research network of Dutch hospitals. They will be matched 2:1 to a control group of neurologically healthy children (n=105). Predictors in the model will include demographic, premorbid and clinical measures prospectively registered from the TBI hospital admission onwards as well as MRI metrics assessed at 1 month post-injury. Outcome measures of the prognostic models are (1) motor functioning, (2) intelligence, (3) behavioural functioning and (4) school performance, all assessed at 6 months post-injury.

ETHICS AND DISSEMINATION : Ethics has been obtained from the Medical Ethical Board of the Amsterdam UMC (location AMC). Findings of our multicentre prospective study will enable clinicians to identify TBI children at risk and aim towards a personalised prognosis. Lastly, findings will be submitted for publication in open access, international and peer-reviewed journals.

TRIAL REGISTRATION NUMBER : NL71283.018.19 and NL9051.

Kooper Cece C, Oosterlaan Jaap, Bruining Hilgo, Engelen Marc, Pouwels Petra J W, Popma Arne, van Woensel Job B M, Buis Dennis R, Steenweg Marjan E, Hunfeld Maayke, Königs Marsh

2022-Jun-29

Magnetic resonance imaging, Paediatric intensive & critical care, Paediatric neurology, Paediatric neurosurgery, Paediatric radiology

General General

Material decomposition from photon-counting CT using a convolutional neural network and energy-integrating CT training labels.

In Physics in medicine and biology

OBJECTIVE : Photon-counting CT (PCCT) has better dose efficiency and spectral resolution than energy-integrating CT, which is advantageous for material decomposition. Unfortunately, the accuracy of PCCT-based material decomposition is limited due to spectral distortions in the photon-counting detector (PCD).

APPROACH : In this work, we demonstrate a deep learning (DL) approach that compensates for spectral distortions in the PCD and improves accuracy in material decomposition by using decomposition maps provided by high-dose multi-energy-integrating detector (EID) data as training labels. We use a 3D U-net architecture and compare networks with PCD filtered backprojection (FBP) reconstruction (FBP2Decomp), PCD iterative reconstruction (Iter2Decomp), and PCD decomposition (Decomp2Decomp) as the input.

MAIN RESULTS : We found that our Iter2Decomp approach performs best, but DL outperforms matrix inversion decomposition regardless of the input. Compared to PCD matrix inversion decomposition, Iter2Decomp gives 27.50% lower root mean squared error (RMSE) in the iodine (I) map and 59.87% lower RMSE in the photoelectric effect (PE) map. In addition, it increases the structural similarity (SSIM) by 1.92%, 6.05%, and 9.33% in the I, Compton scattering (CS), and PE maps, respectively. When taking measurements from iodine and calcium vials, Iter2Decomp provides excellent agreement with multi-EID decomposition. One limitation is some blurring caused by our DL approach, with a decrease from 1.98 line pairs/mm at 50% modulation transfer function (MTF) with PCD matrix inversion decomposition to 1.75 line pairs/mm at 50% MTF when using Iter2Decomp.

SIGNIFICANCE : Overall, this work demonstrates that our DL approach with high-dose multi-EID derived decomposition labels is effective at generating more accurate material maps from PCD data. More accurate preclinical spectral PCCT imaging such as this could serve for developing nanoparticles that show promise in the field of theranostics (therapy and diagnostics).

Nadkarni Rohan, Allphin Alex, Clark Darin P, Badea Cristian T

2022-Jun-29

CNN, deep learning, material decomposition, photon-counting CT, theranostics

Surgery Surgery

A lightweight 3D UNet model for glioma grading.

In Physics in medicine and biology

OBJECTIVE : Glioma is one of the most fatal cancers in the world which has been divided into Low Grade Glioma (LGG) and High Grade Glioma (HGG), and its image grading has become a hot topic of contemporary research. Magnetic Resonance Imaging (MRI) is a vital diagnostic tool for brain tumor detection, analysis, and surgical planning. Accurate and automatic glioma grading is crucial for speeding up diagnosis and treatment planning. Aiming at the problems of 1) large number of parameters, 2) complex calculation, and 3) poor speed of the current glioma grading algorithms based on deep learning, this paper proposes a lightweight 3D UNet deep learning framework, which can improve classification accuracy in comparison with the existing methods.

APPROACH : To improve efficiency while maintaining accuracy, existing 3D UNet has been excluded, and depthwise separable convolution has been applied to 3D convolution to reduce the number of network parameters. The weight of parameters on the basis of space and channel compression & excitation module has been strengthened to improve the model in the feature map, reduce the weight of redundant parameters, and strengthen the performance of the model.

MAIN RESULTS : A total of 560 patients with glioma were retrospectively reviewed. All patients underwent MRI before surgery. The experiments were carried out on T1w, T2w, FLAIR, and CET1w images. Additionally, a way of marking tumor area by cube bounding box is presented which has no significant difference in model performance with the manually drawn ground truth. Evaluated on test data sets using the proposed model has shown good results (with accuracy of 89.29%).

SIGNIFICANCE : This work serves to achieve LGG/HGG grading by simple, effective, and non-invasive diagnostic approaches to provide diagnostic suggestions for clinical usage, thereby facilitating hasten treatment decisions.

Yu Xuan, Wu Yaping, Bai Yan, Han Hui, Chen Lijuan, Gao Haiyan, Wei Huanhuan, Wang Meiyun

2022-Jun-29

MRIs, depthwise separable convolution, glioma grading, lightweight 3D UNet, scSE block

General General

A Semi-Supervised Learning Approach for COVID-19 Detection from Chest CT Scans.

In Neurocomputing

COVID-19 has spread rapidly all over the world and has infected more than 200 countries and regions. Early screening of suspected infected patients is essential for preventing and combating COVID-19. Computed Tomography (CT) is a fast and efficient tool which can quickly provide chest scan results. To reduce the burden on doctors of reading CTs, in this article, a high precision diagnosis algorithm of COVID-19 from chest CTs is designed for intelligent diagnosis. A semi-supervised learning approach is developed to solve the problem when only small amount of labelled data is available. While following the MixMatch rules to conduct sophisticated data augmentation, we introduce a model training technique to reduce the risk of model over-fitting. At the same time, a new data enhancement method is proposed to modify the regularization term in MixMatch. To further enhance the generalization of the model, a convolutional neural network based on an attention mechanism is then developed that enables to extract multi-scale features on CT scans. The proposed algorithm is evaluated on an independent CT dataset of the chest from COVID-19 and achieves the area under the receiver operating characteristic curve (AUC) value of 0.932, accuracy of 90.1%, sensitivity of 91.4%, specificity of 88.9%, and F1-score of 89.9%. The results show that the proposed algorithm can accurately diagnose whether a chest CT belongs to a positive or negative indication of COVID-19, and can help doctors to diagnose rapidly in the early stages of a COVID-19 outbreak.

Zhang Yong, Su Li, Liu Zhenxing, Tan Wei, Jiang Yinuo, Cheng Cheng

2022-Jun-23

Attention mechanisms, COVID-19, Computed tomography, Deep learning, Semi-supervised learning

General General

Forecasting new diseases in low-data settings using transfer learning.

In Chaos, solitons, and fractals

Recent infectious disease outbreaks, such as the COVID-19 pandemic and the Zika epidemic in Brazil, have demonstrated both the importance and difficulty of accurately forecasting novel infectious diseases. When new diseases first emerge, we have little knowledge of the transmission process, the level and duration of immunity to reinfection, or other parameters required to build realistic epidemiological models. Time series forecasts and machine learning, while less reliant on assumptions about the disease, require large amounts of data that are also not available in early stages of an outbreak. In this study, we examine how knowledge of related diseases can help make predictions of new diseases in data-scarce environments using transfer learning. We implement both an empirical and a synthetic approach. Using data from Brazil, we compare how well different machine learning models transfer knowledge between two different dataset pairs: case counts of (i) dengue and Zika, and (ii) influenza and COVID-19. In the synthetic analysis, we generate data with an SIR model using different transmission and recovery rates, and then compare the effectiveness of different transfer learning methods. We find that transfer learning offers the potential to improve predictions, even beyond a model based on data from the target disease, though the appropriate source disease must be chosen carefully. While imperfect, these models offer an additional input for decision makers for pandemic response.

Roster Kirstin, Connaughton Colm, Rodrigues Francisco A

2022-Aug

COVID-19, Epidemic forecasting, Machine learning, Transfer learning, Zika

Pathology Pathology

Exposing and addressing the fragility of neural networks in digital pathology

ArXiv Preprint

Neural networks have achieved impressive results in many medical imaging tasks but often perform substantially worse on out-of-distribution datasets originating from different medical centres or patient cohorts. Evaluating this lack of ability to generalise and address the underlying problem are the two main challenges in developing neural networks intended for clinical practice. In this study, we develop a new method for evaluating neural network models' ability to generalise by generating a large number of distribution-shifted datasets, which can be used to thoroughly investigate their robustness to variability encountered in clinical practice. Compared to external validation, \textit{shifted evaluation} can provide explanations for why neural networks fail on a given dataset, thus offering guidance on how to improve model robustness. With shifted evaluation, we demonstrate that neural networks, trained with state-of-the-art methods, are highly fragile to even small distribution shifts from training data, and in some cases lose all discrimination ability. To address this fragility, we develop an augmentation strategy, explicitly designed to increase neural networks' robustness to distribution shifts. \texttt{StrongAugment} is evaluated with large-scale, heterogeneous histopathology data including five training datasets from two tissue types, 274 distribution-shifted datasets and 20 external datasets from four countries. Neural networks trained with \texttt{StrongAugment} retain similar performance on all datasets, even with distribution shifts where networks trained with current state-of-the-art methods lose all discrimination ability. We recommend using strong augmentation and shifted evaluation to train and evaluate all neural networks intended for clinical practice.

Joona Pohjonen, Carolin Stürenberg, Atte Föhr, Esa Pitkänen, Antti Rannikko, Tuomas Mirtti

2022-06-30

General General

Underwater robot coordination using a bio-inspired electrocommunication system.

In Bioinspiration & biomimetics

Underwater multi-robot coordination systems are rarely developed due to the challenging communication and control systems. In nature, weakly electric fish can electrically communicate (termed electrocommunication) in turbid water to organize their collective activity. Inspired by this phenomenon, we have developed an artificial electrocommunication system for underwater robots in our previous work. In this study, we further design a time-sharing multiplexing network protocol for electrocommunication to avoid communication conflicts during robot coordination. We then revise a distributed controller to coordinate a group of underwater robots based on the developed networking system. In particular, electrocommunication network is used to obtain the state of the neighboring robots. The distributed controller is designed to generate the desired control of each robot according to the neighboring states. A central pattern generator (CPG) controller then adjusts the individual's velocity based on the desired control to achieve the desired position. Simulations and experiments with three developed underwater robots demonstrate that a team of underwater robots is able to achieve successful coordination with the help of the developed electrocommunication and the control systems.

Zhou Yang, Wang Wei, Zhang Han, Zheng Xingwen, Li Liang, Wang Chen, Xu Gang, Xie Guangming

2022-Jun-29

distributed controller, electrocommunication, network protocol, robot coordination, underwater robots

General General

Multi-view cross-subject seizure detection with information bottleneck attribution.

In Journal of neural engineering ; h5-index 52.0

OBJECTIVE : Significant progress has been witnessed in within-subject seizure detection from Electroencephalography (EEG) signals. Consequently, more and more works have been shifted from within-subject seizure detection to cross-subject scenarios. However, the progress is hindered by inter-patient variations caused by gender, seizure type, etc. Approach: To tackle this problem, we propose a multi-view cross-object seizure detection model with information bottleneck attribution. Feature representations specific to seizures are learned from raw EEG data by adversarial deep learning. Combined with the manually designed discriminative features, the model can detect seizures across different subjects. In addition, we introduce information bottleneck attribution to provide insights into the decision-making of the adversarial learning process, thus enhancing the interpretability of the model.

MAIN RESULTS : Extensive experiments are conducted on two benchmark datasets. The experimental results verify the efficacy of the model.

Zhao Yanna, Zhang Gaobo, Zhang Yongfeng, Xiao Tiantian, Wang Ziwei, Xu Fangzhou, Zheng Yuanjie

2022-Jun-29

EEG, adversarial learning, cross-subject, information bottleneck attribution, seizure detection

Internal Medicine Internal Medicine

Frame-by-Frame Analysis of a Commercially Available Artificial Intelligence Polyp Detection System in Full-Length Colonoscopies.

In Digestion

INTRODUCTION : Computer-aided detection (CADe) helps increase colonoscopic polyp detection. However, little is known about other performance metrics like the number and duration of false-positive (FP) activations or how stable the detection of a polyp is.

METHODS : 111 colonoscopy videos with total 1,793,371 frames were analyzed on a frame-by-frame basis using a commercially available CADe system (GI-Genius, Medtronic Inc.). Primary endpoint was the number and duration of FP activations per colonoscopy. Additionally, we analyzed other CADe performance parameters, including per-polyp sensitivity, per-frame sensitivity, and first detection time of a polyp. We additionally investigated whether a threshold for withholding CADe activations can be set to suppress short FP activations and how this threshold alters the CADe performance parameters.

RESULTS : A mean of 101 ± 88 FPs per colonoscopy were found. Most of the FPs consisted of less than three frames with a maximal 66-ms duration. The CADe system detected all 118 polyps and achieved a mean per-frame sensitivity of 46.6 ± 26.6%, with the lowest value for flat polyps (37.6 ± 24.8%). Withholding CADe detections up to 6 frames length would reduce the number of FPs by 87.97% (p < 0.001) without a significant impact on CADe performance metrics.

CONCLUSIONS : The CADe system works reliable but generates many FPs as a side effect. Since most FPs are very short, withholding short-term CADe activations could substantially reduce the number of FPs without impact on other performance metrics. Clinical practice would benefit from the implementation of customizable CADe thresholds.

Brand Markus, Troya Joel, Krenzer Adrian, De Maria Costanza, Mehlhase Niklas, Götze Sebastian, Walter Benjamin, Meining Alexander, Hann Alexander

2022-Jun-29

Artificial intelligence, Colonoscopy, Computer-aided detection, Deep learning

General General

Eliminating CT radiation for clinical PET examination using deep learning.

In European journal of radiology ; h5-index 47.0

Clinical PET/CT examinations rely on CT modality for anatomical localization and attenuation correction of the PET data. However, the use of CT significantly increases the risk of ionizing radiation exposure for patients. We propose a deep learning framework to learn the relationship mapping between attenuation corrected (AC) PET and non-attenuation corrected (NAC) PET images to estimate PET attenuation maps and generate pseudo-CT images for medical observation. In this study, 5760, 1608 and 1351 pairs of transverse PET-CT slices were used as the training, validation, and testing sets, respectively, to implement the proposed framework. A pix2pix model was adopted to predict AC PET images from NAC PET images, which allowed the calculation of PET attenuation maps (µ-maps). The same model was then applied to generate realistic CT images from the calculated µ-maps. The quality of predicted AC PET and CT was assessed using normalized root mean square error (NRMSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and Pearson correlation coefficient (PCC). Relative to true AC PET, the synthetic AC PET achieved superior quantitative performances with 2.20 ± 1.17% NRMSE, 34.03 ± 4.73 dB PSNR, 97.90 ± 1.22% SSIM and 98.45 ± 1.31% PCC. The synthetic CT and synthetic AC PET images were deemed acceptable by radiologists who rated the images, as they provided sufficient anatomical and functional information, respectively. This work demonstrates that the proposed deep learning framework is a promising method in clinical applications, such as radiotherapy and low-dose imaging.

Li Qingneng, Zhu Xiaohua, Zou Sijuan, Zhang Na, Liu Xin, Yang Yongfeng, Zheng Hairong, Liang Dong, Hu Zhanli

2022-Jun-23

Attenuation correction, Clinical examination, Computed tomography, Deep learning, Positron emission tomography

General General

Unsupervised PET logan parametric image estimation using conditional deep image prior.

In Medical image analysis

Recently, deep learning-based denoising methods have been gradually used for PET images denoising and have shown great achievements. Among these methods, one interesting framework is conditional deep image prior (CDIP) which is an unsupervised method that does not need prior training or a large number of training pairs. In this work, we combined CDIP with Logan parametric image estimation to generate high-quality parametric images. In our method, the kinetic model is the Logan reference tissue model that can avoid arterial sampling. The neural network was utilized to represent the images of Logan slope and intercept. The patient's computed tomography (CT) image or magnetic resonance (MR) image was used as the network input to provide anatomical information. The optimization function was constructed and solved by the alternating direction method of multipliers (ADMM) algorithm. Both simulation and clinical patient datasets demonstrated that the proposed method could generate parametric images with more detailed structures. Quantification results showed that the proposed method results had higher contrast-to-noise (CNR) improvement ratios (PET/CT datasets: 62.25%±29.93%; striatum of brain PET datasets : 129.51%±32.13%, thalamus of brain PET datasets: 128.24%±31.18%) than Gaussian filtered results (PET/CT datasets: 23.33%±18.63%; striatum of brain PET datasets: 74.71%±8.71%, thalamus of brain PET datasets: 73.02%±9.34%) and nonlocal mean (NLM) denoised results (PET/CT datasets: 37.55%±26.56%; striatum of brain PET datasets: 100.89%±16.13%, thalamus of brain PET datasets: 103.59%±16.37%).

Cui Jianan, Gong Kuang, Guo Ning, Kim Kyungsang, Liu Huafeng, Li Quanzheng

2022-Jun-23

Deep image prior, Logan plot, PET Parametric image estimation, Unsupervised learning

Surgery Surgery

Patients with Femoral Neck Fractures Are at Risk for Conversion to Arthroplasty After Internal Fixation: A Machine-Learning Algorithm.

In Clinical orthopaedics and related research ; h5-index 71.0

BACKGROUND : Femoral neck fractures are common and are frequently treated with internal fixation. A major disadvantage of internal fixation is the substantially high number of conversions to arthroplasty because of nonunion, malunion, avascular necrosis, or implant failure. A clinical prediction model identifying patients at high risk of conversion to arthroplasty may help clinicians in selecting patients who could have benefited from arthroplasty initially.

QUESTION/PURPOSE : What is the predictive performance of a machine-learning (ML) algorithm to predict conversion to arthroplasty within 24 months after internal fixation in patients with femoral neck fractures?

METHODS : We included 875 patients from the Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trial. The FAITH trial consisted of patients with low-energy femoral neck fractures who were randomly assigned to receive a sliding hip screw or cancellous screws for internal fixation. Of these patients, 18% (155 of 875) underwent conversion to THA or hemiarthroplasty within the first 24 months. All patients were randomly divided into a training set (80%) and test set (20%). First, we identified 27 potential patient and fracture characteristics that may have been associated with our primary outcome, based on biomechanical rationale and previous studies. Then, random forest algorithms (an ML learning, decision tree-based algorithm that selects variables) identified 10 predictors of conversion: BMI, cardiac disease, Garden classification, use of cardiac medication, use of pulmonary medication, age, lung disease, osteoarthritis, sex, and the level of the fracture line. Based on these variables, five different ML algorithms were trained to identify patterns related to conversion. The predictive performance of these trained ML algorithms was assessed on the training and test sets based on the following performance measures: (1) discrimination (the model's ability to distinguish patients who had conversion from those who did not; expressed with the area under the receiver operating characteristic [AUC]), (2) calibration (the plotted estimated versus the observed probabilities; expressed with the calibration curve intercept and slope), and (3) the overall model performance (Brier score: a composite of discrimination and calibration).

RESULTS : None of the five ML algorithms performed well in predicting conversion to arthroplasty in the training set and the test set; AUCs of the algorithms in the training set ranged from 0.57 to 0.64, slopes of calibration plots ranged from 0.53 to 0.82, calibration intercepts ranged from --0.04 to 0.05, and Brier scores ranged from 0.14 to 0.15. The algorithms were further evaluated in the test set; AUCs ranged from 0.49 to 0.73, calibration slopes ranged from 0.17 to 1.29, calibration intercepts ranged from -1.28 to 0.34, and Brier scores ranged from 0.13 to 0.15.

CONCLUSION : The predictive performance of the trained algorithms was poor, despite the use of one of the best datasets available worldwide on this subject. If the current dataset consisted of different variables or more patients, the performance may have been better. Also, various reasons for conversion to arthroplasty were pooled in this study, but the separate prediction of underlying pathology (such as, avascular necrosis or nonunion) may be more precise. Finally, it may be possible that it is inherently difficult to predict conversion to arthroplasty based on preoperative variables alone. Therefore, future studies should aim to include more variables and to differentiate between the various reasons for arthroplasty.

LEVEL OF EVIDENCE : Level III, prognostic study.

van de Kuit Anouk, Oosterhoff Jacobien H F, Dijkstra Hidde, Sprague Sheila, Bzovsky Sofia, Bhandari Mohit, Swiontkowski Marc, Schemitsch Emil H, IJpma Frank F A, Poolman Rudolf W, Doornberg Job N, Hendrickx Laurent A M

2022-Jun-21

Surgery Surgery

Machine-learning Models Predict 30-Day Mortality, Cardiovascular Complications, and Respiratory Complications After Aseptic Revision Total Joint Arthroplasty.

In Clinical orthopaedics and related research ; h5-index 71.0

BACKGROUND : Aseptic revision THA and TKA are associated with an increased risk of adverse outcomes compared with primary THA and TKA. Understanding the risk profiles for patients undergoing aseptic revision THA or TKA may provide an opportunity to decrease the risk of postsurgical complications. There are risk stratification tools for postoperative complications after aseptic revision TKA or THA; however, current tools only include nonmodifiable risk factors, such as medical comorbidities, and do not include modifiable risk factors.

QUESTIONS/PURPOSES : (1) Can machine learning predict 30-day mortality and complications for patients undergoing aseptic revision THA or TKA using a cohort from the American College of Surgeons National Surgical Quality Improvement Program database? (2) Which patient variables are the most relevant in predicting complications?

METHODS : This was a temporally validated, retrospective study analyzing the 2014 to 2019 National Surgical Quality Improvement Program database, as this database captures a large cohort of aseptic revision THA and TKA patients across a broad range of clinical settings and includes preoperative laboratory values. The training data set was 2014 to 2018, and 2019 was the validation data set. Given that predictive models learn expected prevalence of outcomes, this split allows assessment of model performance in contemporary patients. Between 2014 and 2019, a total of 24,682 patients underwent aseptic revision TKA and 17,871 patients underwent aseptic revision THA. Of those, patients with CPT codes corresponding to aseptic revision TKA or THA were considered as potentially eligible. Based on excluding procedures involving unclean wounds, 78% (19,345 of 24,682) of aseptic revision TKA procedures and 82% (14,711 of 17,871) of aseptic revision THA procedures were eligible. Ten percent of patients in each of the training and validation cohorts had missing predictor variables. Most of these missing data were preoperative sodium or hematocrit (8% in both the training and validation cohorts). No patients had missing outcome data. No patients were excluded due to missing data. The mean patient was age 66 ± 12 years, the mean BMI was 32 ± 7 kg/m2, and the mean American Society of Anesthesiologists (ASA) Physical Score was 3 (56%). XGBoost was then used to create a scoring tool for 30-day adverse outcomes. XGBoost was chosen because it can handle missing data, it is nonlinear, it can assess nuanced relationships between variables, it incorporates techniques to reduce model complexity, and it has a demonstrated record of producing highly accurate machine-learning models. Performance metrics included discrimination and calibration. Discrimination was assessed by c-statistics, which describe the area under the receiver operating characteristic curve. This quantifies how well a predictive model discriminates between patients who have the outcome of interest versus those who do not. Relevant ranges for c-statistics include good (0.70 to 0.79), excellent (0.80 to 0.89), and outstanding (> 0.90). We estimated 95% confidence intervals (CIs) for c-statistics by 500-sample bootstrapping. Calibration curves quantify reliability of model predictions. Reliable models produce prediction probabilities for outcomes that are similar to observed probabilities of those outcomes, so a well-calibrated model should demonstrate a calibration curve that does not deviate substantially from a line of slope 1 and intercept 0. Calibration curves were generated on the 2019 validation data. Shapley Additive Explanations (SHAP) visualizations were used to investigate feature importance to gain insight into how models made predictions. The models were built into an online calculator for ongoing testing and validation. The risk calculator, which is freely available (http://nb-group.org/rev2/), allows a user to input patient data to calculate postoperative risk of 30-day mortality, cardiac, and respiratory complications after aseptic revision TKA or THA. A post hoc analysis was performed to assess whether using data from 2020 would improve calibration on 2019 data.

RESULTS : The model accurately predicted mortality, cardiac complications, and respiratory complications after aseptic revision THA or TKA, with c-statistics of 0.88 (95% CI 0.83 to 0.93), 0.80 (95% CI 0.75 to 0.84), and 0.78 (95% CI 0.74 to 0.82), respectively, on internal validation and 0.87 (95% CI 0.77 to 0.96), 0.70 (95% CI 0.61 to 0.78), and 0.82 (95% CI 0.75 to 0.88), respectively, on temporal validation. Calibration curves demonstrated slight over-confidence in predictions (most predicted probabilities were higher than observed probabilities). Post hoc analysis of 2020 data did not yield improved calibration on the 2019 validation set. Important risk factors for all models included increased age and higher ASA, BMI, hematocrit level, and sodium level. Hematocrit and ASA were in the top three most important features for all models. The factor with the strongest association for mortality and cardiac complication models was age, and for the respiratory model, chronic obstructive pulmonary disease. Risk related to sodium followed a U-shaped curve. Preoperative hyponatremia and hypernatremia predicted an increased risk of mortality and respiratory complications, with a nadir of 138 mmol/L; hyponatremia was more strongly associated with mortality than hypernatremia. A hematocrit level less than 36% predicted an increased risk of all three adverse outcomes. A BMI less than 24 kg/m2-and especially less than 20 kg/m2-predicted an increased risk of all three adverse outcomes, with little to no effect for higher BMI.

CONCLUSION : This temporally validated model predicted 30-day mortality, cardiac complications, and respiratory complications after aseptic revision THA or TKA with c-statistics ranging from 0.78 to 0.88. This freely available risk calculator can be used preoperatively by surgeons to educate patients on their individual postoperative risk of these specific adverse outcomes. Unanswered questions that remain include whether altering the studied preoperative patient variables, such as sodium or hematocrit, would affect postoperative risk of adverse outcomes; however, a prospective cohort study is needed to answer this question.

LEVEL OF EVIDENCE : Level III, therapeutic study.

Abraham Vivek Mathew, Booth Greg, Geiger Phillip, Balazs George Christian, Goldman Ashton

2022-Jun-20

General General

Accelerating Discovery of High Fractional Free Volume Polymers from a Data-Driven Approach.

In ACS applied materials & interfaces ; h5-index 147.0

As a fundamental structure characteristic in polymers, fractional free volume (FFV) plays an indispensable role in governing polymer properties and performance. However, the design of new high-FFV polymers is challenging. In this study, we report a data-driven approach and aim to accelerate the discovery of high-FFV polymers. First, a computational method is proposed to calculate FFV, and a two-step fragmentation method is developed to construct a fragment library for digital representation of polymer structures. Data mining is employed to identify promising fragments for high FFV. Subsequently, machine learning (ML) models are trained using a data set with 1683 polymers and their excellent transferability is demonstrated by out-of-sample predictions in another data set with 11,479 polymers. Finally, the ML models are used to screen ∼1 million hypothetical polymers, and 29,482 polymers with FFV > 0.2 are shortlisted; representative high-FFV polymers are validated by molecular simulations, and design strategies are highlighted. To further facilitate the discovery of new high-FFV polymers, we develop an online interactive platform https://ffv-prediction.herokuapp.com, which allows for rapid FFV predictions, given polymer structures. The data-driven approach in this study might advance the development of new high-FFV polymers and further explore quantitative structure-property relationships for polymers.

Wang Mao, Jiang Jianwen

2022-Jun-29

Fractional free volume, fragmentation, machine learning, molecular simulation, polymer

General General

Development and Initial Validation of Two Brief Measures of Left-Wing Authoritarianism: A Machine Learning Approach.

In Journal of personality assessment

Although authoritarianism has predominantly been studied among political conservatives, authoritarian individuals exist on both "poles" of the political spectrum. A 39-item multidimensional measure of left-wing authoritarianism, the Left-wing Authoritarianism Index, was recently developed to extend the study of authoritarianism to members of the far-left. The present study coupled a fully automated machine learning approach (i.e., a genetic algorithm) with multidimensional item response theory in a large, demographically representative American sample (N = 834) to generate and evaluate two abbreviated versions of the Left-wing Authoritarianism Index. We subsequently used a second community sample (N = 477) to conduct extensive validational tests of the abbreviated measures, which comprise 25- and 13-items. The abbreviated forms demonstrated remarkable convergence with the full LWA Index in terms of their psychometric (e.g., internal consistency) and distributional (e.g., mean, standard deviation, skew, kurtosis) properties. This convergence extended to virtually identical cross-measure patterns of correlations with 14 external criteria, including need for chaos, political violence, anomia, low institutional trust. In light of these results, the LWA-25 and LWA-13 scales appeared to function effectively as measures of LWA.

Costello Thomas H, Patrick Christopher J

2022-Jun-29

General General

Neuromorphic computing hardware and neural architectures for robotics.

In Science robotics

Neuromorphic hardware enables fast and power-efficient neural network-based artificial intelligence that is well suited to solving robotic tasks. Neuromorphic algorithms can be further developed following neural computing principles and neural network architectures inspired by biological neural systems. In this Viewpoint, we provide an overview of recent insights from neuroscience that could enhance signal processing in artificial neural networks on chip and unlock innovative applications in robotics and autonomous intelligent systems. These insights uncover computing principles, primitives, and algorithms on different levels of abstraction and call for more research into the basis of neural computation and neuronally inspired computing hardware.

Sandamirskaya Yulia, Kaboli Mohsen, Conradt Jorg, Celikel Tansu

2022-Jun-29

General General

Lung Ultrasound to Assist ICU Admission Decision-Making Process of COVID-19 Patients With Acute Respiratory Failure.

In Critical care explorations

** : There is only low-certainty evidence on the use of predictive models to assist COVID-19 patient's ICU admission decision-making process. Accumulative evidence suggests that lung ultrasound (LUS) assessment of COVID-19 patients allows accurate bedside evaluation of lung integrity, with the added advantage of repeatability, absence of radiation exposure, reduced risk of virus dissemination, and low cost. Our goal is to assess the performance of a quantified indicator resulting from LUS data compared with standard clinical practice model to predict critical respiratory illness in the 24 hours following hospital admission.

DESIGN : Prospective cohort study.

SETTING : Critical Care Unit from University Hospital Purpan (Toulouse, France) between July 2020 and March 2021.

PATIENTS : Adult patients for COVID-19 who were in acute respiratory failure (ARF), defined as blood oxygen saturation as measured by pulse oximetry less than 90% while breathing room air or respiratory rate greater than or equal to 30 breaths/min at hospital admission. Linear multivariate models were used to identify factors associated with critical respiratory illness, defined as death or mild/severe acute respiratory distress syndrome (Pao2/Fio2 < 200) in the 24 hours after patient's hospital admission.

INTERVENTION : LUS assessment.

MEASUREMENTS AND MAIN RESULTS : One hundred and forty COVID-19 patients with ARF were studied. This cohort was split into two independent groups: learning sample (first 70 patients) and validation sample (last 70 patients). Interstitial lung water, thickening of the pleural line, and alveolar consolidation detection were strongly associated with patient's outcome. The LUS model predicted more accurately patient's outcomes than the standard clinical practice model (DeLong test: Testing: z score = 2.50, p value = 0.01; Validation: z score = 2.11, p value = 0.03).

CONCLUSIONS : LUS assessment of COVID-19 patients with ARF at hospital admission allows a more accurate prediction of the risk of critical respiratory illness than standard clinical practice. These results hold the promise of improving ICU resource allocation process, particularly in the case of massive influx of patients or limited resources, both now and in future anticipated pandemics.

Aguersif Amazigh, Sarton Benjamine, Bouharaoua Sihem, Gaillard Lucien, Standarovski Denis, Faucoz Orphée, Martin Blondel Guillaume, Khallel Hatem, Thalamas Claire, Sommet Agnes, Riu Béatrice, Morand Eric, Bataille Benoit, Silva Stein

2022-Jun

COVID-19, acute respiratory distress syndrome, acute respiratory failure, intensive care unit admission decision-making, lung ultrasound, machine learning

Public Health Public Health

COVID-19 severity detection using machine learning techniques from CT-images.

In Evolutionary intelligence

COVID-19 has spread worldwide and the World Health Organization was forced to list it as a Public Health Emergency of International Concern. The disease has severely impacted most of the people because it affects the lung and causes severe breathing problems and lung infections. Differentiating other lung ailments from COVID-19 infection and determining the severity is a challenging process. Doctors can give vital life-saving services and support patients' lives only if the severity of their condition is determined. This work proposed a two-step approach for detecting the COVID-19 infection from the lung CT images and determining the severity of the patient's illness. To extract the features, pre-trained models are used, and by analyzing them, integrated the features from AlexNet, DenseNet-201, and ResNet-50. The COVID-19 detection is carried out by using an Artificial Neural Network(ANN) model. After the COVID-19 infection has been identified, severity detection is performed. For that, image features are combined with the clinical data and is classified as High, Moderate, Low with the help of Cubic Support Vector Machine(SVM). By considering three severity levels, patients with high risk can be given more attention. The method was tested on a publicly available dataset and obtained an accuracy of 92.0%, sensitivity of 96.0%, and an F1-Score of 91.44% for COVID-19 detection and got overall accuracy of 90.0% for COVID-19 severity detection for three classes.

Aswathy A L, Anand Hareendran S, Chandra S S Vinod

2022-Jun-24

AlexNet, Computed tomography, DenseNet-201, Neural network, ResNet-50, Transfer learning

Pathology Pathology

InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation

ArXiv Preprint

Nuclei Segmentation from histology images is a fundamental task in digital pathology analysis. However, deep-learning-based nuclei segmentation methods often suffer from limited annotations. This paper proposes a realistic data augmentation method for nuclei segmentation, named InsMix, that follows a Copy-Paste-Smooth principle and performs morphology-constrained generative instance augmentation. Specifically, we propose morphology constraints that enable the augmented images to acquire luxuriant information about nuclei while maintaining their morphology characteristics (e.g., geometry and location). To fully exploit the pixel redundancy of the background and improve the model's robustness, we further propose a background perturbation method, which randomly shuffles the background patches without disordering the original nuclei distribution. To achieve contextual consistency between original and template instances, a smooth-GAN is designed with a foreground similarity encoder (FSE) and a triplet loss. We validated the proposed method on two datasets, i.e., Kumar and CPS datasets. Experimental results demonstrate the effectiveness of each component and the superior performance achieved by our method to the state-of-the-art methods.

Yi Lin, Zeyu Wang, Kwang-Ting Cheng, Hao Chen

2022-06-30

General General

Analysis of pilots' EEG map in take-off and landing tasks.

In Biomedizinische Technik. Biomedical engineering

The take-off and landing phases are considered the critical stages of aircraft flight. To ensure flight efficiency and safety in the critical stages, this research proposes a method for analyzing and monitoring pilot flight status by β-wave. The focus of the study is β potential changes on the EEG map. First, the proportion of β-wave in the electroencephalogram (EEG) of pilots during take-off and landing increases significantly. Second, the EEG map accurately and intuitively reflects the spatial distribution of potential changes in brain regions. Finally, correlation and machine learning are used for further research of β-wave. The conclusions show that the significant changes in the β-wave caused by take-off and landing can be seen in the EEG map to identify and adjust the pilot's state. Therefore, this research provides more accurate and effective reference information (like the EEG map, correlation and machine learning) for efficient and safe flight training in the critical stages.

Ji Li, Zhang Chen, Li Haiwei, Zhang Ningning, Zheng Peng, Guo Changhao, Zhang Yong, Tang Xiaoyu

2022-Jun-28

EEG map, electroencephalogram (EEG), safe flight, take-off and landing, β-wave

Public Health Public Health

Deep learning-based integration of genetics with registry data for stratification of schizophrenia and depression.

In Science advances

Currently, psychiatric diagnoses are, in contrast to most other medical fields, based on subjective symptoms and observable signs and call for new and improved diagnostics to provide the most optimal care. On the basis of a deep learning approach, we performed unsupervised patient stratification of 19,636 patients with depression [major depressive disorder (MDD)] and/or schizophrenia (SCZ) and 22,467 population controls from the iPSYCH2012 case cohort. We integrated data of disorder severity, history of mental disorders and disease comorbidities, genetics, and medical birth data. From this, we stratified the individuals in six and seven unique clusters for MDD and SCZ, respectively. When censoring data until diagnosis, we could predict MDD clusters with areas under the curve (AUCs) of 0.54 to 0.80 and SCZ clusters with AUCs of 0.71 to 0.86. Overall cases and controls could be predicted with an AUC of 0.81, illustrating the utility of data-driven subgrouping in psychiatry.

Allesøe Rosa Lundbye, Nudel Ron, Thompson Wesley K, Wang Yunpeng, Nordentoft Merete, Børglum Anders D, Hougaard David M, Werge Thomas, Rasmussen Simon, Benros Michael Eriksen

2022-Jul

General General

Performance of a Multisensor Smart Ring to Evaluate Sleep: In-Lab and Home-Based Evaluation of Generalized and Personalized Algorithms.

In Sleep

STUDY OBJECTIVES : Wearable sleep technology has rapidly expanded across the consumer market due to advances in technology and increased interest in personalized sleep assessment to improve health and mental performance. We tested the performance of a novel device, the Happy Ring, alongside other commercial wearables (Actiwatch 2, Fitbit Charge 4, Whoop 3.0, Oura Ring V2), against in-lab polysomnography (PSG) and an at-home EEG-derived sleep monitoring device, the Dreem 2 Headband.

METHODS : 36 healthy adults with no diagnosed sleep disorders and no recent use of medications or substances known to affect sleep pattern were assessed across 77 nights. Subjects participated in a single night of in-lab PSG and 2 nights of at-home data collection. The Happy Ring includes sensors for skin conductance, movement, heart rate, and skin temperature. The Happy Ring utilized two machine-learning derived scoring algorithms: a "generalized" algorithm that applied broadly to all users, and a "personalized" algorithm that adapted to individual subjects' data. Epoch-by-epoch analyses compared the wearable devices to in-lab PSG and to at-home EEG Headband.

RESULTS : Compared to in-lab PSG, the "generalized" and "personalized" algorithms demonstrated good sensitivity (94% and 93%, respectively) and specificity (70% and 83%, respectively). The Happy Personalized model demonstrated a lower bias and more narrow limits of agreement across Bland-Altman measures.

CONCLUSION : The Happy Ring performed well at home and in the lab, especially regarding sleep/wake detection. The personalized algorithm demonstrated improved detection accuracy over the generalized approach and other devices, suggesting that adaptable, dynamic algorithms can enhance sleep detection accuracy.

Grandner Michael A, Bromberg Zohar, Hadley Aaron, Morrell Zoe, Graf Arnulf, Hutchison Stephen, Freckleton Dustin

2022-Jun-29

actigraphy, polysomnography, sensors, sleep technology, validation, wearables

General General

Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning.

In PLoS computational biology

A major challenge to the characterization of intrinsically disordered regions (IDRs), which are widespread in the proteome, but relatively poorly understood, is the identification of molecular features that mediate functions of these regions, such as short motifs, amino acid repeats and physicochemical properties. Here, we introduce a proteome-scale feature discovery approach for IDRs. Our approach, which we call "reverse homology", exploits the principle that important functional features are conserved over evolution. We use this as a contrastive learning signal for deep learning: given a set of homologous IDRs, the neural network has to correctly choose a held-out homolog from another set of IDRs sampled randomly from the proteome. We pair reverse homology with a simple architecture and standard interpretation techniques, and show that the network learns conserved features of IDRs that can be interpreted as motifs, repeats, or bulk features like charge or amino acid propensities. We also show that our model can be used to produce visualizations of what residues and regions are most important to IDR function, generating hypotheses for uncharacterized IDRs. Our results suggest that feature discovery using unsupervised neural networks is a promising avenue to gain systematic insight into poorly understood protein sequences.

Lu Alex X, Lu Amy X, Pritišanac Iva, Zarin Taraneh, Forman-Kay Julie D, Moses Alan M

2022-Jun-29

General General

Three-Dimensional Printed Bimodal Electronic Skin with High Resolution and Breathability for Hair Growth.

In ACS applied materials & interfaces ; h5-index 147.0

People with neurological deficits face difficulties perceiving their surroundings, resulting in an urgent need for wearable electronic skin (e-skin) that can monitor external stimuli and temperature changes. However, the monolithic structure of e-skin is not conducive to breathability and hinders hair growth, limiting its wearing comfort. In this work, we prepared fully three-dimensional (3D) printed e-skin that allowed hair penetration and growth. This e-skin also achieved simultaneous pressure and temperature detection and a high tactile resolution of 100 cm-2, which is close to that of human fingertips. The temperature sensor maintained linear measurements within 10-60 °C. The pore microstructure prepared by a sacrificial template method helped the pressure-sensing unit achieve a high sensitivity of 0.213 kPa-1. Considering the distribution of human hair, the design of the main structure of the e-skin was studied to realize hair penetration and growth. High-performance pressure-sensitive inks and transparent flexible substrate inks for 3D printing were developed, and e-skins combining these functions were realized through multimaterial in situ 3D printing with high accuracy and high consistency. The temperature and pressure sensors separately performed simultaneous detection without interference, and the tactile sensor array accurately identified stimuli at different locations.

Sang Shengbo, Pei Zhen, Zhang Fan, Ji Chao, Li Qiang, Ji Jianlong, Yang Kun, Zhang Qiang

2022-Jun-29

3D printing, electronic skin, hair penetrable, high resolution, porous structure, pressure−temperature bimodal sensing

General General

Novel extreme regression-voting classifier to predict death risk in vaccinated people using VAERS data.

In PloS one ; h5-index 176.0

COVID-19 vaccination raised serious concerns among the public and people are mind stuck by various rumors regarding the resulting illness, adverse reactions, and death. Such rumors are dangerous to the campaign against the COVID-19 and should be dealt with accordingly and timely. One prospective solution is to use machine learning-based models to predict the death risk for vaccinated people and clarify people's perceptions regarding death risk. This study focuses on the prediction of the death risks associated with vaccinated people followed by a second dose for two reasons; first to build consensus among people to get the vaccines; second, to reduce the fear regarding vaccines. Given that, this study utilizes the COVID-19 VAERS dataset that records adverse events after COVID-19 vaccination as 'recovered', 'not recovered', and 'survived'. To obtain better prediction results, a novel voting classifier extreme regression-voting classifier (ER-VC) is introduced. ER-VC ensembles extra tree classifier and logistic regression using soft voting criterion. To avoid model overfitting and get better results, two data balancing techniques synthetic minority oversampling (SMOTE) and adaptive synthetic sampling (ADASYN) have been applied. Moreover, three feature extraction techniques term frequency-inverse document frequency (TF-IDF), bag of words (BoW), and global vectors (GloVe) have been used for comparison. Both machine learning and deep learning models are deployed for experiments. Results obtained from extensive experiments reveal that the proposed model in combination with TF-TDF has shown robust results with a 0.85 accuracy when trained on the SMOTE-balanced dataset. In line with this, validation of the proposed voting classifier on binary classification shows state-of-the-art results with a 0.98 accuracy. Results show that machine learning models can predict the death risk with high accuracy and can assist the authors in taking timely measures.

Saad Eysha, Sadiq Saima, Jamil Ramish, Rustam Furqan, Mehmood Arif, Choi Gyu Sang, Ashraf Imran

2022

General General

Robo-advisor acceptance: Do gender and generation matter?

In PloS one ; h5-index 176.0

Robo-advice technology refers to services offered by a virtual financial advisor based on artificial intelligence. Research on the application of robo-advice technology already highlights the potential benefit in terms of financial inclusion. We analyze the process for adopting robo-advice through the technology acceptance model (TAM), focusing on a highly educated sample and exploring generational and gender differences. We find no significant gender difference in the causality links with adoption, although some structural differences still arise between male and female groups. Further, we find evidence that generational cohorts affect the path to future adoption of robo-advice technology. Indeed, the ease of use is the factor which triggers the adoption by Generation Z and Generation Y, whereas the perceived usefulness of robo-advice technology is the key factor driving Generation X+, who need to understand the ultimate purpose of a robo-advice technology tool before adopting it. Overall, the above findings may reflect that, while gender differences are wiped out in a highly educated population, generation effects still matter in the adoption of a robo-advice technology tool.

Figà-Talamanca Gianna, Tanzi Paola Musile, D’Urzo Eleonora

2022

General General

Set the tone: Trustworthy and dominant novel voices classification using explicit judgement and machine learning techniques.

In PloS one ; h5-index 176.0

Prior research has established that valence-trustworthiness and power-dominance are the two main dimensions of voice evaluation at zero-acquaintance. These impressions shape many of our interactions and high-impact decisions, so it is crucial for many domains to understand this dynamic. Yet, the relationship between acoustical properties of novel voices and personality/attitudinal traits attributions remains poorly understood. The fundamental problem of understanding vocal impressions and relative decision-making is linked to the complex nature of the acoustical properties in voices. In order to disentangle this relationship, this study extends the line of research on the acoustical bases of vocal impressions in two ways. First, by attempting to replicate previous finding on the bi-dimensional nature of first impressions: using personality judgements and establishing a correspondence between acoustics and voice-first-impression (VFI) dimensions relative to sex (Study 1). Second (Study 2), by exploring the non-linear relationships between acoustical parameters and VFI by the means of machine learning models. In accordance with literature, a bi-dimensional projection comprising valence-trustworthiness and power-dominance evaluations is found to explain 80% of the VFI. In study 1, brighter (high center of gravity), smoother (low shimmers), and louder (high minimum intensity) voices reflected trustworthiness, while vocal roughness (harmonic to noise-ratio), energy in the high frequencies (Energy3250), pitch (Quantile 1, Quantile 5) and lower range of pitch values reflected dominance. In study 2, above chance classification of vocal profiles was achieved by both Support Vector Machine (77.78%) and Random-Forest (Out-Of-Bag = 36.14) classifiers, generally confirming that machine learning algorithms could predict first impressions from voices. Hence results support a bi-dimensional structure to VFI, emphasize the usefulness of machine learning techniques in understanding vocal impressions, and shed light on the influence of sex on VFI formation.

Chappuis Cyrielle, Grandjean Didier

2022

Pathology Pathology

A Self Supervised StyleGAN for Image Annotation and Classification with Extremely Limited Labels.

In IEEE transactions on medical imaging ; h5-index 74.0

The recent success of learning-based algorithms can be greatly attributed to the immense amount of annotated data used for training. Yet, many datasets lack annotations due to the high costs associated with labeling, resulting in degraded performances of deep learning methods. Self-supervised learning is frequently adopted to mitigate the reliance on massive labeled datasets since it exploits unlabeled data to learn relevant feature representations. In this work, we propose SS-StyleGAN, a self-supervised approach for image annotation and classification suitable for extremely small annotated datasets. This novel framework adds self-supervision to the StyleGAN architecture by integrating an encoder that learns the embedding to the StyleGAN latent space, which is well-known for its disentangled properties. The learned latent space enables the smart selection of representatives from the data to be labeled for improved classification performance. We show that the proposed method attains strong classification results using small labeled datasets of sizes 50 and even 10. We demonstrate the superiority of our approach for the tasks of COVID-19 and liver tumor pathology identification.

Hochberg Dana Cohen, Greenspan Hayit, Giryes Raja

2022-Jun-29

General General

Multiview Deep Anomaly Detection: A Systematic Exploration.

In IEEE transactions on neural networks and learning systems

Anomaly detection (AD), which models a given normal class and distinguishes it from the rest of abnormal classes, has been a long-standing topic with ubiquitous applications. As modern scenarios often deal with massive high-dimensional complex data spawned by multiple sources, it is natural to consider AD from the perspective of multiview deep learning. However, it has not been formally discussed by the literature and remains underexplored. Motivated by this blank, this article makes fourfold contributions: First, to the best of our knowledge, this is the first work that formally identifies and formulates the multiview deep AD problem. Second, we take recent advances in relevant areas into account and systematically devise various baseline solutions, which lays the foundation for multiview deep AD research. Third, to remedy the problem that limited benchmark datasets are available for multiview deep AD, we extensively collect the existing public data and process them into more than 30 multiview benchmark datasets via multiple means, so as to provide a better evaluation platform for multiview deep AD. Finally, by comprehensively evaluating the devised solutions on different types of multiview deep AD benchmark datasets, we conduct a thorough analysis on the effectiveness of the designed baselines and hopefully provide other researchers with beneficial guidance and insight into the new multiview deep AD topic.

Wang Siqi, Liu Jiyuan, Yu Guang, Liu Xinwang, Zhou Sihang, Zhu En, Yang Yuexiang, Yin Jianping, Yang Wenjing

2022-Jun-29

General General

Deep transfer learning for the recognition of types of face masks as a core measure to prevent the transmission of COVID-19.

In Applied soft computing

The use of face masks in public places has emerged as one of the most effective non-pharmaceutical measures to lower the spread of COVID-19 infection. This has led to the development of several detection systems for identifying people who do not wear a face mask. However, not all face masks or coverings are equally effective in preventing virus transmission or illness caused by viruses and therefore, it appears important for those systems to incorporate the ability to distinguish between the different types of face masks. This paper implements four pre-trained deep transfer learning models (NasNetMobile, MobileNetv2, ResNet101v2, and ResNet152v2) to classify images based on the type of face mask (KN95, N95, surgical and cloth) worn by people. Experimental results indicate that the deep residual networks (ResNet101v2 and ResNet152v2) provide the best performance with the highest accuracy and the lowest loss.

Mar-Cupido Ricardo, García Vicente, Rivera Gilberto, Sánchez J Salvador

2022-Jun-23

COVID-19, Deep learning, Face mask, Recognition, Transfer learning

General General

A Semi-Supervised Learning Approach for COVID-19 Detection from Chest CT Scans.

In Neurocomputing

COVID-19 has spread rapidly all over the world and has infected more than 200 countries and regions. Early screening of suspected infected patients is essential for preventing and combating COVID-19. Computed Tomography (CT) is a fast and efficient tool which can quickly provide chest scan results. To reduce the burden on doctors of reading CTs, in this article, a high precision diagnosis algorithm of COVID-19 from chest CTs is designed for intelligent diagnosis. A semi-supervised learning approach is developed to solve the problem when only small amount of labelled data is available. While following the MixMatch rules to conduct sophisticated data augmentation, we introduce a model training technique to reduce the risk of model over-fitting. At the same time, a new data enhancement method is proposed to modify the regularization term in MixMatch. To further enhance the generalization of the model, a convolutional neural network based on an attention mechanism is then developed that enables to extract multi-scale features on CT scans. The proposed algorithm is evaluated on an independent CT dataset of the chest from COVID-19 and achieves the area under the receiver operating characteristic curve (AUC) value of 0.932, accuracy of 90.1%, sensitivity of 91.4%, specificity of 88.9%, and F1-score of 89.9%. The results show that the proposed algorithm can accurately diagnose whether a chest CT belongs to a positive or negative indication of COVID-19, and can help doctors to diagnose rapidly in the early stages of a COVID-19 outbreak.

Zhang Yong, Su Li, Liu Zhenxing, Tan Wei, Jiang Yinuo, Cheng Cheng

2022-Jun-23

Attention mechanisms, COVID-19, Computed tomography, Deep learning, Semi-supervised learning

Pathology Pathology

Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathology

ArXiv Preprint

When designing a diagnostic model for a clinical application, it is crucial to guarantee the robustness of the model with respect to a wide range of image corruptions. Herein, an easy-to-use benchmark is established to evaluate how deep neural networks perform on corrupted pathology images. Specifically, corrupted images are generated by injecting nine types of common corruptions into validation images. Besides, two classification and one ranking metrics are designed to evaluate the prediction and confidence performance under corruption. Evaluated on two resulting benchmark datasets, we find that (1) a variety of deep neural network models suffer from a significant accuracy decrease (double the error on clean images) and the unreliable confidence estimation on corrupted images; (2) A low correlation between the validation and test errors while replacing the validation set with our benchmark can increase the correlation. Our codes are available on https://github.com/superjamessyx/robustness_benchmark.

Yunlong Zhang, Yuxuan Sun, Honglin Li, Sunyi Zheng, Chenglu Zhu, Lin Yang

2022-06-30

General General

Self-Supervised Self-Organizing Clustering Network: A Novel Unsupervised Representation Learning Method.

In IEEE transactions on neural networks and learning systems

Deep learning-based clustering methods usually regard feature extraction and feature clustering as two independent steps. In this way, the features of all images need to be extracted before feature clustering, which consumes a lot of calculation. Inspired by the self-organizing map network, a self-supervised self-organizing clustering network (S 3 OCNet) is proposed to jointly learn feature extraction and feature clustering, thus realizing a single-stage clustering method. In order to achieve joint learning, we propose a self-organizing clustering header (SOCH), which takes the weight of the self-organizing layer as the cluster centers, and the output of the self-organizing layer as the similarities between the feature and the cluster centers. In order to optimize our network, we first convert the similarities into probabilities which represents a soft cluster assignment, and then we obtain a target for self-supervised learning by transforming the soft cluster assignment into a hard cluster assignment, and finally we jointly optimize backbone and SOCH. By setting different feature dimensions, a Multilayer SOCHs strategy is further proposed by cascading SOCHs. This strategy achieves clustering features in multiple clustering spaces. S 3 OCNet is evaluated on widely used image classification benchmarks such as Canadian Institute For Advanced Research (CIFAR)-10, CIFAR-100, Self-Taught Learning (STL)-10, and Tiny ImageNet. Experimental results show that our method significant improvement over other related methods. The visualization of features and images shows that our method can achieve good clustering results.

Li Shuo, Liu Fang, Jiao Licheng, Chen Puhua, Li Lingling

2022-Jun-29

Internal Medicine Internal Medicine

Automatic International Classification of Diseases Coding System: Deep Contextualized Language Model With Rule-Based Approaches.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : The tenth revision of the International Classification of Diseases (ICD-10) is widely used for epidemiological research and health management. The clinical modification (CM) and procedure coding system (PCS) of ICD-10 were developed to describe more clinical details with increasing diagnosis and procedure codes and applied in disease-related groups for reimbursement. The expansion of codes made the coding time-consuming and less accurate. The state-of-the-art model using deep contextual word embeddings was used for automatic multilabel text classification of ICD-10. In addition to input discharge diagnoses (DD), the performance can be improved by appropriate preprocessing methods for the text from other document types, such as medical history, comorbidity and complication, surgical method, and special examination.

OBJECTIVE : This study aims to establish a contextual language model with rule-based preprocessing methods to develop the model for ICD-10 multilabel classification.

METHODS : We retrieved electronic health records from a medical center. We first compared different word embedding methods. Second, we compared the preprocessing methods using the best-performing embeddings. We compared biomedical bidirectional encoder representations from transformers (BioBERT), clinical generalized autoregressive pretraining for language understanding (Clinical XLNet), label tree-based attention-aware deep model for high-performance extreme multilabel text classification (AttentionXLM), and word-to-vector (Word2Vec) to predict ICD-10-CM. To compare different preprocessing methods for ICD-10-CM, we included DD, medical history, and comorbidity and complication as inputs. We compared the performance of ICD-10-CM prediction using different preprocesses, including definition training, external cause code removal, number conversion, and combination code filtering. For the ICD-10 PCS, the model was trained using different combinations of DD, surgical method, and key words of special examination. The micro F1 score and the micro area under the receiver operating characteristic curve were used to compare the model's performance with that of different preprocessing methods.

RESULTS : BioBERT had an F1 score of 0.701 and outperformed other models such as Clinical XLNet, AttentionXLM, and Word2Vec. For the ICD-10-CM, the model had an F1 score that significantly increased from 0.749 (95% CI 0.744-0.753) to 0.769 (95% CI 0.764-0.773) with the ICD-10 definition training, external cause code removal, number conversion, and combination code filter. For the ICD-10-PCS, the model had an F1 score that significantly increased from 0.670 (95% CI 0.663-0.678) to 0.726 (95% CI 0.719-0.732) with a combination of discharge diagnoses, surgical methods, and key words of special examination. With our preprocessing methods, the model had the highest area under the receiver operating characteristic curve of 0.853 (95% CI 0.849-0.855) and 0.831 (95% CI 0.827-0.834) for ICD-10-CM and ICD-10-PCS, respectively.

CONCLUSIONS : The performance of our model with the pretrained contextualized language model and rule-based preprocessing method is better than that of the state-of-the-art model for ICD-10-CM or ICD-10-PCS. This study highlights the importance of rule-based preprocessing methods based on coder coding rules.

Chen Pei-Fu, Chen Kuan-Chih, Liao Wei-Chih, Lai Feipei, He Tai-Liang, Lin Sheng-Che, Chen Wei-Jen, Yang Chi-Yu, Lin Yu-Cheng, Tsai I-Chang, Chiu Chi-Hao, Chang Shu-Chih, Hung Fang-Ming

2022-Jun-29

International Classification of Diseases, algorithm, coding system, data mining, deep learning, electronic health record, medical records, multilabel text classification, natural language processing

General General

Enhancing Mechanical Metamodels with a Generative Model-Based Augmented Training Dataset.

In Journal of biomechanical engineering ; h5-index 32.0

Modeling biological soft tissue is complex in part due to material heterogeneity. Microstructural patterns, which play a major role in defining the mechanical behavior of these tissues, are both challenging to characterize, and difficult to simulate. Recently, machine learning (ML)-based methods to predict the mechanical behavior of heterogeneous materials have made it possible to more thoroughly explore the massive input parameter space associated with heterogeneous blocks of material. Specifically, we can train ML models to closely approximate computationally expensive heterogeneous material simulations where the ML model is trained on datasets of simulations with relevant spatial heterogeneity. However, when it comes to applying these techniques to tissue, there is a major limitation: the number of useful examples available to characterize the input domain under study is often limited. In this work, we investigate the efficacy of both ML-based generative models and procedural methods as tools for augmenting limited input pattern datasets. We find that a Style-based Generative Adversarial Network with an adaptive discriminator augmentation mechanism is able to successfully leverage just 1,000 example patterns to create authentic generated patterns. And, we find that diverse generated patterns with adequate resemblance to real patterns can be used as inputs to finite element simulations to meaningfully augment the training dataset. To enable this methodological contribution, we have created an open access Finite Element Analysis simulation dataset based on Cahn-Hilliard patterns. We anticipate that future researchers will be able to leverage this dataset and build on the work presented here.

Kobeissi Hiba, Mohammadzadeh Saeed, Lejeune Emma

2022-Jun-29

General General

Pretrained Transformer Language Models Versus Pretrained Word Embeddings for the Detection of Accurate Health Information on Arabic Social Media: Comparative Study.

In JMIR formative research

BACKGROUND : In recent years, social media has become a major channel for health-related information in Saudi Arabia. Prior health informatics studies have suggested that a large proportion of health-related posts on social media are inaccurate. Given the subject matter and the scale of dissemination of such information, it is important to be able to automatically discriminate between accurate and inaccurate health-related posts in Arabic.

OBJECTIVE : The first aim of this study is to generate a data set of generic health-related tweets in Arabic, labeled as either accurate or inaccurate health information. The second aim is to leverage this data set to train a state-of-the-art deep learning model for detecting the accuracy of health-related tweets in Arabic. In particular, this study aims to train and compare the performance of multiple deep learning models that use pretrained word embeddings and transformer language models.

METHODS : We used 900 health-related tweets from a previously published data set extracted between July 15, 2019, and August 31, 2019. Furthermore, we applied a pretrained model to extract an additional 900 health-related tweets from a second data set collected specifically for this study between March 1, 2019, and April 15, 2019. The 1800 tweets were labeled by 2 physicians as accurate, inaccurate, or unsure. The physicians agreed on 43.3% (779/1800) of tweets, which were thus labeled as accurate or inaccurate. A total of 9 variations of the pretrained transformer language models were then trained and validated on 79.9% (623/779 tweets) of the data set and tested on 20% (156/779 tweets) of the data set. For comparison, we also trained a bidirectional long short-term memory model with 7 different pretrained word embeddings as the input layer on the same data set. The models were compared in terms of their accuracy, precision, recall, F1 score, and macroaverage of the F1 score.

RESULTS : We constructed a data set of labeled tweets, 38% (296/779) of which were labeled as inaccurate health information, and 62% (483/779) of which were labeled as accurate health information. We suggest that this was highly efficacious as we did not include any tweets in which the physician annotators were unsure or in disagreement. Among the investigated deep learning models, the Transformer-based Model for Arabic Language Understanding version 0.2 (AraBERTv0.2)-large model was the most accurate, with an F1 score of 87%, followed by AraBERT version 2-large and AraBERTv0.2-base.

CONCLUSIONS : Our results indicate that the pretrained language model AraBERTv0.2 is the best model for classifying tweets as carrying either inaccurate or accurate health information. Future studies should consider applying ensemble learning to combine the best models as it may produce better results.

Albalawi Yahya, Nikolov Nikola S, Buckley Jim

2022-Jun-29

BERT, bidirectional encoder representations from transformers, deep learning, health informatics, health information, infodemiology, language model, machine learning, misinformation, pretrained language models, social media, tweets

Pathology Pathology

Logistic Regression-Based Model Is More Efficient Than U-Net Model for Reliable Whole Brain Magnetic Resonance Imaging Segmentation.

In Topics in magnetic resonance imaging : TMRI

OBJECTIVES : Automated whole brain segmentation from magnetic resonance images is of great interest for the development of clinically relevant volumetric markers for various neurological diseases. Although deep learning methods have demonstrated remarkable potential in this area, they may perform poorly in nonoptimal conditions, such as limited training data availability. Manual whole brain segmentation is an incredibly tedious process, so minimizing the data set size required for training segmentation algorithms may be of wide interest. The purpose of this study was to compare the performance of the prototypical deep learning segmentation architecture (U-Net) with a previously published atlas-free traditional machine learning method, Classification using Derivative-based Features (C-DEF) for whole brain segmentation, in the setting of limited training data.

MATERIALS AND METHODS : C-DEF and U-Net models were evaluated after training on manually curated data from 5, 10, and 15 participants in 2 research cohorts: (1) people living with clinically diagnosed HIV infection and (2) relapsing-remitting multiple sclerosis, each acquired at separate institutions, and between 5 and 295 participants' data using a large, publicly available, and annotated data set of glioblastoma and lower grade glioma (brain tumor segmentation). Statistics was performed on the Dice similarity coefficient using repeated-measures analysis of variance and Dunnett-Hsu pairwise comparison.

RESULTS : C-DEF produced better segmentation than U-Net in lesion (29.2%-38.9%) and cerebrospinal fluid (5.3%-11.9%) classes when trained with data from 15 or fewer participants. Unlike C-DEF, U-Net showed significant improvement when increasing the size of the training data (24%-30% higher than baseline). In the brain tumor segmentation data set, C-DEF produced equivalent or better segmentations than U-Net for enhancing tumor and peritumoral edema regions across all training data sizes explored. However, U-Net was more effective than C-DEF for segmentation of necrotic/non-enhancing tumor when trained on 10 or more participants, probably because of the inconsistent signal intensity of the tissue class.

CONCLUSIONS : These results demonstrate that classical machine learning methods can produce more accurate brain segmentation than the far more complex deep learning methods when only small or moderate amounts of training data are available (n ≤ 15). The magnitude of this advantage varies by tissue and cohort, while U-Net may be preferable for deep gray matter and necrotic/non-enhancing tumor segmentation, particularly with larger training data sets (n ≥ 20). Given that segmentation models often need to be retrained for application to novel imaging protocols or pathology, the bottleneck associated with large-scale manual annotation could be avoided with classical machine learning algorithms, such as C-DEF.

Dieckhaus Henry, Meijboom Rozanna, Okar Serhat, Wu Tianxia, Parvathaneni Prasanna, Mina Yair, Chandran Siddharthan, Waldman Adam D, Reich Daniel S, Nair Govind

2022-Jun-01

General General

Impact of Deep Learning Architectures on Accelerated Cardiac T1 Mapping using MyoMapNet.

In NMR in biomedicine ; h5-index 41.0

OBJECTIVE : To investigate the performance of various deep learning (DL) architectures for MyoMapNet, a DL model for T1 estimation using accelerated cardiac T1 mapping from four T1 -weighted images collected after a single inversion pulse (Look-Locker, LL4).

METHODS : We implemented and tested three DL architectures for MyoMapNet: (a) a fully connected neural network (FC), (b) convolutional neural networks (VGG19, ResNet50), and (c) encoder-decoder networks with skip connections (ResUNet, U-Net). Modified Look-Locker Inversion Recovery (MOLLI) images from 749 patients at 3T were used for training, validation, and testing. The first four T1 -weighted images from MOLLI5(3)3 and/or MOLLI4(1)3(1)2 protocols were extracted to create accelerated cardiac T1 mapping data. We also prospectively collected data from 28 subjects using MOLLI and LL4 to further evaluate model performance.

RESULTS : Despite rigorous training, conventional VGG19 and ResNet50 models failed to produce anatomically correct T1 maps, and T1 values had significant errors. While ResUNet yielded good quality maps, it significantly under-estimated T1 . Both FC and U-Net, however, yielded excellent image quality with good T1 accuracy for both native (FC/U-Net/MOLLI= 1217 ± 64/ 1208 ± 61/1199 ± 61 ms, all P<0.05) and post-contrast myocardial T1 (FC/U-Net/MOLLI= 578 ± 57/ 567 ± 54/574 ± 55 ms, all P<0.05). In terms of precision, the U-Net model yielded better T1 precision compared to the FC architecture (standard deviation of 61 ms vs. 67 ms for the myocardium for native (P<0.05), and 31 ms vs. 38 ms (P<0.05), for post-contrast). Similar findings were observed in prospectively collected LL4 data.

CONCLUSION : U-Net and FC DL models in MyoMapNet enable fast myocardial T1 mapping using only four T1 -weighted images collected from a single Lock-Locker sequence with comparable accuracy. U-Net also provides slight improvement in precision.

Amyar Amine, Guo Rui, Cai Xiaoying, Assana Salah, Chow Kelvin, Rodriguez Jennifer, Yankama Tuyen, Cirillo Julia, Pierce Patrick, Goddu Beth, Ngo Long, Nezafat Reza

2022-Jun-29

Cardiac MRI, Deep learning, Inversion-recovery cardiac T1 mapping, Myocardial tissue characterization

General General

Molecular subtypes classification of low-grade gliomas patients using MRI-based radiomics and machine learning.

In NMR in biomedicine ; h5-index 41.0

Since 2016, the World Health Organization (WHO) has updated glioma classification by incorporating molecular biology parameters, including low-grade glioma (LGG). In the new scheme, LGGs have three molecular subtypes: IDH-mutated 1p/19q-codeleted, IDH-mutated 1p/19q-non-codeleted, and IDH-wildtype 1p/19q-non-codeleted entities. This work proposes a model prediction of LGG molecular subtypes using Magnetic Resonance Imaging (MRI). MRIs were segmented and converted into radiomics features, thereby providing predictive information about the brain tumor classification. With 726 raw features obtained from the feature extraction procedure, we developed a hybrid machine learning-based radiomics by incorporating a genetic algorithm and eXtreme Gradient Boosting (XGBoost) classifier, to find out 12 optimal features for the tumor classification. To resolve imbalance data, the Synthetic Minority Oversampling Technique (SMOTE) was applied in our study. The XGBoost algorithm outperformed the other algorithms on the training dataset by accuracy value of 0.885. We continued evaluating the XGBoost model, then achieved an overall accuracy of 0.6905 for the three-subtype classification of LGG on external validation dataset. Our model is among the few ones that resolved the three-subtype classification challenge of LGG with a high result compared to previous studies that did the same work.

Lam Luu Ho Thanh, Do Duyen Thi, Diep Doan Thi Ngoc, Nguyet Dang Le Nhu, Truong Quang Dinh, Tri Tran Thanh, Thanh Huynh Ngoc, Le Nguyen Quoc Khanh

2022-Jun-29

General General

Pain avoidance and functional connectivity between insula and amygdala identifies suicidal attempters in patients with major depressive disorder using machine learning.

In Psychophysiology

Pain avoidance can effectively classify suicide attempters from non-attempters among patients with major depressive disorder (MDD). However, the neural circuits underlying pain processing in suicide attempters have not been described comprehensively. In Study 1, we recruited MDD patients with a history of suicide attempts (MDD-SA), and those without (MDD-NSA) to examine the patterns of psychological pain using the latent profile analysis. Further, in Study 2, participants including the MDD-SA, MDD-NSA, and healthy controls underwent resting-state functional magnetic resonance imaging. We used machine learning that included features of gray matter volume (GMV), the functional connectivity (FC) brain patterns of the region of interest, and behavioral data to identify suicide attempters. The results identified three latent classes of psychological pain in MDD patients: the low pain class (18.9%), the painful feeling class (37.2%), and the pain avoidance class (43.9%). Furthermore, the proportion of suicide attempters with high pain avoidance was the highest. The accuracy of multimodality classifiers (63%-92%) was significantly higher than that of brain-only classifiers (56%-85%) and behavior-only classifiers (64%-73%). Pain avoidance ranked first in the optimal feature set of the suicide attempt classification model. The crucial brain imaging features were FC between the left amygdala and right insula, right orbitofrontal and left thalamus, left anterior cingulate cortex and left insula, right orbitofrontal, amygdala, and the GMV of right thalamus. Additionally, the optimal feature set, including pain avoidance and crucial brain patterns of psychological pain neural circuits, was provided for the identification of suicide attempters.

Hao Ziyu, Li Huanhuan, Ouyang Lisheng, Sun Fang, Wen Xiaotong, Wang Xiang

2022-Jun-29

fMRI, functional connectivity, gray matter volume, suicide attempts, three-dimensional psychological pain

General General

The Interfield Strength Agreement of Left Ventricular Strain Measurements at 1.5 T and 3 T Using Cardiac MRI Feature Tracking.

In Journal of magnetic resonance imaging : JMRI

BACKGROUND : Left ventricular (LV) strain measurements can be derived using cardiac MRI from routinely acquired balanced steady-state free precession (bSSFP) cine images.

PURPOSE : To compare the interfield strength agreement of global systolic strain, peak strain rates and artificial intelligence (AI) landmark-based global longitudinal shortening at 1.5 T and 3 T.

STUDY TYPE : Prospective.

SUBJECTS : A total of 22 healthy individuals (mean age 36 ± 12 years; 45% male) completed two cardiac MRI scans at 1.5 T and 3 T in a randomized order within 30 minutes. FIELD STRENGTH/SEQUENCE: bSSFP cine images at 1.5 T and 3 T.

ASSESSMENT : Two software packages, Tissue Tracking (cvi42, Circle Cardiovascular Imaging) and QStrain (Medis Suite, Medis Medical Imaging Systems), were used to derive LV global systolic strain in the longitudinal, circumferential and radial directions and peak (systolic, early diastolic, and late diastolic) strain rates. Global longitudinal shortening and mitral annular plane systolic excursion (MAPSE) were measured using an AI deep neural network model.

STATISTICAL TESTS : Comparisons between field strengths were performed using Wilcoxon signed-rank test (P value < 0.05 considered statistically significant). Agreement was determined using intraclass correlation coefficients (ICCs) and Bland-Altman plots.

RESULTS : Minimal bias was seen in all strain and strain rate measurements between field strengths. Using Tissue Tracking, strain and strain rate values derived from long-axis images showed poor to fair agreement (ICC range 0.39-0.71), whereas global longitudinal shortening and MAPSE showed good agreement (ICC = 0.81 and 0.80, respectively). Measures derived from short-axis images showed good to excellent agreement (ICC range 0.78-0.91). Similar results for the agreement of strain and strain rate measurements were observed with QStrain.

CONCLUSION : The interfield strength agreement of short-axis derived LV strain and strain rate measurements at 1.5 T and 3 T was better than those derived from long-axis images; however, the agreement of global longitudinal shortening and MAPSE was good.

EVIDENCE LEVEL : 2 TECHNICAL EFFICACY: Stage 2.

Ayton Sarah L, Alfuhied Aseel, Gulsin Gaurav S, Parke Kelly S, Wormleighton Joanne V, Arnold J Ranjit, Moss Alastair J, Singh Anvesha, Xue Hui, Kellman Peter, Graham-Brown Matthew P M, McCann Gerry P

2022-Jun-29

cardiac MRI, field strength, left ventricular strain, myocardial deformation analysis

General General

Adaptive data-driven models to best predict the likelihood of live birth as the IVF cycle moves on and for each embryo transfer.

In Journal of assisted reproduction and genetics ; h5-index 39.0

PURPOSE : To dynamically assess the evolution of live birth predictive factors' impact throughout the in vitro fertilization (IVF) process, for each fresh and subsequent frozen embryo transfers.

METHODS : In this multicentric study, data from 13,574 fresh IVF cycles and 6,770 subsequent frozen embryo transfers were retrospectively analyzed. Fifty-seven descriptive parameters were included and split into four categories: (1) demographic (couple's baseline characteristics), (2) ovarian stimulation, (3) laboratory data, and (4) embryo transfer (fresh and frozen). All these parameters were used to develop four successive predictive models with the outcome being a live birth event.

RESULTS : Eight parameters were predictive of live birth in the first step after the first consultation, 9 in the second step after the stimulation, 11 in the third step with laboratory data, and 13 in the 4th step at the transfer stage. The predictive performance of the models increased at each step. Certain parameters remained predictive in all 4 models while others were predictive only in the first models and no longer in the subsequent ones when including new parameters. Moreover, some parameters were predictive in fresh transfers but not in frozen transfers.

CONCLUSION : This work evaluates the chances of live birth for each embryo transfer individually and not the cumulative outcome after multiple IVF attempts. The different predictive models allow to determine which parameters should be taken into account or not at each step of an IVF cycle, and especially at the time of each embryo transfer, fresh or frozen.

Grzegorczyk-Martin Véronika, Roset Julie, Di Pizio Pierre, Fréour Thomas, Barrière Paul, Pouly Jean Luc, Grynberg Michael, Parneix Isabelle, Avril Catherine, Pacheco Joe, Grzegorczyk Tomasz M

2022-Jun-29

In vitro fertilization, Live birth, Predictive factors, Predictive models

General General

A novel model to predict severe COVID-19 and mortality using an artificial intelligence algorithm to interpret chest radiographs and clinical variables.

In ERJ open research

Background : Patients with coronavirus disease 2019 (COVID-19) could develop severe disease requiring admission to the intensive care unit (ICU). This article presents a novel method that predicts whether a patient will need admission to the ICU and assesses the risk of in-hospital mortality by training a deep-learning model that combines a set of clinical variables and features in chest radiographs.

Methods : This was a prospective diagnostic test study. Patients with confirmed severe acute respiratory syndrome coronavirus 2 infection between March 2020 and January 2021 were included. This study was designed to build predictive models obtained by training convolutional neural networks for chest radiograph images using an artificial intelligence (AI) tool and a random forest analysis to identify critical clinical variables. Then, both architectures were connected and fine-tuned to provide combined models.

Results : 2552 patients were included in the clinical cohort. The variables independently associated with ICU admission were age, fraction of inspired oxygen (F iO2 ) on admission, dyspnoea on admission and obesity. Moreover, the variables associated with hospital mortality were age, F iO2 on admission and dyspnoea. When implementing the AI model to interpret the chest radiographs and the clinical variables identified by random forest, we developed a model that accurately predicts ICU admission (area under the curve (AUC) 0.92±0.04) and hospital mortality (AUC 0.81±0.06) in patients with confirmed COVID-19.

Conclusions : This automated chest radiograph interpretation algorithm, along with clinical variables, is a reliable alternative to identify patients at risk of developing severe COVID-19 who might require admission to the ICU.

Munera Nicolás, Garcia-Gallo Esteban, Gonzalez Álvaro, Zea José, Fuentes Yuli V, Serrano Cristian, Ruiz-Cuartas Alejandra, Rodriguez Alejandro, Reyes Luis F

2022-Apr

General General

Lung Ultrasound to Assist ICU Admission Decision-Making Process of COVID-19 Patients With Acute Respiratory Failure.

In Critical care explorations

** : There is only low-certainty evidence on the use of predictive models to assist COVID-19 patient's ICU admission decision-making process. Accumulative evidence suggests that lung ultrasound (LUS) assessment of COVID-19 patients allows accurate bedside evaluation of lung integrity, with the added advantage of repeatability, absence of radiation exposure, reduced risk of virus dissemination, and low cost. Our goal is to assess the performance of a quantified indicator resulting from LUS data compared with standard clinical practice model to predict critical respiratory illness in the 24 hours following hospital admission.

DESIGN : Prospective cohort study.

SETTING : Critical Care Unit from University Hospital Purpan (Toulouse, France) between July 2020 and March 2021.

PATIENTS : Adult patients for COVID-19 who were in acute respiratory failure (ARF), defined as blood oxygen saturation as measured by pulse oximetry less than 90% while breathing room air or respiratory rate greater than or equal to 30 breaths/min at hospital admission. Linear multivariate models were used to identify factors associated with critical respiratory illness, defined as death or mild/severe acute respiratory distress syndrome (Pao2/Fio2 < 200) in the 24 hours after patient's hospital admission.

INTERVENTION : LUS assessment.

MEASUREMENTS AND MAIN RESULTS : One hundred and forty COVID-19 patients with ARF were studied. This cohort was split into two independent groups: learning sample (first 70 patients) and validation sample (last 70 patients). Interstitial lung water, thickening of the pleural line, and alveolar consolidation detection were strongly associated with patient's outcome. The LUS model predicted more accurately patient's outcomes than the standard clinical practice model (DeLong test: Testing: z score = 2.50, p value = 0.01; Validation: z score = 2.11, p value = 0.03).

CONCLUSIONS : LUS assessment of COVID-19 patients with ARF at hospital admission allows a more accurate prediction of the risk of critical respiratory illness than standard clinical practice. These results hold the promise of improving ICU resource allocation process, particularly in the case of massive influx of patients or limited resources, both now and in future anticipated pandemics.

Aguersif Amazigh, Sarton Benjamine, Bouharaoua Sihem, Gaillard Lucien, Standarovski Denis, Faucoz Orphée, Martin Blondel Guillaume, Khallel Hatem, Thalamas Claire, Sommet Agnes, Riu Béatrice, Morand Eric, Bataille Benoit, Silva Stein

2022-Jun

COVID-19, acute respiratory distress syndrome, acute respiratory failure, intensive care unit admission decision-making, lung ultrasound, machine learning

Surgery Surgery

Neural Rendering for Stereo 3D Reconstruction of Deformable Tissues in Robotic Surgery

ArXiv Preprint

Reconstruction of the soft tissues in robotic surgery from endoscopic stereo videos is important for many applications such as intra-operative navigation and image-guided robotic surgery automation. Previous works on this task mainly rely on SLAM-based approaches, which struggle to handle complex surgical scenes. Inspired by recent progress in neural rendering, we present a novel framework for deformable tissue reconstruction from binocular captures in robotic surgery under the single-viewpoint setting. Our framework adopts dynamic neural radiance fields to represent deformable surgical scenes in MLPs and optimize shapes and deformations in a learning-based manner. In addition to non-rigid deformations, tool occlusion and poor 3D clues from a single viewpoint are also particular challenges in soft tissue reconstruction. To overcome these difficulties, we present a series of strategies of tool mask-guided ray casting, stereo depth-cueing ray marching and stereo depth-supervised optimization. With experiments on DaVinci robotic surgery videos, our method significantly outperforms the current state-of-the-art reconstruction method for handling various complex non-rigid deformations. To our best knowledge, this is the first work leveraging neural rendering for surgical scene 3D reconstruction with remarkable potential demonstrated. Code is available at: https://github.com/med-air/EndoNeRF.

Yuehao Wang, Yonghao Long, Siu Hin Fan, Qi Dou

2022-06-30

Surgery Surgery

Feature pyramid self-attention network for respiratory motion prediction in ultrasound image guided surgery.

In International journal of computer assisted radiology and surgery

PURPOSE : The robot-assisted automated puncture system under ultrasound guidance can well improve the puncture accuracy in ablation surgery. The automated puncture system requires advanced definition of the puncture location, while the displacement of thoracic-abdominal tumors caused by respiratory motion makes it difficult for the system to locate the best puncture position. Predicting tumor motion is an effective way to help the automated puncture system output a more accurate puncture position.

METHODS : In this paper, we propose a self-attention-based feature pyramid algorithm FPSANet for time-series forecasting, which can extract both linear and nonlinear dependencies of time series. Firstly, we use the temporal convolutional network as the backbone to extract different scale time-series features, and the self-attention module is followed to weigh more significant features to improve nonlinear prediction. Secondly, we use autoregressive models to perform linear prediction. Finally, we directly combine the above two kinds of predictions as the final prediction.

RESULTS : FPSANet is trained and tested on our private datasets captured from clinical individuals, and we predict the target position after 50 ms, 150 ms, 300 ms and 400 ms. The result shows the evaluation criteria of the MAE is less than 1 mm at 50 ms and 150 ms, and less than 2 mm at 300 ms. Compared with the AR model, bidirectional LSTM and RVM, our method not only outperforms both models in accuracy (AR: ~ 7.7%; bidirectional LSTM: ~ 75.9%; RVM: ~ 76.5%) but is also more stable on different types of respiratory curves.

CONCLUSION : Respiratory motion in the liver in actual clinical practice vary widely from person to person, while sometimes having less distinct periodic patterns. Under these conditions, our algorithm has the advantage of excellent stability for prediction on various sequences, and its running time of performing single sequence prediction can meet clinical requirements.

Yao Chen, He Jishuai, Che Hui, Huang Yibin, Wu Jian

2022-Jun-29

AR, Deep learning, Respiratory motion prediction, Self-attention, Temporal convolutional network

Ophthalmology Ophthalmology

Deep Residual Network for Diagnosis of Retinal Diseases Using Optical Coherence Tomography Images.

In Interdisciplinary sciences, computational life sciences

Diabetic retinopathy occurs due to damage to the blood vessels in the retina, and it is a major health problem in recent years that progresses slowly without recognizable symptoms. Optical coherence tomography (OCT) is a popular and widely used noninvasive imaging modality for the diagnosis of diabetic retinopathy. Accurate and early diagnosis of this disease using OCT images is crucial for the prevention of blindness. In recent years, several deep learning methods have been very successful in automating the process of detecting retinal diseases from OCT images. However, most methods face reliability and interpretability issues. In this study, we propose a deep residual network for the classification of four classes of retinal diseases, namely diabetic macular edema (DME), choroidal neovascularization (CNV), DRUSEN and NORMAL in OCT images. The proposed model is based on the popular architecture called ResNet50, which eliminates the vanishing gradient problem and is pre-trained on large dataset such as ImageNet and trained end-to-end on the publicly available OCT image dataset. We removed the fully connected layer of ResNet50 and placed our new fully connected block on top to improve the classification accuracy and avoid overfitting in the proposed model. The proposed model was trained and evaluated using different performance metrics, including receiver operating characteristic (ROC) curve on a dataset of 84,452 OCT images with expert disease grading as DRUSEN, CNV, DME and NORMAL. The proposed model provides an improved overall classification accuracy of 99.48% with only 5 misclassifications out of 968 test samples and outperforms existing methods on the same dataset. The results show that the proposed model is well suited for the diagnosis of retinal diseases in ophthalmology clinics.

Asif Sohaib, Amjad Kamran

2022-Jun-29

Computer-aided detection and diagnosis, Deep learning, Optical coherence tomography, Residual network, Transfer learning

Cardiology Cardiology

The future of heart failure with preserved ejection fraction : Deep phenotyping for targeted therapeutics.

In Herz

Heart failure (HF) with preserved ejection fraction (HFpEF) is a multi-organ, systemic syndrome that involves multiple cardiac and extracardiac pathophysiologic abnormalities. Because HFpEF is a heterogeneous syndrome and resistant to a "one-size-fits-all" approach it has proven to be very difficult to treat. For this reason, several research groups have been working on methods for classifying HFpEF and testing targeted therapeutics for the HFpEF subtypes identified. Apart from conventional classification strategies based on comorbidity, etiology, left ventricular remodeling, and hemodynamic subtypes, researchers have been combining deep phenotyping with innovative analytical strategies (e.g., machine learning) to classify HFpEF into therapeutically homogeneous subtypes over the past few years. Despite the growing excitement for such approaches, there are several potential pitfalls to their use, and there is a pressing need to follow up on data-driven HFpEF subtypes in order to determine their underlying mechanisms and molecular basis. Here we provide a framework for understanding the phenotype-based approach to HFpEF by reviewing (1) the historical context of HFpEF; (2) the current HFpEF paradigm of comorbidity-induced inflammation and endothelial dysfunction; (3) various methods of sub-phenotyping HFpEF; (4) comorbidity-based classification and treatment of HFpEF; (5) machine learning approaches to classifying HFpEF; (6) examples from HFpEF clinical trials; and (7) the future of phenomapping (machine learning and other advanced analytics) for the classification of HFpEF.

Heinzel Frank R, Shah Sanjiv J

2022-Jun-29

Cardiovascular disease, Classification, Machine learning, Phenomapping, Phenotype

General General

A comparison of artificial intelligence versus radiologists in the diagnosis of thyroid nodules using ultrasonography: a systematic review and meta-analysis.

In European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery

BACKGROUND : Thyroid nodules are common. Ultrasonography (US) is the first investigation for thyroid nodules. Artificial Intelligence (AI) is widely integrated into medical diagnosis to provide additional information. The primary objective of this study was to accumulate the pooled sensitivity and specificity between all available AI and radiologists using thyroid US imaging. The secondary objective was to compare AI's diagnostic performance to that of radiologists.

MATERIALS AND METHODS : A systematic review meta-analysis. PubMed, Scopus, Web of Science, and Cochrane Library data were searched for studies from inception until June 11, 2020.

RESULTS : Twenty five studies were included in this meta-analysis. The pooled sensitivity and specificity of AI were 0.86 (95% CI 0.81-0.91) and 0.78 (95% CI 0.73-0.83), respectively. The pooled sensitivity and specificity of radiologists were 0.85 (95% CI 0.80-0.89) and 0.82 (95% CI 0.77-0.86), respectively. The accuracy of AI and radiologists is equivalent in terms of AUC [AI 0.89 (95% CI 0.86-0.92), radiologist 0.91 (95% CI 0.88-0.93)]. The diagnostic odd ratio (DOR) between AI 23.10 (95% CI 14.20-37.58) and radiologists 27.12 (95% CI 17.45-42.16) had no statistically significant difference (P = 0.56). Meta-regression analysis revealed that Deep Learning AI had significantly greater sensitivity and specificity than classic machine learning AI (P < 0.001).

CONCLUSION : AI demonstrated comparable performance to radiologists in diagnosing benign and malignant thyroid nodules using ultrasonography. Additional research to establish its equivalency should be conducted.

Potipimpanon Pimrada, Charakorn Natamon, Hirunwiwatkul Prakobkiat

2022-Jun-29

Artificial intelligence, Deep learning, Machine learning, Neural network, Thyroid nodule, Ultrasonography

Radiology Radiology

Application of kNN and SVM to predict the prognosis of advanced schistosomiasis.

In Parasitology research ; h5-index 47.0

Predictive models for prognosis of small sample advanced schistosomiasis patients have not been well studied. We aimed to construct prognostic predictive models of small sample advanced schistosomiasis patients using two machine learning algorithms, k nearest neighbour (kNN) and support vector machine (SVM) utilising routinely available data under the government medical assistance programme. The predictive models were derived from 229 patients from Xiantao and externally validated by 77 patients of Jiayu, two county-level cities in Hubei province, China. Candidate predictors were selected according to expert opinions and literature reports, including clinical features, sociodemographic characteristics, and medical examinations results. An area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the models' predictive performances. The AUC values were 0.879 for the kNN model and 0.890 for the SVM model in the training set, 0.852 for the kNN model, and 0.785 for the SVM model in the external validation set. The kNN and SVM models can be used to improve the health services provided by healthcare planners, clinicians, and policymakers.

Zhou Xiaorong, Wang He, Xu Chuan, Peng Li, Xu Feng, Lian Lifei, Deng Gang, Ji Suqiong, Hu Mengyan, Zhu Hong, Xu Yi, Li Guo

2022-Jun-29

Advanced schistosomiasis, Predictive model, Support vector machine, k nearest neighbour

Surgery Surgery

The utility of machine learning algorithms for the prediction of patient-reported outcome measures following primary hip and knee total joint arthroplasty.

In Archives of orthopaedic and trauma surgery

BACKGROUND : Patient-reported outcome measures (PROMs) are increasingly used as quality benchmark in total hip and knee arthroplasty (THA; TKA) due to bundled payment systems that aim to provide a patient-centered, value-based treatment approach. However, there is a paucity of predictive tools for postoperative PROMs. Therefore, this study aimed to develop and validate machine learning models for the prediction of numerous patient-reported outcome measures following primary hip and knee total joint arthroplasty.

METHODS : A total of 4526 consecutive patients (2137 THA; 2389 TKA) who underwent primary hip and knee total joint arthroplasty and completed both pre- and postoperative PROM scores was evaluated in this study. The following PROM scores were included for analysis: HOOS-PS, KOOS-PS, Physical Function SF10A, PROMIS SF Physical and PROMIS SF Mental. Patient charts were manually reviewed to identify patient demographics and surgical variables associated with postoperative PROM scores. Four machine learning algorithms were developed to predict postoperative PROMs following hip and knee total joint arthroplasty. Model assessment was performed through discrimination, calibration and decision curve analysis.

RESULTS : The factors most significantly associated with the prediction of postoperative PROMs include preoperative PROM scores, Charlson Comorbidity Index, American Society of Anaesthesiology score, insurance status, age, length of hospital stay, body mass index and ethnicity. The four machine learning models all achieved excellent performance across discrimination (AUC > 0.83), calibration and decision curve analysis.

CONCLUSION : This study developed machine learning models for the prediction of patient-reported outcome measures at 1-year following primary hip and knee total joint arthroplasty. The study findings show excellent performance on discrimination, calibration and decision curve analysis for all four machine learning models, highlighting the potential of these models in clinical practice to inform patients prior to surgery regarding their expectations of postoperative functional outcomes following primary hip and knee total joint arthroplasty.

LEVEL OF EVIDENCE : Level III, case control retrospective analysis.

Klemt Christian, Uzosike Akachimere Cosmas, Esposito John G, Harvey Michael Joseph, Yeo Ingwon, Subih Murad, Kwon Young-Min

2022-Jun-29

Artificial intelligence, Hip and knee total joint arthroplasty, Machine learning, Patient-reported outcome measures, Risk factors

General General

Artificial Intelligence-Enabled ECG: Physiologic and Pathophysiologic Insights and Implications.

In Comprehensive Physiology ; h5-index 52.0

Advancements in machine learning and computing methods have given new life and great excitement to one of the most essential diagnostic tools to date-the electrocardiogram (ECG). The application of artificial intelligence-enabled ECG (AI-ECG) has resulted in the ability to identify electrocardiographic signatures of conventional and unique variables and pathologies, giving way to tremendous clinical potential. However, what these AI-ECG models are detecting that the human eye is missing remains unclear. In this article, we highlight some of the recent developments in the field and their potential clinical implications, while also attempting to shed light on the physiologic and pathophysiologic features that enable these models to have such high diagnostic yield. © 2022 American Physiological Society. Compr Physiol 12:3417-3424, 2022.

Kashou Anthony H, Adedinsewo Demilade A, Siontis Konstantinos C, Noseworthy Peter A

2022-Jun-29

General General

Visualizing and quantifying molecular and cellular processes in C. elegans using light microscopy.

In Genetics ; h5-index 66.0

Light microscopes are the cell and developmental biologists' "best friend", providing a means to see structures and follow dynamics from the protein to the organism level. A huge advantage of C. elegans as a model organism is its transparency, which coupled with its small size means that nearly every biological process can be observed and measured with the appropriate probe and light microscope. Continuous improvement in microscope technologies along with novel genome editing techniques to create transgenic probes have facilitated the development and implementation of a dizzying array of methods for imaging worm embryos, larvae and adults. In this review we provide an overview of the molecular and cellular processes that can be visualized in living worms using light microscopy. A partial inventory of fluorescent probes and techniques successfully used in worms to image the dynamics of cells, organelles, DNA, and protein localization and activity is followed by a practical guide to choosing between various imaging modalities, including widefield, confocal, lightsheet, and structured illumination microscopy. Finally, we discuss the available tools and approaches, including machine learning, for quantitative image analysis tasks, such as colocalization, segmentation, object tracking, and lineage tracing. Hopefully, this review will inspire worm researchers who have not yet imaged their worms to begin, and push those who are imaging to go faster, finer, and longer.

Shah Pavak, Bao Zhirong, Zaidel-Bar Ronen

2022-Jun-29

Green fluorescent protein, activity sensors, confocal microscopy, fluorescence microscopy, image analysis, lightsheet microscopy, localization, molecular dynamics, spatiotemporal resolution, super resolution

General General

The role of bone mineral density and cartilage volume to predict knee cartilage degeneration.

In European journal of translational myology

Knee Osteoarthritis (OA) is a highly prevalent condition affecting knee joint that causes loss of physical function and pain. Clinical treatments are mainly focused on pain relief and limitation of disabilities; therefore, it is crucial to find new paradigms assessing cartilage conditions for detecting and monitoring the progression of OA. The goal of this paper is to highlight the predictive power of several features, such as cartilage density, volume and surface. These features were extracted from the 3D reconstruction of knee joint of forty-seven different patients, subdivided into two categories: degenerative and non-degenerative. The most influent parameters for the degeneration of the knee cartilage were determined using two machine learning classification algorithms (logistic regression and support vector machine); later, box plots, which depicted differences between the classes by gender, were presented to analyze several of the key features' trend. This work is part of a strategy that aims to find a new solution to assess cartilage condition based on new-investigated features.

Ciliberti Federica Kiyomi, Cesarelli Giuseppe, Guerrini Lorena, Gunnarsson Arnar Evgeni, Forni Riccardo, Aubonnet Romain, Recenti Marco, Jacob Deborah, Jónsson Halldór, Cangiano Vincenzo, Islind Anna Sigríður, Gambacorta Monica, Gargiulo Paolo

2022-Jun-28

General General

Deep brain stimulation and stereotactic-assisted brain graft injection targeting fronto-striatal circuits for Huntington's disease: an update.

In Expert review of neurotherapeutics

INTRODUCTION : Huntington's Disease as progressive neurological disorders associated with motor, behavioral, and cognitive impairment poses a therapeutic challenge in case of limited responsiveness to established therapeutics. Pallidal deep brain stimulation and neurorestorative strategies (brain grafts) scoping to modulate fronto-striatal circuits have gained increased recognition for the treatment of refractory Huntington's disease (HD).

AREAS COVERED : A review (2000-2022) was performed in PubMed, Embase, and Cochrane Library covering clinical trials conceptualized to determine the efficacy and safety of invasive, stereotactic-guided deep-brain stimulation and intracranial brain-graft injection targeting the globus pallidus and adjunct structures (striatum).

EXPERT OPINION : Stereotactic brain-grafting strategies were performed in few HD patients with inconsistent findings and mild-to-moderate clinical responsiveness with a recently published large, randomized-controlled trial (NCT00190450) yielding negative results. We identified 19 in-human DBS trials (uncontrolled) targeting the globus pallidus internus/externus along with randomized-controlled trial pending report (NCT02535884). We did not detect any significant changes in the UHDRS total score after restorative injections, while in contrast, the use of deep-brain stimulation resulted in a significant reduction of chorea. GPi-DBS should be considered in cases where selective chorea is present. However, both invasive therapies remain experimental and are not ready for the implementation in clinical use.

Kinfe Thomas, Del Vecchio Alessandro, Nüssel Martin, Zhao Yining, Stadlbauer Andreas, Buchfelder Michael

2022-Jun-29

Huntington´s disease, brain grafts, chorea, deep-brain stimulation, dystonia, efficacy, fronto-striatal circuits, globus pallidus externus/internus, parkinsonism, quality of life, safety, striatum, unified Huntington´s disease rating scale (UHDRS)

General General

Multireader image quality evaluation of dynamic myocardial computed tomography perfusion imaging with a novel four-dimensional noise reduction filter.

In Acta radiologica (Stockholm, Sweden : 1987)

BACKGROUND : Dynamic myocardial computed tomography perfusion (CTP) is a novel technique able to depict cardiac ischemia.

PURPOSE : To evaluate the impact of a four-dimensional noise reduction filter (similarity filter [4D-SF]) on image quality in dynamic CTP imaging, allowing for substantial radiation dose reduction.

MATERIAL AND METHODS : Dynamic CTP datasets of 30 patients (16 women) with suspected coronary artery disease, acquired with a 320-slice CT system, were retrieved, reconstructed with the deep learning-based algorithm of the system (DLR), and filtered with the 4D-SF. For each case, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in six regions of interest (33-38mm2) were calculated before and after filtering, in four-chamber and short-axis views, and t-tested. Furthermore, six radiologists of different expertise evaluated subjective image preference by answering five visual grading analysis-type questions (regarding acceptable level of noise, absence of artifacts, natural appearance, cardiac contour sharpness, diagnostic acceptability) using a 5-point scale. The results were analyzed using visual grade characteristics (VGC) and intraclass correlation coefficient (ICC).

RESULTS : Mean SNR in four-chamber view (unfiltered vs. filtered) were: septum=4.1 ± 2.1 versus 7.6 ± 5.6; lateral wall=4.5 ± 2.0 versus 8.0 ± 4.9; CNRseptum=16.6 ± 8.9 versus 31.7 ± 28; lateral wall=16.2 ± 8.9 versus 31.3 ± 28.9. Similar results were obtained in short-axis view. The perceived filtered image quality indicated decreased noise (VGCAUC=0.96) and artifacts (0.65), improved natural appearance (0.59), cardiac contour sharpness (0.74), and diagnostic acceptability (0.78). The inter-observer variability was excellent (ICC=0.79). All results were statistically significant (P < 0.05).

CONCLUSION : Similarity filtering after DLR improves image quality, possibly enabling dose reduction in dynamic CTP imaging in patient with suspected chronic coronary syndrome.

Sliwicka Olga, Swiderska-Chadaj Zaneta, Snoeren Miranda, Brink Monique, Salah Khibar, Peters-Bax Liesbeth, Stille Tip, van Amerongen Martinus Johannes, Sechopoulos Ioannis, Habets Jesse

2022-Jun-28

Computed tomography, dynamic myocardial computed tomography perfusion imaging, image quality, noise reduction filter

General General

Deep transfer learning for the recognition of types of face masks as a core measure to prevent the transmission of COVID-19.

In Applied soft computing

The use of face masks in public places has emerged as one of the most effective non-pharmaceutical measures to lower the spread of COVID-19 infection. This has led to the development of several detection systems for identifying people who do not wear a face mask. However, not all face masks or coverings are equally effective in preventing virus transmission or illness caused by viruses and therefore, it appears important for those systems to incorporate the ability to distinguish between the different types of face masks. This paper implements four pre-trained deep transfer learning models (NasNetMobile, MobileNetv2, ResNet101v2, and ResNet152v2) to classify images based on the type of face mask (KN95, N95, surgical and cloth) worn by people. Experimental results indicate that the deep residual networks (ResNet101v2 and ResNet152v2) provide the best performance with the highest accuracy and the lowest loss.

Mar-Cupido Ricardo, García Vicente, Rivera Gilberto, Sánchez J Salvador

2022-Jun-23

COVID-19, Deep learning, Face mask, Recognition, Transfer learning

General General

Physics-informed machine learning for Structural Health Monitoring

ArXiv Preprint

The use of machine learning in Structural Health Monitoring is becoming more common, as many of the inherent tasks (such as regression and classification) in developing condition-based assessment fall naturally into its remit. This chapter introduces the concept of physics-informed machine learning, where one adapts ML algorithms to account for the physical insight an engineer will often have of the structure they are attempting to model or assess. The chapter will demonstrate how grey-box models, that combine simple physics-based models with data-driven ones, can improve predictive capability in an SHM setting. A particular strength of the approach demonstrated here is the capacity of the models to generalise, with enhanced predictive capability in different regimes. This is a key issue when life-time assessment is a requirement, or when monitoring data do not span the operational conditions a structure will undergo. The chapter will provide an overview of physics-informed ML, introducing a number of new approaches for grey-box modelling in a Bayesian setting. The main ML tool discussed will be Gaussian process regression, we will demonstrate how physical assumptions/models can be incorporated through constraints, through the mean function and kernel design, and finally in a state-space setting. A range of SHM applications will be demonstrated, from loads monitoring tasks for off-shore and aerospace structures, through to performance monitoring for long-span bridges.

Elizabeth J Cross, Samuel J Gibson, Matthew R Jones, Daniel J Pitchforth, Sikai Zhang, Timothy J Rogers

2022-06-30

Surgery Surgery

Waveform detection by deep learning reveals multi-area spindles that are selectively modulated by memory load.

In eLife

Sleep is generally considered to be a state of large-scale synchrony across thalamus and neocortex; however, recent work has challenged this idea by reporting isolated sleep rhythms such as slow oscillations and spindles. What is the spatial scale of sleep rhythms? To answer this question, we adapted deep learning algorithms initially developed for detecting earthquakes and gravitational waves in high-noise settings for analysis of neural recordings in sleep. We then studied sleep spindles in non-human primate electrocorticography (ECoG), human electroencephalogram (EEG), and clinical intracranial electroencephalogram (iEEG) recordings in the human. Within each recording type, we find widespread spindles occur much more frequently than previously reported. We then analyzed the spatiotemporal patterns of these large-scale, multi-area spindles and, in the EEG recordings, how spindle patterns change following a visual memory task. Our results reveal a potential role for widespread, multi-area spindles in consolidation of memories in networks widely distributed across primate cortex.

Mofrad Maryam H, Gilmore Greydon, Koller Dominik, Mirsattari Seyed M, Burneo Jorge G, Steven David A, Khan Ali R, Suller Marti Ana, Muller Lyle

2022-Jun-29

computational biology, deep learning, human, memory, memory consolidation, neuroscience, rhesus macaque, sleep spindles, systems biology

General General

Association Between High-Sensitivity C-Reactive Protein and Prognosis in Different Periods After Ischemic Stroke or Transient Ischemic Attack.

In Journal of the American Heart Association ; h5-index 70.0

Background The aim of this study was to investigate the association between hsCRP (high-sensitivity C-reactive protein) and prognosis over time after stroke onset. Methods and Results In this prespecified prospective substudy of the Third China National Stroke Registry, a total of 9438 patients with acute ischemic stroke or transient ischemic attack and measured hsCRP were included. Patients were categorized into 3 groups according to the sampling time after index onset (<24 hours, 24-72 hours, 72 hours-8 days). The outcomes consisted of stroke recurrence and combined vascular events within 1 year, and dependence or death defined as modified Rankin Scale score of 3 to 6 at 1 year. The associations between hsCRP and outcomes in different groups were analyzed by using Cox proportional hazards and logistic regression models. The median levels of hsCRP within 24 hours, between 24 and 72 hours and between 72 hours and 8 days were 2.01, 1.72, and 1.72 mg/L, respectively (P < 0.05). Compared with the bottom quartile, patients in the top quartile measured within 72 hours were at increased risk of recurrent stroke (<24 hours: adjusted hazard ratio [HR], 1.57 [95% CI, 1.05-2.35], P = 0.03; 24-72 hours: adjusted HR, 1.60 [95% CI, 1.18-2.17], P = 0.003). Association was attenuated after further adjusting for the Org 10 172 test in the Treatment of Acute Stroke classification (<24 hours: adjusted HR, 1.51 [95% CI, 1.01-2.27]; P = 0.05; 24-72 hours: adjusted HR, 1.55 [95% CI, 1.14-2.10]; P = 0.01). The association only existed in patients with large-artery atherosclerosis (adjusted HR, 1.68 [95% CI, 1.06-2.64]; P = 0.03). However, the association was not found in the hsCRP level measured between 72 hours and 8 days. Similar results were found for the outcome of combined vascular events. Additionally, hsCRP levels measured between 24 and 72 hours were associated with an increased risk of poor functional outcomes. Conclusions Elevated levels of hsCRP measured in the first 72 hours after ischemic stroke or transient ischemic attack but not 72 hours to 8 days, were associated with an increased risk of 1-year stroke recurrence.

Wang Yu, Li Jiejie, Pan Yuesong, Wang Mengxing, Meng Xia, Wang Yongjun

2022-Jun-29

TIA, high‐sensitivity C‐reactive protein, inflammation, ischemic stroke

oncology Oncology

Real Time Volumetric MRI for 3D Motion Tracking via Geometry-Informed Deep Learning.

In Medical physics ; h5-index 59.0

PURPOSE : To develop a geometry-informed deep learning framework for volumetric MRI with sub-second acquisition time in support of 3D motion tracking, which is highly desirable for improved radiotherapy precision but hindered by the long image acquisition time.

METHODS : A 2D-3D deep learning network with an explicitly defined geometry module that embeds geometric priors of the k-space encoding pattern was investigated, where a 2D generation network first augmented the sparsely sampled image dataset by generating new 2D representations of the underlying 3D subject. A geometry module then unfolded the 2D representations to the volumetric space. Finally, a 3D refinement network took the unfolded 3D data and outputted high-resolution volumetric images. Patient-specific models were trained for 7 abdominal patients to reconstruct volumetric MRI from both orthogonal cine slices and sparse radial samples. To evaluate the robustness of the proposed method to longitudinal patient anatomy and position changes, we tested the trained model on separate datasets acquired more than one month later and evaluated 3D target motion tracking accuracy using the model-reconstructed images by deforming a reference MRI with gross tumor volume (GTV) contours to a 5-min time series of both ground truth and model-reconstructed volumetric images with a temporal resolution of 340 ms.

RESULTS : Across the 7 patients evaluated, the median distances between model-predicted and ground truth GTV centroids in the superior-inferior direction were 0.4±0.3 mm and 0.5±0.4 mm for cine and radial acquisitions respectively. The 95-percentile Hausdorff distances between model-predicted and ground truth GTV contours were 4.7±1.1 mm and 3.2±1.5 mm for cine and radial acquisitions, which are of the same scale as cross-plane image resolution.

CONCLUSION : Incorporating geometric priors into deep learning model enables volumetric imaging with high spatial and temporal resolution, which is particularly valuable for 3D motion tracking and has the potential of greatly improving MRI-guided radiotherapy precision. This article is protected by copyright. All rights reserved.

Liu Lianli, Shen Liyue, Johansson Adam, Balter James M, Cao Yue, Chang Daniel, Xing Lei

2022-Jun-29

Deep learning, Image reconstruction, MRI-guided radiotherapy, Motion tracking

General General

Artificial intelligence assisted cytological detection for early esophageal squamous epithelial lesions by using low-grade squamous intraepithelial lesion as diagnostic threshold.

In Cancer medicine

BACKGROUND : Manual cytological diagnosis for early esophageal squamous cell carcinoma (early ESCC) and high-grade intraepithelial neoplasia (HGIN) is unsatisfactory. Herein, we have introduced an artificial intelligence (AI)-assisted cytological diagnosis for such lesions.

METHODS : Low-grade squamous intraepithelial lesion or worse was set as the diagnostic threshold for AI-assisted diagnosis. The performance of AI-assisted diagnosis was evaluated and compared to that of manual diagnosis. Feasibility in large-scale screening was also assessed.

RESULTS : AI-assisted diagnosis for abnormal cells was superior to manual reading by presenting a higher efficiency for each slide (50.9 ± 0.8 s vs 236.8 ± 3.9 s, p = 1.52 × 10-76 ) and a better interobserver agreement (93.27% [95% CI, 92.76%-93.74%] vs 65.29% [95% CI, 64.35%-66.22%], p = 1.03 × 10-84 ). AI-assisted detection showed a higher diagnostic accuracy (96.89% [92.38%-98.57%] vs 72.54% [65.85%-78.35%], p = 1.42 × 10-14 ), sensitivity (99.35% [95.92%-99.97%] vs 68.39% [60.36%-75.48%], p = 7.11 × 10-15 ), and negative predictive value (NPV) (97.06% [82.95%-99.85%] vs 40.96% [30.46%-52.31%], p = 1.42 × 10-14 ). Specificity and positive predictive value (PPV) were not significantly differed. AI-assisted diagnosis demonstrated a smaller proportion of participants of interest (3.73%, [79/2117] vs.12.84% [272/2117], p = 1.59 × 10-58 ), a higher consistence between cytology and endoscopy (40.51% [32/79] vs. 12.13% [33/272], p = 1.54 × 10- 8), specificity (97.74% [96.98%-98.32%] vs 88.52% [87.05%-89.84%], p = 3.19 × 10-58 ), and PPV (40.51% [29.79%-52.15%] vs 12.13% [8.61%-16.75%], p = 1.54 × 10-8 ) in community-based screening. Sensitivity and NPV were not significantly differed. AI-assisted diagnosis as primary screening significantly reduced average cost for detecting positive cases.

CONCLUSION : Our study provides a novel cytological method for detecting and screening early ESCC and HGIN.

Yao Bin, Feng Yadong, Zhao Kai, Liang Yan, Huang Peilin, Zang Juncai, Song Jie, Li Mengjie, Wang Xiaofen, Shu Huazhong, Shi Ruihua

2022-Jun-29

AI-assisted diagnosis, cytology, early esophageal squamous cell cancer, precursor lesion, screening

Dermatology Dermatology

Comparative Effectiveness of Biologics in Clinical Practice: Week 12 Primary Outcomes from an International Observational Psoriasis Study of Health Outcomes (PSoHO).

In Journal of the European Academy of Dermatology and Venereology : JEADV

BACKGROUND : Clinical trials study treatment outcomes under stringent conditions, capturing incompletely the heterogeneity of patient populations and treatment complexities encountered in real-world practice.

OBJECTIVES : To compare the effectiveness of anti-interleukin (IL)-17A biologics relative to other approved biologics in patients with moderate-to-severe psoriasis.

METHODS : The Psoriasis Study of Health Outcomes (PSoHO) is an ongoing 3-year observational cohort study in adults with chronic moderate-to-severe plaque psoriasis initiating or switching to a new biologic. Primary study endpoint is proportion of patients achieving 90% improvement in Psoriasis Area and Severity Index (PASI 90) and/or static Physician Global Assessment (sPGA) 0/1 at Week 12 (W12) in the anti-IL-17A cohort (ixekizumab [IXE], secukinumab) versus all other approved biologics. Secondary outcomes include proportion of patients who achieve PASI 75/90/100, absolute PASI scores ≤5, ≤2 and ≤1, Dermatology Life Quality Index (DLQI) score of 0/1 at W12 between the two cohorts and among the individual biologics. Comparative effectiveness analyses were conducted using Frequentist Model Averaging (FMA), a novel causal inference machine learning approach. Missing data for binary outcomes were imputed as non-response.

RESULTS : Patient profiles in the anti-IL-17A cohort and other biologics cohort were similar, with more frequent comorbid psoriatic arthritis and less frequent exposure to conventional treatments in the patients receiving anti-IL17A biologics. At W12, 71.4% of patients who received an anti-IL-17A biologic achieved PASI 90 and/or sPGA 0/1 compared to 58.6% of patients who received other biologics (odds ratios [OR], 1.9; 95% confidence intervals [CI], [1.6, 2.4]). Similar findings were observed for secondary outcomes.

CONCLUSIONS : These results reflect the high efficacy and early onset of skin clearance of IL-17A inhibitors observed in randomized clinical trials and confirm the effectiveness of anti-IL17A biologics in the real-world setting.

Pinter Andreas, Puig Luis, Schäkel Knut, Reich Adam, Zaheri Shirin, Costanzo Antonio, Tsai Tsen Fang, Smith Saxon D, Lynde Charles, Brnabic Alan, Reed Catherine, Hill Julie, Schuster Christopher, Riedl Elisabeth, Paul Carle

2022-Jun-29

health outcomes, ixekizumab, psoriasis, real world evidence

Ophthalmology Ophthalmology

Correlation between corneal dynamic responses and keratoconus topographic parameters.

In The Journal of international medical research

OBJECTIVE : To investigate the correlation between corneal biomechanical properties and topographic parameters using machine learning networks for automatic severity diagnosis and reference benchmark construction.

METHODS : This was a retrospective study involving 31 eyes from 31 patients with keratonus. Two clustering approaches were used (i.e., shape-based and feature-based). The shape-based method used a keratoconus benchmark validated for indicating the severity of keratoconus. The feature-based method extracted imperative features for clustering analysis.

RESULTS : There were strong correlations between the symmetric modes and the keratoconus severity and between the asymmetric modes and the location of the weak centroid. The Pearson product-moment correlation coefficient (PPMC) between the symmetric mode and normality was 0.92 and between the asymmetric mode and the weak centroid value was 0.75.

CONCLUSION : This study confirmed that there is a relationship between the keratoconus signs obtained from topography and the corneal dynamic behaviour captured by the Corvis ST device. Further studies are required to gather more patient data to establish a more extensive database for validation.

Tai Hsi-Yun, Lin Jun-Ji, Huang Yi-Hung, Shih Po-Jen, Wang I-Jong, Yen Jia-Yush

2022-Jun

Corneal dynamic characteristics, Corvis ST, keratoconus, machine learning

Public Health Public Health

Automated identification of chicken distress vocalizations using deep learning models.

In Journal of the Royal Society, Interface

The annual global production of chickens exceeds 25 billion birds, which are often housed in very large groups, numbering thousands. Distress calling triggered by various sources of stress has been suggested as an 'iceberg indicator' of chicken welfare. However, to date, the identification of distress calls largely relies on manual annotation, which is very labour-intensive and time-consuming. Thus, a novel convolutional neural network-based model, light-VGG11, was developed to automatically identify chicken distress calls using recordings (3363 distress calls and 1973 natural barn sounds) collected on an intensive farm. The light-VGG11 was modified from VGG11 with significantly fewer parameters (9.3 million versus 128 million) and 55.88% faster detection speed while displaying comparable performance, i.e. precision (94.58%), recall (94.89%), F1-score (94.73%) and accuracy (95.07%), therefore more useful for model deployment in practice. To additionally improve light-VGG11's performance, we investigated the impacts of different data augmentation techniques (i.e. time masking, frequency masking, mixed spectrograms of the same class and Gaussian noise) and found that they could improve distress calls detection by up to 1.52%. Our distress call detection demonstration on continuous audio recordings, shows the potential for developing technologies to monitor the output of this call type in large, commercial chicken flocks.

Mao Axiu, Giraudet Claire S E, Liu Kai, De Almeida Nolasco Inês, Xie Zhiqin, Xie Zhixun, Gao Yue, Theobald James, Bhatta Devaki, Stewart Rebecca, McElligott Alan G

2022-Jun

animal welfare, bioacoustics, convolutional neural networks, data augmentation, precision livestock farming

General General

Artificially Intelligent Nanoarray Detects Various Cancers by Liquid Biopsy of Volatile Markers.

In Advanced healthcare materials

Cancer usually is not symptomatic in its early stages. However, early detection can vastly improve prognosis. Liquid biopsy holds great promise for early detection, although it still suffers from many disadvantages, mainly searching for specific cancer biomarkers. Here, we propose a new approach for liquid biopsies, based on volatile organic compounds (VOCs) patterns in blood headspace. We develop and use an artificial intelligence nanoarray based on a varied set of chemi-sensitive nano-based structured films to detect and stage cancer. As a proof of concept, we test three cancer models showing high incidence and mortality rates in the population: Breast cancer (BC), ovarian cancer (OVC), and pancreatic cancer (PC). The nanoarray has >84% accuracy, >81% sensitivity and >80% specificity for early detection and >97% accuracy, 100% sensitivity and >88% specificity for metastasis detection. Complementary mass spectrometry analysis validated these results . The ability to analyze complex biological fluid as blood, while considering data of a many VOCs at a time using our artificially intelligent nanoarray increases the sensitivity of predictive models and leads us to a potential efficient early diagnosis and disease-monitoring tool for cancer. This article is protected by copyright. All rights reserved.

Amor Reef Einoch, Zinger Assaf, Broza Yoav Y, Schroeder Avi, Haick Hossam

2022-Jun-28

breast cancer, liquid biopsy, machine learning, nanotechnology, ovarian cancer, pancreatic cancer, sensor

General General

Neuroimaging in the Era of Artificial Intelligence: Current Applications.

In Federal practitioner : for the health care professionals of the VA, DoD, and PHS

Background : Artificial intelligence (AI) in medicine has shown significant promise, particularly in neuroimaging. AI increases efficiency and reduces errors, making it a valuable resource for physicians. With the increasing amount of data processing and image interpretation required, the ability to use AI to augment and aid the radiologist could improve the quality of patient care.

Observations : AI can predict patient wait times, which may allow more efficient patient scheduling. Additionally, AI can save time for repeat magnetic resonance neuroimaging and reduce the time spent during imaging. AI has the ability to read computed tomography, magnetic resonance imaging, and positron emission tomography with reduced or without contrast without significant loss in sensitivity for detecting lesions. Neuroimaging does raise important ethical considerations and is subject to bias. It is vital that users understand the practical and ethical considerations of the technology.

Conclusions : The demonstrated applications of AI in neuroimaging are numerous and varied, and it is reasonable to assume that its implementation will increase as the technology matures. AI's use for detecting neurologic conditions holds promise in combatting ever increasing imaging volumes and providing timely diagnoses.

Monsour Robert, Dutta Mudit, Mohamed Ahmed-Zayn, Borkowski Andrew, Viswanadhan Narayan A

2022-Apr

General General

Drug repositioning in drug discovery of T2DM and repositioning potential of antidiabetic agents.

In Computational and structural biotechnology journal

Repositioning or repurposing drugs account for a substantial part of entering approval pipeline drugs, which indicates that drug repositioning has huge market potential and value. Computational technologies such as machine learning methods have accelerated the process of drug repositioning in the last few decades years. The repositioning potential of type 2 diabetes mellitus (T2DM) drugs for various diseases such as cancer, neurodegenerative diseases, and cardiovascular diseases have been widely studied. Hence, the related summary about repurposing antidiabetic drugs is of great significance. In this review, we focus on the machine learning methods for the development of new T2DM drugs and give an overview of the repurposing potential of the existing antidiabetic agents.

Zhu Sha, Bai Qifeng, Li Lanqing, Xu Tingyang

2022

AD, Alzheimer’s Disease, AEs, autoencoders, ASCVD, atherosclerotic cardiovascular disease, Antidiabetic drugs, CNNs, convolutional neural networks, CV, cardiovascular, CVD, cardiovascular diseases, DBNs, deep brief networks, DDA, drug-disease association, DDI, drug-drug interaction, DL, deep learning, DM, diabetes mellitus, DNNs, deep neural networks, DPP-4, dipeptidyl peptidase 4, DTI, drug-target interaction, Deep learning, Drug repositioning, Drug repurposing, GLP-1, glucagon-like peptide 1, GNNs, graph neural networks, ML, machine learning, Machine learning, PD, Parkinson’s Disease, PI3K/AKT, phosphatidylinositol 3-kinase/AKT, RNNs, recurrent neural networks, SGLT-2, sodium-glucose cotransporter 2, T2DM, T2DM, type 2 diabetes mellitus, TZD, thiazolidinedione, cAMP/PKA, cyclic adenosine monophosphate/protein kinase A

General General

Single-shot T2 mapping via multi-echo-train multiple overlapping-echo detachment planar imaging and multi-task deep learning.

In Medical physics ; h5-index 59.0

BACKGROUND : Quantitative magnetic resonance imaging provides robust biomarkers in clinics. Nevertheless, the lengthy scan time reduces imaging throughput and increases the susceptibility of imaging results to motion. In this context, a single-shot T2 mapping method based on multiple overlapping-echo detachment (MOLED) planar imaging was presented, but the relatively small echo time range limits its accuracy, especially in tissues with large T2 .

PURPOSE : In this work we proposed a novel single-shot method, Multi-Echo-Train Multiple OverLapping-Echo Detachment (METMOLED) planar imaging, to accommodate a large range of T2 quantification without additional measurements to rectify signal degeneration arisen from refocusing pulse imperfection.

METHODS : Multiple echo-train techniques were integrated into the MOLED sequence to capture larger TE information. Maps of T2 , B1 , and spin density were reconstructed synchronously from acquired METMOLED data via multi-task deep learning. A typical U-Net was trained with 3000/600 synthetic data with geometric/brain patterns to learn the mapping relationship between METMOLED signals and quantitative maps. The refocusing pulse imperfection was settled through the inherent information of METMOLED data and auxiliary tasks.

RESULTS : Experimental results on the digital brain (structural similarity (SSIM) index = 0.975/0.991/0.988 for MOLED/METMOLED-2/METMOLED-3, hyphenated number denotes the number of echo-trains), physical phantom (the slope of linear fitting with reference T2 map = 1.047/1.017/1.006 for MOLED/METMOLED-2/METMOLED-3), and human brain (Pearson's correlation coefficient (PCC) = 0.9581/0.9760/0.9900 for MOLED/METMOLED-2/METMOLED-3) demonstrated that the METMOLED improved the quantitative accuracy and the tissue details in contrast to the MOLED. These improvements were more pronounced in tissues with large T2 and in application scenarios with high temporal resolution (PCC = 0.8692/0.9465/0.9743 for MOLED/METMOLED-2/METMOLED-3). Moreover, the METMOLED could rectify the signal deviations induced by the non-ideal slice profiles of refocusing pulses without additional measurements. A preliminary measurement also demonstrated that the METMOLED is highly repeatable (mean coefficient of variation (CV) = 1.65%).

CONCLUSIONS : METMOLED breaks the restriction of echo-train length to TE and implements unbiased T2 estimates in an extensive range. Furthermore, it corrects the effect of refocusing pulse inaccuracy without additional measurements or signal post-processing, thus retaining its single-shot characteristic. This technique would be beneficial for accurate T2 quantification. This article is protected by copyright. All rights reserved.

Ouyang Binyu, Yang Qizhi, Wang Xiaoyin, He Hongjian, Ma Lingceng, Yang Qinqin, Zhou Zihan, Cai Shuhui, Chen Zhong, Wu Zhigang, Zhong Jianhui, Cai Congbo

2022-Jun-28

T2 mapping, multi-task deep learning, multiple overlapping-echo detachment, parametric map reconstruction, quantitative magnetic resonance imaging

General General

A novel model to predict severe COVID-19 and mortality using an artificial intelligence algorithm to interpret chest radiographs and clinical variables.

In ERJ open research

Background : Patients with coronavirus disease 2019 (COVID-19) could develop severe disease requiring admission to the intensive care unit (ICU). This article presents a novel method that predicts whether a patient will need admission to the ICU and assesses the risk of in-hospital mortality by training a deep-learning model that combines a set of clinical variables and features in chest radiographs.

Methods : This was a prospective diagnostic test study. Patients with confirmed severe acute respiratory syndrome coronavirus 2 infection between March 2020 and January 2021 were included. This study was designed to build predictive models obtained by training convolutional neural networks for chest radiograph images using an artificial intelligence (AI) tool and a random forest analysis to identify critical clinical variables. Then, both architectures were connected and fine-tuned to provide combined models.

Results : 2552 patients were included in the clinical cohort. The variables independently associated with ICU admission were age, fraction of inspired oxygen (F iO2 ) on admission, dyspnoea on admission and obesity. Moreover, the variables associated with hospital mortality were age, F iO2 on admission and dyspnoea. When implementing the AI model to interpret the chest radiographs and the clinical variables identified by random forest, we developed a model that accurately predicts ICU admission (area under the curve (AUC) 0.92±0.04) and hospital mortality (AUC 0.81±0.06) in patients with confirmed COVID-19.

Conclusions : This automated chest radiograph interpretation algorithm, along with clinical variables, is a reliable alternative to identify patients at risk of developing severe COVID-19 who might require admission to the ICU.

Munera Nicolás, Garcia-Gallo Esteban, Gonzalez Álvaro, Zea José, Fuentes Yuli V, Serrano Cristian, Ruiz-Cuartas Alejandra, Rodriguez Alejandro, Reyes Luis F

2022-Apr

General General

Custom Pretrainings and Adapted 3D-ConvNeXt Architecture for COVID Detection and Severity Prediction

ArXiv Preprint

Since COVID strongly affects the respiratory system, lung CT scans can be used for the analysis of a patients health. We introduce an neural network for the prediction of the severity of lung damage and the detection of infection using three-dimensional CT-scans. Therefore, we adapt the recent ConvNeXt model to process three-dimensional data. Furthermore, we introduce different pretraining methods specifically adjusted to improve the models ability to handle three-dimensional CT-data. In order to test the performance of our model, we participate in the 2nd COV19D Competition for severity prediction and infection detection.

Daniel Kienzle, Julian Lorenz, Robin Schön, Katja Ludwig, Rainer Lienhart

2022-06-30

General General

Custom Pretrainings and Adapted 3D-ConvNeXt Architecture for COVID Detection and Severity Prediction

ArXiv Preprint

Since COVID strongly affects the respiratory system, lung CT scans can be used for the analysis of a patients health. We introduce an neural network for the prediction of the severity of lung damage and the detection of infection using three-dimensional CT-scans. Therefore, we adapt the recent ConvNeXt model to process three-dimensional data. Furthermore, we introduce different pretraining methods specifically adjusted to improve the models ability to handle three-dimensional CT-data. In order to test the performance of our model, we participate in the 2nd COV19D Competition for severity prediction and infection detection.

Daniel Kienzle, Julian Lorenz, Robin Schön, Katja Ludwig, Rainer Lienhart

2022-06-30

General General

Identification of plant vacuole proteins by exploiting deep representation learning features.

In Computational and structural biotechnology journal

Plant vacuoles are the most important organelles for plant growth, development, and defense, and they play an important role in many types of stress responses. An important function of vacuole proteins is the transport of various classes of amino acids, ions, sugars, and other molecules. Accurate identification of vacuole proteins is crucial for revealing their biological functions. Several automatic and rapid computational tools have been proposed for the subcellular localization of proteins. Regrettably, they are not specific for the identification of plant vacuole proteins. To the best of our knowledge, there is only one computational software specifically trained for plant vacuolar proteins. Although its accuracy is acceptable, the prediction performance and stability of this method in practical applications can still be improved. Hence, in this study, a new predictor named iPVP-DRLF was developed to identify plant vacuole proteins specifically and effectively. This prediction software is designed using the light gradient boosting machine (LGBM) algorithm and hybrid features composed of classic sequence features and deep representation learning features. iPVP-DRLF achieved fivefold cross-validation and independent test accuracy values of 88.25 % and 87.16 %, respectively, both outperforming previous state-of-the-art predictors. Moreover, the blind dataset test results also showed that the performance of iPVP-DRLF was significantly better than the existing tools. The results of comparative experiments confirmed that deep representation learning features have an advantage over other classic sequence features in the identification of plant vacuole proteins. We believe that iPVP-DRLF would serve as an effective computational technique for plant vacuole protein prediction and facilitate related future research. The online server is freely accessible at https://lab.malab.cn/~acy/iPVP-DRLF. In addition, the source code and datasets are also accessible at https://github.com/jiaoshihu/iPVP-DRLF.

Jiao Shihu, Zou Quan

2022

Deep representation learning, Feature selection, Light gradient boosting machine, Machine learning, Vacuole proteins

General General

A brief review of protein-ligand interaction prediction.

In Computational and structural biotechnology journal

The task of identifying protein-ligand interactions (PLIs) plays a prominent role in the field of drug discovery. However, it is infeasible to identify potential PLIs via costly and laborious in vitro experiments. There is a need to develop PLI computational prediction approaches to speed up the drug discovery process. In this review, we summarize a brief introduction to various computation-based PLIs. We discuss these approaches, in particular, machine learning-based methods, with illustrations of different emphases based on mainstream trends. Moreover, we analyzed three research dynamics that can be further explored in future studies.

Zhao Lingling, Zhu Yan, Wang Junjie, Wen Naifeng, Wang Chunyu, Cheng Liang

2022

Drug discovery, Drug-target binding affinity, Machine learning, Protein–ligand interactions

General General

Application of interpretable machine learning for early prediction of prognosis in acute kidney injury.

In Computational and structural biotechnology journal

Background : This study aimed to develop an algorithm using the explainable artificial intelligence (XAI) approaches for the early prediction of mortality in intensive care unit (ICU) patients with acute kidney injury (AKI).

Methods : This study gathered clinical data with AKI patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) in the US between 2008 and 2019. All the data were further randomly divided into a training cohort and a validation cohort. Seven machine learning methods were used to develop the models for assessing in-hospital mortality. The optimal model was selected based on its accuracy and area under the curve (AUC). The SHapley Additive exPlanation (SHAP) values and Local Interpretable Model-Agnostic Explanations (LIME) algorithm were utilized to interpret the optimal model.

Results : A total of 22,360 patients with AKI were finally enrolled in this study (median age, 69.5 years; female, 42.8%). They were randomly split into a training cohort (16770, 75%) and a validation cohort (5590, 25%). The eXtreme Gradient Boosting (XGBoost) model achieved the best performance with an AUC of 0.890. The SHAP values showed that Glasgow Coma Scale (GCS), blood urea nitrogen, cumulative urine output on Day 1 and age were the top 4 most important variables contributing to the XGBoost model. The LIME algorithm was used to explain the individualized predictions.

Conclusions : Machine-learning models based on clinical features were developed and validated with great performance for the early prediction of a high risk of death in patients with AKI.

Hu Chang, Tan Qing, Zhang Qinran, Li Yiming, Wang Fengyun, Zou Xiufen, Peng Zhiyong

2022

Acute kidney injury, Critically illness, Interpretability, Machine learning, Mortality

General General

PERISCOPE-Opt: Machine learning-based prediction of optimal fermentation conditions and yields of recombinant periplasmic protein expressed in Escherichia coli.

In Computational and structural biotechnology journal

Optimization of the fermentation process for recombinant protein production (RPP) is often resource-intensive. Machine learning (ML) approaches are helpful in minimizing the experimentations and find vast applications in RPP. However, these ML-based tools primarily focus on features with respect to amino-acid-sequence, ruling out the influence of fermentation process conditions. The present study combines the features derived from fermentation process conditions with that from amino acid-sequence to construct an ML-based model that predicts the maximal protein yields and the corresponding fermentation conditions for the expression of target recombinant protein in the Escherichia coli periplasm. Two sets of XGBoost classifiers were employed in the first stage to classify the expression levels of the target protein as high (>50 mg/L), medium (between 0.5 and 50 mg/L), or low (<0.5 mg/L). The second-stage framework consisted of three regression models involving support vector machines and random forest to predict the expression yields corresponding to each expression-level-class. Independent tests showed that the predictor achieved an overall average accuracy of 75% and a Pearson coefficient correlation of 0.91 for the correctly classified instances. Therefore, our model offers a reliable substitution of numerous trial-and-error experiments to identify the optimal fermentation conditions and yield for RPP. It is also implemented as an open-access webserver, PERISCOPE-Opt (http://periscope-opt.erc.monash.edu).

Packiam Kulandai Arockia Rajesh, Ooi Chien Wei, Li Fuyi, Mei Shutao, Tey Beng Ti, Ong Huey Fang, Song Jiangning, Ramanan Ramakrishnan Nagasundara

2022

AUC, area under the curve, CV, cross-validation, CfsSubsetEval, Correlation-based Forward Selection Subset Evaluator, ClassifierSubsetEval, Classifier Subset Evaluator, E. coli, Escherichia coli, Escherichia coli, FC1, Feature Category 1, FC2, Feature Category 2, FC3, Feature Category 3, FC4, Feature Category 4, IPTG, isopropyl β-D-1-thiogalactopyranoside, LOOCV, Leave-one-out cross-validation, MAE, mean absolute error, MCC, Mathew correlation coefficient, ML, machine learning, MLR, machine learning in R, Machine learning, OD, optical density at 600 nm, Optimization, PCC, Pearson correlation coefficient, Periplasmic expression, Prediction model, RF, random forest, RFR, RF regression, RFR-High, RFR for high, RFR-Medium, RFR for medium, RMSE, root mean squared error, RPP, Recombinant protein production, RSM, response surface methodology, Recombinant protein production, SMOTE, Synthetic Minority Over-sampling Technique, SP, signal peptides, SVM, support vector machines, SVR, SVM regression, SVR-Low, SVR for class: “low”, XGB, XGBoost, pI, isoelectric point

General General

A unified platform enabling biomarker ranking and validation for 1562 drugs using transcriptomic data of 1250 cancer cell lines.

In Computational and structural biotechnology journal

Intro : In vitro cell line models provide a valuable resource to investigate compounds useful in the systemic chemotherapy of cancer. However, the due to the dispersal of the data into several different databases, the utilization of these resources is limited. Here, our aim was to establish a platform enabling the validation of chemoresistance-associated genes and the ranking of available cell line models.

Methods : We processed four independent databases, DepMap, GDSC1, GDSC2, and CTRP. The gene expression data was quantile normalized and HUGO gene names were assigned to have unambiguous identification of the genes. Resistance values were exported for all agents. The correlation between gene expression and therapy resistance is computed using ROC test.

Results : We combined four datasets with chemosensitivity data of 1562 agents and transcriptome-level gene expression of 1250 cancer cell lines. We have set up an online tool utilizing this database to correlate available cell line sensitivity data and treatment response in a uniform analysis pipeline (www.rocplot.com/cells). We employed the established pipeline to by rank genes related to resistance against afatinib and lapatinib, two inhibitors of the tyrosine-kinase domain of ERBB2.

Discussion : The computational tool is useful 1) to correlate gene expression with resistance, 2) to identify and rank resistant and sensitive cell lines, and 3) to rank resistance associated genes, cancer hallmarks, and gene ontology pathways. The platform will be an invaluable support to speed up cancer research by validating gene-resistance correlations and by selecting the best cell line models for new experiments.

Tibor Fekete János, Győrffy Balázs

2022

Chemotherapy, In vitro, Machine learning, Proliferation, RNAseq, Random forest, Receiver operator characteristics

General General

mRNAsi-related genes can effectively distinguish hepatocellular carcinoma into new molecular subtypes.

In Computational and structural biotechnology journal

Background : Recent studies have shown that the mRNA expression-based stemness index (mRNAsi) can accurately quantify the similarity of cancer cells to stem cells, and mRNAsi-related genes are used as biomarkers for cancer. However, mRNAsi-driven tumor heterogeneity is rarely investigated, especially whether mRNAsi can distinguish hepatocellular carcinoma (HCC) into different molecular subtypes is still largely unknown.

Methods : Using OCLR machine learning algorithm, weighted gene co-expression network analysis, consistent unsupervised clustering, survival analysis and multivariate cox regression etc. to identify biomarkers and molecular subtypes related to tumor stemness in HCC.

Results : We firstly demonstrate that the high mRNAsi is significantly associated with the poor survival and high disease grades in HCC. Secondly, we identify 212 mRNAsi-related genes that can divide HCC into three molecular subtypes: low cancer stemness cell phenotype (CSCP-L), moderate cancer stemness cell phenotype (CSCP-M) and high cancer stemness cell phenotype (CSCP-H), especially over-activated ribosomes, spliceosomes and nucleotide metabolism lead to the worst prognosis for the CSCP-H subtype patients, while activated amino acids, fatty acids and complement systems result in the best prognosis for the CSCP-L subtype. Thirdly, we find that three CSCP subtypes have different mutation characteristics, immune microenvironment and immune checkpoint expression, which may cause the differential prognosis for three subtypes. Finally, we identify 10 robust mRNAsi-related biomarkers that can effectively predict the survival of HCC patients.

Conclusions : These novel cancer stemness-related CSCP subtypes and biomarkers in this study will be of great clinical significance for the diagnosis, prognosis and targeted therapy of HCC patients.

Wang Canbiao, Qin Shijie, Pan Wanwan, Shi Xuejia, Gao Hanyu, Jin Ping, Xia Xinyi, Ma Fei

2022

Cancer stem cell, Hepatocellular Carcinoma, Molecular subtype, Prognosis, mRNAsi

General General

Emerging artificial intelligence applications in Spatial Transcriptomics analysis.

In Computational and structural biotechnology journal

Spatial transcriptomics (ST) has advanced significantly in the last few years. Such advancement comes with the urgent need for novel computational methods to handle the unique challenges of ST data analysis. Many artificial intelligence (AI) methods have been developed to utilize various machine learning and deep learning techniques for computational ST analysis. This review provides a comprehensive and up-to-date survey of current AI methods for ST analysis.

Li Yijun, Stanojevic Stefan, Garmire Lana X

2022

Artificial intelligence, Deep learning, Machine learning, Spatial transcriptomics

General General

Optimization of Service Process in Emergency Department Using Discrete Event Simulation and Machine Learning Algorithm.

In Archives of academic emergency medicine

Introduction : Emergency departments are operating with limited resources and high levels of unexpected requests. This study aimed to minimize patients' waiting time and the percentage of units' engagement to improve the emergency department (ED) efficiency.

Methods : A comprehensive combination method involving Discrete Event Simulation (DES), Artificial Neural Network (ANN) algorithm, and finally solving the model by use of Genetic Algorithm (GA) was used in this study. After simulating the case and making sure about the validity of the model, experiments were designed to study the effects of change in individuals and equipment on the average time that patients wait, as well as units' engagement in ED. Objective functions determined using Artificial Neural Network algorithm and MATLAB software were used to train it. Finally, after estimating objective functions and adding related constraints to the problem, a fractional Genetic Algorithm was used to solve the model.

Results : According to the model optimization result, it was determined that the hospitalization unit, as well as the hospitalization units' doctors, were in an optimized condition, but the triage unit, as well as the fast track units' doctors, should be optimized. After experiments in which the average waiting time in the triage section reached near zero, the average waiting time in the screening section was reduced to 158.97 minutes and also the coefficient of units' engagement in both sections were 69% and 84%, respectively.

Conclusions : Using the service optimization method creates a significant improvement in patient's waiting time and stream at emergency departments, which is made possible through appropriate allocation of the human and material resources.

Hosseini Shokouh Sayyed Morteza, Mohammadi Kasra, Yaghoubi Maryam

2022

Efficiency, Emergency Service, Hospital, Operations Research, Patients

General General

Source code Optimized Parallel Inception: A fast COVID-19 screening software.

In Software impacts

COVID-19 and swine-origin influenza A (H1N1) are both pandemics that sparked significant concern worldwide. These two viruses have the same symptoms and occur at a collision timeline. Optimized Parallel Inception (OPI) presents a new strategy to screen the COVID-19 from H1N1 with use of only symptoms. In this paper the process of preprocessing, screening, and specifying feature importance by OPI and particle swarm optimization is presented. Experimental results indicate 98.88 accuracy for screening COVID-19, H1N1, and Neither COVID-19 Nor H1N1.

Tavakolian Alireza, Hajati Farshid, Rezaee Alireza, Fasakhodi Amirhossein Oliaei, Uddin Shahadat

2022-Jun-22

COVID-19, Coronavirus, Deep learning, H1N1 virus, Outbreak, Screening

General General

Forecasting new diseases in low-data settings using transfer learning.

In Chaos, solitons, and fractals

Recent infectious disease outbreaks, such as the COVID-19 pandemic and the Zika epidemic in Brazil, have demonstrated both the importance and difficulty of accurately forecasting novel infectious diseases. When new diseases first emerge, we have little knowledge of the transmission process, the level and duration of immunity to reinfection, or other parameters required to build realistic epidemiological models. Time series forecasts and machine learning, while less reliant on assumptions about the disease, require large amounts of data that are also not available in early stages of an outbreak. In this study, we examine how knowledge of related diseases can help make predictions of new diseases in data-scarce environments using transfer learning. We implement both an empirical and a synthetic approach. Using data from Brazil, we compare how well different machine learning models transfer knowledge between two different dataset pairs: case counts of (i) dengue and Zika, and (ii) influenza and COVID-19. In the synthetic analysis, we generate data with an SIR model using different transmission and recovery rates, and then compare the effectiveness of different transfer learning methods. We find that transfer learning offers the potential to improve predictions, even beyond a model based on data from the target disease, though the appropriate source disease must be chosen carefully. While imperfect, these models offer an additional input for decision makers for pandemic response.

Roster Kirstin, Connaughton Colm, Rodrigues Francisco A

2022-Aug

COVID-19, Epidemic forecasting, Machine learning, Transfer learning, Zika

General General

Relating instance hardness to classification performance in a dataset: a visual approach.

In Machine learning

Machine Learning studies often involve a series of computational experiments in which the predictive performance of multiple models are compared across one or more datasets. The results obtained are usually summarized through average statistics, either in numeric tables or simple plots. Such approaches fail to reveal interesting subtleties about algorithmic performance, including which observations an algorithm may find easy or hard to classify, and also which observations within a dataset may present unique challenges. Recently, a methodology known as Instance Space Analysis was proposed for visualizing algorithm performance across different datasets. This methodology relates predictive performance to estimated instance hardness measures extracted from the datasets. However, the analysis considered an instance as being an entire classification dataset and the algorithm performance was reported for each dataset as an average error across all observations in the dataset. In this paper, we developed a more fine-grained analysis by adapting the ISA methodology. The adapted version of ISA allows the analysis of an individual classification dataset by a 2-D hardness embedding, which provides a visualization of the data according to the difficulty level of its individual observations. This allows deeper analyses of the relationships between instance hardness and predictive performance of classifiers. We also provide a