AIMC Topic: Sex Factors

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Selecting the best machine learning algorithm to support the diagnosis of Non-Alcoholic Fatty Liver Disease: A meta learner study.

PloS one Oct 20, 2020
BACKGROUND & AIMS: Liver ultrasound scan (US) use in diagnosing Non-Alcoholic Fatty Liver Disease (NAFLD) causes costs and waiting lists overloads. We aimed to compare various Machine learning algorithms with a Meta learner approach to find the best ...
Age Factors Glucose Non-alcoholic Fatty Liver Disease Sex Factors Adult Cross-Sectional Studies Humans Male Machine Learning Middle Aged Algorithms Child, Preschool Female Ultrasonography Risk Factors
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Chest x-ray analysis with deep learning-based software as a triage test for pulmonary tuberculosis: a prospective study of diagnostic accuracy for culture-confirmed disease.

The Lancet. Digital health Oct 19, 2020
BACKGROUND: Deep learning-based radiological image analysis could facilitate use of chest x-rays as triage tests for pulmonary tuberculosis in resource-limited settings. We sought to determine whether commercially available chest x-ray analysis softw...
Sex Factors X-Rays Triage Radiology Female Humans Sputum Software Pakistan Prospective Studies Tuberculosis, Pulmonary Lung Mycobacterium tuberculosis Deep Learning HIV Infections Young Adult Image Processing, Computer-Assisted Adult Body Mass Index Sensitivity and Specificity Age Factors Male Middle Aged
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Temporal Trends in Cervical Spine Curvature of South Korean Adults Assessed by Deep Learning System Segmentation, 2006-2018.

JAMA network open Oct 1, 2020
IMPORTANCE: The loss of the physiologic cervical lordotic curve is a common degenerative disorder known to be associated with abnormal spinal alignment. However, the changing trends among sex and age groups has not yet been well established.
Humans Retrospective Studies Republic of Korea Female Age Factors Cervical Vertebrae Sex Factors Kyphosis Lordosis Spinal Curvatures Young Adult Adult Cross-Sectional Studies Male Middle Aged Deep Learning
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Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients: analysis of 697 Italian patients.

European radiology Sep 18, 2020
OBJECTIVE: To evaluate whether the initial chest X-ray (CXR) severity assessed by an AI system may have prognostic utility in patients with COVID-19.
Humans Artificial Intelligence COVID-19 Prognosis Retrospective Studies Tomography, X-Ray Computed Male Middle Aged Comorbidity Severity of Illness Index Deep Learning Age Factors Intensive Care Units Female SARS-CoV-2 Hospitalization Aged Pulmonary Disease, Chronic Obstructive Proportional Hazards Models Radiography, Thoracic Mortality Radiography Emergency Service, Hospital Italy Neurodegenerative Diseases Coronary Artery Disease Sex Factors
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Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: an international multicentre study.

The European respiratory journal Aug 20, 2020
BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality.
Sex Factors Survival Analysis Internationality Belgium Clinical Laboratory Techniques Pneumonia, Viral China Cohort Studies Hospital Mortality ROC Curve Decision Support Systems, Clinical Age Factors Predictive Value of Tests Triage Humans Area Under Curve Machine Learning Italy Pandemics Coronavirus Infections COVID-19 Testing Aged Adult Male Middle Aged Risk Assessment COVID-19 Severity of Illness Index Retrospective Studies Female Reproducibility of Results Hospitalization
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The novel approaches to classify cyclist accident injury-severity: Hybrid fuzzy decision mechanisms.

Accident; analysis and prevention Jul 2, 2020
In this study, two novel fuzzy decision approaches, where the fuzzy logic (FL) model was revised with the C4.5 decision tree (DT) algorithm, were applied to the classification of cyclist injury-severity in bicycle-vehicle accidents. The study aims to...
Humans Fuzzy Logic Accidents, Traffic Sex Factors Injury Severity Score Built Environment Bicycling
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An artificial neural network approach for predicting hypertension using NHANES data.

Scientific reports Jun 30, 2020
This paper focus on a neural network classification model to estimate the association among gender, race, BMI, age, smoking, kidney disease and diabetes in hypertensive patients. It also shows that artificial neural network techniques applied to larg...
Body Mass Index Age Factors Models, Statistical Sex Factors Smoking Adult Sensitivity and Specificity Male Middle Aged Nutrition Surveys Hypertension Humans Neural Networks, Computer Algorithms Female Risk Factors
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Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas.

European radiology Jun 27, 2020
OBJECTIVES: To evaluate the differential diagnostic performance of a computed tomography (CT)-based deep learning nomogram (DLN) in identifying tuberculous granuloma (TBG) and lung adenocarcinoma (LAC) presenting as solitary solid pulmonary nodules (...
Algorithms Retrospective Studies Tomography, X-Ray Computed Female Adult Male Middle Aged Diagnosis, Computer-Assisted Calibration Deep Learning Adenocarcinoma of Lung ROC Curve Image Processing, Computer-Assisted Lung Neoplasms Granuloma Humans Tuberculosis Logistic Models Sex Factors Diagnostic Tests, Routine Nomograms Observer Variation Age Factors Pattern Recognition, Automated Solitary Pulmonary Nodule Regression Analysis Diagnosis, Differential
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Deep learning-based survival prediction for multiple cancer types using histopathology images.

PloS one Jun 17, 2020
Providing prognostic information at the time of cancer diagnosis has important implications for treatment and monitoring. Although cancer staging, histopathological assessment, molecular features, and clinical variables can provide useful prognostic ...
Sex Factors Survival Analysis Datasets as Topic Neoplasms Deep Learning Humans Image Processing, Computer-Assisted Prognosis Age Factors Female Neoplasm Staging Risk Factors Feasibility Studies Adult Male Middle Aged Risk Assessment
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Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis.

Proceedings of the National Academy of Sciences of the United States of America May 26, 2020
Artificial intelligence (AI) systems for computer-aided diagnosis and image-based screening are being adopted worldwide by medical institutions. In such a context, generating fair and unbiased classifiers becomes of paramount importance. The research...
Humans Radiography, Thoracic Female Male Deep Learning Bias Radiographic Image Interpretation, Computer-Assisted Sex Factors Datasets as Topic Reference Standards
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