AIMC Topic: Diagnosis, Computer-Assisted

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Multiclass machine learning vs. conventional calculators for stroke/CVD risk assessment using carotid plaque predictors with coronary angiography scores as gold standard: a 500 participants study.

The international journal of cardiovascular imaging Nov 12, 2020
Machine learning (ML)-based algorithms for cardiovascular disease (CVD) risk assessment have shown promise in clinical decisions. However, they usually predict binary events using only conventional risk factors. Our overall goal was to develop the "m...
Ultrasonography Support Vector Machine Predictive Value of Tests Coronary Angiography Middle Aged Risk Assessment Diagnosis, Computer-Assisted Pilot Projects Carotid Stenosis Coronary Stenosis Radiographic Image Interpretation, Computer-Assisted Machine Learning Stroke Decision Support Techniques Heart Disease Risk Factors Prognosis Plaque, Atherosclerotic Female Reproducibility of Results Aged Humans Cross-Sectional Studies Male
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Recent Advances in Medical Image Processing.

Acta cytologica Nov 11, 2020
BACKGROUND: Application and development of the artificial intelligence technology have generated a profound impact in the field of medical imaging. It helps medical personnel to make an early and more accurate diagnosis. Recently, the deep convolutio...
Humans Neural Networks, Computer Reproducibility of Results Diagnosis, Computer-Assisted Image Interpretation, Computer-Assisted Deep Learning Predictive Value of Tests
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Neural Network Vessel Lumen Regression for Automated Lumen Cross-Section Segmentation in Cardiovascular Image-Based Modeling.

Cardiovascular engineering and technology Nov 11, 2020
PURPOSE: We accelerate a pathline-based cardiovascular model building method by training machine learning models to directly predict vessel lumen surface points from computed tomography (CT) and magnetic resonance (MR) medical image data.
Blood Vessels Magnetic Resonance Angiography Computed Tomography Angiography Models, Cardiovascular Patient-Specific Modeling Diagnosis, Computer-Assisted Predictive Value of Tests Radiographic Image Interpretation, Computer-Assisted Automation Humans Neural Networks, Computer Reproducibility of Results
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Predictive modeling of proliferative vitreoretinopathy using automated machine learning by ophthalmologists without coding experience.

Scientific reports Nov 11, 2020
We aimed to assess the feasibility of machine learning (ML) algorithm design to predict proliferative vitreoretinopathy (PVR) by ophthalmologists without coding experience using automated ML (AutoML). The study was a retrospective cohort study of 506...
Humans Machine Learning Algorithms Retrospective Studies Female Risk Factors Aged Vitrectomy Male Ophthalmologists Middle Aged Retinal Detachment Postoperative Complications Vitreoretinopathy, Proliferative Diagnosis, Computer-Assisted
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Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death.

Circulation research Nov 10, 2020
RATIONALE: Susceptibility to VT/VF (ventricular tachycardia/fibrillation) is difficult to predict in patients with ischemic cardiomyopathy either by clinical tools or by attempting to translate cellular mechanisms to the bedside.
Death, Sudden, Cardiac Electrophysiologic Techniques, Cardiac Myocardial Infarction Tachycardia, Ventricular Cardiomyopathies Ventricular Fibrillation Middle Aged Risk Assessment Diagnosis, Computer-Assisted Time Factors Humans Neural Networks, Computer Risk Factors Phenotype Aged Prospective Studies Aged, 80 and over Prognosis Male Female Support Vector Machine Action Potentials Predictive Value of Tests Signal Processing, Computer-Assisted
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CheXLocNet: Automatic localization of pneumothorax in chest radiographs using deep convolutional neural networks.

PloS one Nov 9, 2020
BACKGROUND: Pneumothorax can lead to a life-threatening emergency. The experienced radiologists can offer precise diagnosis according to the chest radiographs. The localization of the pneumothorax lesions will help to quickly diagnose, which will be ...
Image Processing, Computer-Assisted Radiography Pneumothorax Humans Neural Networks, Computer Diagnosis, Computer-Assisted Deep Learning
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Deep Learning-Based Segmentation and Quantification in Experimental Kidney Histopathology.

Journal of the American Society of Nephrology : JASN Nov 5, 2020
BACKGROUND: Nephropathologic analyses provide important outcomes-related data in experiments with the animal models that are essential for understanding kidney disease pathophysiology. Precision medicine increases the demand for quantitative, unbiase...
Neural Networks, Computer Algorithms Reproducibility of Results Mice, Inbred C57BL Kidney Diseases Kidney Glomerulus Translational Research, Biomedical Periodic Acid Schiff Bases Pattern Recognition, Automated Animals Image Processing, Computer-Assisted Male Mice Diagnosis, Computer-Assisted Deep Learning Kidney Disease Models, Animal
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Cumulative learning enables convolutional neural network representations for small mass spectrometry data classification.

Nature communications Nov 5, 2020
Rapid and accurate clinical diagnosis remains challenging. A component of diagnosis tool development is the design of effective classification models with Mass spectrometry (MS) data. Some Machine Learning approaches have been investigated but these ...
Humans Neural Networks, Computer Machine Learning Deep Learning Databases, Factual Animals Mass Spectrometry Diagnosis, Computer-Assisted
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Predictive diagnosis of chronic obstructive pulmonary disease using serum metabolic biomarkers and least-squares support vector machine.

Journal of clinical laboratory analysis Nov 3, 2020
OBJECTIVE: Development of biofluid-based biomarkers is attractive for the diagnosis of chronic obstructive pulmonary disease (COPD) but still lacking. Thus, here we aimed to identify serum metabolic biomarkers for the diagnosis of COPD.
Male Middle Aged Case-Control Studies Diagnosis, Computer-Assisted Biomarkers Female Aged Pulmonary Disease, Chronic Obstructive Humans Artificial Intelligence Support Vector Machine Metabolomics Least-Squares Analysis Magnetic Resonance Spectroscopy
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Artificial intelligence in endoscopy: Present and future perspectives.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society Nov 3, 2020
Artificial intelligence (AI) has been attracting considerable attention as an important scientific topic in the field of medicine. Deep-leaning (DL) technologies have been applied more dominantly than other traditional machine-learning methods. They ...
Humans Artificial Intelligence Machine Learning Endoscopy Diagnosis, Computer-Assisted
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