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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
AbstractMachine 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 “multiclass machine learning (MCML)-based algorithms” (labelled as AtheroEdge 3.0ML) and assess whether considering carotid ultrasound imaging fused with conventional risk factors can provide better CVD/stroke risk prediction than conventional CVD risk calculators (CCVRC). Carotid ultrasound and coronary angiography were performed on 500 participants. Stenosis in the ...
Source: The International Journal of Cardiovascular Imaging - November 12, 2020 Category: Radiology Source Type: research

Ultrasound-based internal carotid artery plaque characterization using deep learning paradigm on a supercomputer: a cardiovascular disease/stroke risk assessment system
AbstractVisual or manual characterization and classification of atherosclerotic plaque lesions are tedious, error-prone, and time-consuming. The purpose of this study is to develop and design an automated carotid plaque characterization and classification system into binary classes, namely symptomatic and asymptomatic types via the deep learning (DL) framework implemented on a supercomputer. We hypothesize that on ultrasound images, symptomatic carotid plaques have (a) a low grayscale median because of a histologically large lipid core and relatively little collagen and calcium, and (b) a higher chaotic (heterogeneous) gra...
Source: The International Journal of Cardiovascular Imaging - January 9, 2021 Category: Radiology Source Type: research