A deep and handcrafted feature fusion model detects pediatric congenital heart disease from phonocardiograms with 92% accuracy, 91% sensitivity, and 96% AUROC on a patient-wise held-out test set from 751 subjects.
Huang, et al., Applying artificial intelligence to wearable sensor data to diagnose and predict cardiovascular disease: A review, Sensors 22 (2022) 8002
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Automated detection of pediatric congenital heart disease from phonocardiograms using deep and handcrafted feature fusion
A deep and handcrafted feature fusion model detects pediatric congenital heart disease from phonocardiograms with 92% accuracy, 91% sensitivity, and 96% AUROC on a patient-wise held-out test set from 751 subjects.