Deep learning pipeline differentiates HCM from cardiac amyloidosis
Researchers developed a multi-view deep learning framework to distinguish between Hypertrophic Cardiomyopathy (HCM) and Cardiac Amyloidosis (CA), two conditions that often appear similar on echocardiograms. The system processes 2D echocardiographic data by classifying images into five specific clinical views: apical 4-chamber, parasternal long axis of left ventricle, parasternal short axis at…
Key points
- Framework classifies 2D echocardiographic data into five clinically relevant views for feature extraction.
- Study cohort included 212 HCM patients, 119 CA patients, and 200 control subjects enrolled 2018 to 2022.
- Model achieved precision of 0.83, sensitivity of 0.81, specificity of 0.89, and micro-F1 score of 0.82.
The study utilized a cohort of 212 patients with HCM, 119 with CA, and 200 control subjects with normal cardiac function, enrolled between 2018 and 2022. Using fivefold cross-validation, the model achieved a precision of 0.83, sensitivity of 0.81, specificity of 0.89, and a micro-F1 score of 0.82. The authors state these results confirm the framework's effectiveness as a reliable diagnostic tool. The work was supported by the Natural Science Foundation of Sichuan Province and used GPU resources from the High-Performance Computing Center at Southwest Petroleum University.
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