A deep learning-driven pipeline for differentiating hypertrophic cardiomyopathy from cardiac amyloidosis using 2D multi-view echocardiography

Hypertrophic Cardiomyopathy (HCM) and Cardiac Amyloidosis (CA) are two cardiac conditions that can advance to heart failure if left untreated, which present considerable diagnostic challenges due to their overlapping echocardiographic appearance. To address these challenges, this study develops a multi-view deep learning framework that classifies 2D echocardiographic data into five clinically relevant views after image pre-processing: apical 4-chamber, parasternal long axis of left ventricle, parasternal short axis at levels of the mitral valve, papillary muscle, and apex. The framework independently extracts distinctive features from each view, which are then fused for accurate disease classification. The cohort for this study included 212 patients with HCM, 119 with CA, and 200 control subjects with normal cardiac function, enrolled from 2018 to 2022. Utilizing fivefold cross-validation, the model demonstrated precision of 0.83, sensitivity of 0.81, specificity of 0.89, and a micro-F1 score of 0.82. These results affirm the effectiveness of the framework as a reliable diagnostic tool for differentiating between HCM and CA Using a clinical dataset.