AI and Rare Diseases ── Heart and lung ── 2026-10-07

2D echocardiographic images are pre-processed and sorted into five clinically relevant views. Distinctive features are then extracted independently from each view and fused to classify hypertrophic cardiomyopathy, cardiac amyloidosis or control. The cohort had 212 HCM, 119 CA and 200 control subjects. Fivefold cross-validation gave precision 0.83, sensitivity 0.81, specificity 0.89 and micro-F1 0.82, with internal evaluation only. — Image abstract — the whole article on one page (click to enlarge)

1 Heart and lung papers. Lead: A deep learning-driven pipeline for differentiating hypertrophic cardiomyopathy from cardiac amyloidosis using 2D multi-view echocardiography

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Rare cardiovascular and respiratory diseases: treatment, trials and AI

A deep learning-driven pipeline for differentiating hypertrophic cardiomyopathy from cardiac amyloidosis using 2D multi-view echocardiography
Scientific Reports · 2026-10-05 · Evidence: Diagnostic model development on an observational cohort (internal five-fold cross-validation only)
Abstract
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.
Journal IF-equivalent: 5.2 (OpenAlex 2-year mean citedness, value as of 2026-10-06, retrieved 2026-10-07; not the official Clarivate IF)
Reference: Li X, Peng B, Li X, Wang Z, Deng H, Luo X, et al. A deep learning-driven pipeline for differentiating hypertrophic cardiomyopathy from cardiac amyloidosis using 2D multi-view echocardiography. Sci Rep. 2026 Oct 5 [Epub ahead of print]. doi:10.1038/s41598-025-15443-5.
Checked: Abstract only

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