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Identification of hypertrophic cardiomyopathy on electrocardiographic images with deep learning

Sangha, Veer; Dhingra, Lovedeep Singh; Aminorroaya, Arya; Croon, Philip M; Sikand, Nikhil V; Sen, Sounok; Martinez, Matthew W; ... Khera, Rohan; + view all (2025) Identification of hypertrophic cardiomyopathy on electrocardiographic images with deep learning. Nature Cardiovascular Research , 4 pp. 991-1000. 10.1038/s44161-025-00685-3.

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Abstract

Hypertrophic cardiomyopathy (HCM) is frequently underdiagnosed. Although deep learning (DL) models using raw electrocardiographic (ECG) voltage data can enhance detection, their use at the point of care is limited. Here we report the development and validation of a DL model that detects HCM from images of 12-lead ECGs across layouts. The model was developed using 124,553 ECGs from 66,987 individuals at the Yale New Haven Hospital (YNHH), with HCM features determined by concurrent imaging (cardiac magnetic resonance (CMR) or echocardiography). External validation included ECG images from MIMIC-IV, the Amsterdam University Medical Center (AUMC) and the UK Biobank (UKB), where HCM was defined by CMR (YNHH, MIMIC-IV and AUMC) and diagnosis codes (UKB). The model demonstrated robust performance across image formats and sites (areas under the receiver operating characteristic curve (AUROCs): 0.95 internal testing; 0.94 MIMIC-IV; 0.92 AUMC; 0.91 UKB). Discriminative features localized to anterior/lateral leads (V4 and V5) regardless of layout. This approach enables scalable, image-based screening for HCM across clinical settings.

Type: Article
Title: Identification of hypertrophic cardiomyopathy on electrocardiographic images with deep learning
Location: England
DOI: 10.1038/s44161-025-00685-3
Publisher version: https://doi.org/10.1038/s44161-025-00685-3
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Cardiac hypertrophy
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology
URI: https://discovery.ucl.ac.uk/id/eprint/10215369
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