ArticlemedRxiv : the preprint server for health sciences2025
Identification of Hypertrophic Cardiomyopathy on Electrocardiographic Images with Deep Learning.
Veer Sangha, Lovedeep Singh Dhingra, Arya Aminorroaya, Philip M Croon, Nikhil V Sikand, Sounok Sen, Matthew W Martinez, Martin S Maron, Harlan M Krumholz, Folkert W Asselbergs and 2 more
Open access · greenAbstract readPreprint
In one paragraphArticle in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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2 · The registryThe trial behind it
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3 · Its place in the literatureWho cites it
0 citing papers in PubMed, 25 citations in OpenAlex.
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4 · The recordCorrections and comments
5 · Who and what moneyAuthors and funding
12 authors at 4 institutions in 3 countries.
Veer SanghaSection of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.ORCID 0000-0002-8524-1203 Lovedeep Singh DhingraSection of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.ORCID 0000-0002-5664-4126 Arya AminorroayaSection of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.ORCID 0000-0003-3197-2657 Philip M CroonDepartment of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, Netherlands.
Nikhil V SikandSection of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
Sounok SenSection of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.ORCID 0000-0002-1806-3566 Matthew W MartinezDepartment of Cardiovascular Medicine, Atlantic Health, Morristown Medical Center, Morristown, NJ, USA.
Martin S MaronHCM Institute, Tufts Medical Center, Boston, Massachusetts, USA.
Harlan M KrumholzSection of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.ORCID 0000-0003-2046-127X Folkert W AsselbergsDepartment of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, Netherlands.
Evangelos K OikonomouSection of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.ORCID 0000-0003-4362-0720 Rohan KheraSection of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.ORCID 0000-0001-9467-6199 Yale University · USYale New Haven Hospital · USNational Institute for Health Research · GBTufts Medical Center · US
Funding
Deep learning enhanced detection and personalized monitoring of aortic stenosis - The DETECT-AS StudyR01AG089981 · NIA · YALE UNIVERSITY · PI Rohan Khera · 2024 to 2026
$2.4MTranslating Personalized Inference from Randomized Clinical Trials to Real-World Cardiovascular CareR01HL167858 · NHLBI · YALE UNIVERSITY · PI Rohan Khera · 2024 to 2026
$2.3MEvaluating and Improving Utilization of Evidence-Based Medical Therapy in Patients with Heart Failure using Automated Tools in the Electronic Health RecordK23HL153775 · NHLBI · YALE UNIVERSITY · PI KHERA, ROHAN · 2021 to 2025
$918kA multi-modal approach for efficient, point-of-care screening of hypertrophic cardiomyopathyF32HL170592 · NHLBI · YALE UNIVERSITY · PI OIKONOMOU, EVANGELOS · 2023 to 2024
$166kNHLBI NIH HHS F32 HL170592NHLBI NIH HHS K23 HL153775NHLBI NIH HHS R01 HL167858NIA NIH HHS R01 AG089981
6 · The paper itselfAbstract
Hypertrophic cardiomyopathy (HCM) is frequently underdiagnosed. While 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, Amsterdam University Medical Center (AUMC), and UK Biobank, where HCM was defined by CMR (YNHH, MIMIC-IV, AUMC) and diagnosis codes (UK Biobank). The model demonstrated robust performance across image formats and sites (AUROCs: 0.95 internal testing; 0.94 MIMIC-IV; 0.92 AUMC; 0.91 UK Biobank). Discriminative features localized to anterior/lateral leads (V4-V5) regardless of layout. This approach enables scalable, image-based screening for HCM across clinical settings.
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PMID38234746
PMCPMC10793540
OpenAlexW4390347844
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