Work

Identification of hypertrophic cardiomyopathy on electrocardiographic images with deep learning

Year: 2025

Type: article

Source: Nature Cardiovascular Research

DOI: 10.1038/s44161-025-00685-3

Authors: 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, Evangelos K. Oikonomou, Rohan Khera

Institutions: Yale University, University of Oxford, Amsterdam University Medical Centers, Amsterdam Cardiovascular Sciences, University of Amsterdam, Morristown Medical Center, Tufts Medical Center, Yale New Haven Hospital, National Institute for Health and Care Research, UCL Biomedical Research Centre, University College London

Language: English

Topic: ECG Monitoring and Analysis

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: U.S. Department of Health and Human Services, National Institutes of Health, National Heart, Lung, and Blood Institute

Awards: G4450967602, A multi-modal approach for efficient, point-of-care screening of hypertrophic cardiomyopathy, Deep learning enhanced detection and personalized monitoring of aortic stenosis - The DETECT-AS Study, G5809268895, G7375741648, Translating Personalized Inference from Randomized Clinical Trials to Real-World Cardiovascular Care, Evaluating and Improving Utilization of Evidence-Based Medical Therapy in Patients with Heart Failure using Automated Tools in the Electronic Health Record

OpenAlex ID: W4412552358

FWCI: 19.18

Cites: 51

Cited by: 38

Related to: 10