Health AI Central
Peer-Reviewed Publication
Am J Prev Cardiol2026;27101539.June 1, 2026Journal Article

Artificial intelligence-enabled electrocardiography to triage echocardiography for structural heart disease diagnosis in a low-resource setting.

Aline F Pedroso1,2, Bruno Ramos Nascimento3,4,5, Lovedeep S Dhingra1,2, Sumukh V Shankar1,2, Wanessa C Vinhal6, Raldner Borges E Reges6, Clareci S Cardoso6, Craig Sable5, Antonio L P Ribeiro3,6, Rohan Khera1,2,7,8,9
1Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
2Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT, USA.
3Departamento de Clinica Medica, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brasil.
4Interventional Cardiology, Hospital Madre Teresa, Belo Horizonte, MG, Brazil.
5Congenital Heart Center, Ochsner Children's Hospital, New Orleans, LA, United States.
6Centro de Telessaude, Hospital das Clínicas & Departamento de Clinica Medica, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
7Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA.
8Department of Biomedical Informatics, Yale School of Medicine, New Haven, CT, USA.
9Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
PubMed PMC DOI PDF

Abstract

BACKGROUND: Timely diagnosis of structural heart disease (SHD) is often constrained by limited access to echocardiography in low-resource settings. Although electrocardiography (ECG) is widely available, traditional interpretation poorly reflects underlying SHD risk, leading to inefficient referral for echocardiography. METHODS: We used data from PROVAR+, a large community-based cardiovascular sc…

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