Development and Multinational Validation of Artificial Intelligence-Enabled ASCVD Risk Stratification Using Electrocardiograms
We developed an ECG-ASCVD toolkit and validated it across diverse multinational cohorts. These results highlight the potential utility of resting ECG information for predicting ASCVD risk, enabling targeted screening.
Authors
- Bruno Batinica (ORCID: https://orcid.org/0000-0002-5525-9007)
- Aline F Pedroso (ORCID: https://orcid.org/0000-0002-1876-8304)
- Dhruva Biswas (ORCID: https://orcid.org/0000-0001-9141-5188)
- Arya Aminorroaya (ORCID: https://orcid.org/0000-0003-3197-2657)
- Antonio L P Ribeiro
- Rohan Khera
- Evangelos K Oikonomou
- Lovedeep S Dhingra
Institutions
- Universidade Federal de Minas Gerais (BR)
- Yale New Haven Hospital (US)
- Yale University (US)
- Yale New Haven Health System (US)
- Hospital das Clínicas da Universidade Federal de Minas Gerais (BR)
Publication Details
- Journal
- medRxiv
- Published
- 2026-01-06
- DOI
- https://doi.org/10.64898/2026.01.05.26343465
- Citations
- 1
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 30.07
Funders
- National Institutes of Health