Automated Echocardiographic Detection of Congenital Heart Disease Using Artificial Intelligence
EchoFocus-CHD shows promise for automated CHD detection and highlights the need to address domain shift for real-world deployment. By identifying high-risk CHD lesions, this approach could support triage, prioritize expert review, and optimize resource allocation, advancing more equitable global cardiovascular care.
Authors
- William La Cava (ORCID: https://orcid.org/0000-0002-1332-2960)
- JK Triedman
- Sunil J. Ghelani (ORCID: https://orcid.org/0000-0002-4227-8823)
- Nao Sasaki (ORCID: https://orcid.org/0000-0002-4279-6137)
- J. Mayourian
- Danielle Sganga
- Platon Lukyanenko
- Bohan Jiang
- Yuting Yang
- Timothy Miller
- David Michael Harrild
- Tal Geva
- Andrew Powell
- Francesca Sperotto
Institutions
- Boston Children's Hospital (US)
- Harvard University (US)
Publication Details
- Journal
- medRxiv
- Published
- 2026-01-26
- DOI
- https://doi.org/10.64898/2026.01.24.26344771
- Primary Topic
- Congenital Heart Disease Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00