Automated Echocardiographic Detection of Mitral Valve Prolapse and Mitral Regurgitation with Video-based Artificial Intelligence Algorithms
AI algorithms can perform automatic detection of MVP and clinically significant MR from echocardiogram studies with high performance. The MVP DNN performed particularly well for more severe MVP phenotypes such as mitral annular disjunction or bileaflet MVP. These algorithms could provide a novel approach for automated, accurate, and rapid diagnosis of MVP and its common clinical sequelae across institutions.
Authors
- M. Perrocheau
- Lionel Tastet (ORCID: https://orcid.org/0000-0002-6374-5398)
- Dwight Bibby
- Valentina Crudo (ORCID: https://orcid.org/0000-0002-2465-8830)
- J. Barrios
- Dipan J. Shah (ORCID: https://orcid.org/0000-0002-6179-2393)
- Luca Cristin (ORCID: https://orcid.org/0009-0003-2932-8659)
- Farzin Arya
- Rohit Jhawar
- Minhaj Ansari
- Amy H. Rich
- Qizhi Fang
- Thuy Nguyen
- Geoffrey H Tison
Institutions
- Houston Methodist (US)
- University of California, San Francisco (US)
Publication Details
- Journal
- medRxiv
- Published
- 2026-03-02
- DOI
- https://doi.org/10.64898/2026.02.26.26347229
- Primary Topic
- Cardiac Valve Diseases and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institutes of Health
- National Heart, Lung, and Blood Institute