medRxiv : the preprint server for health sciences · 2026
Automated Echocardiographic Detection of Mitral Valve Prolapse and Mitral Regurgitation with Video-based Artificial Intelligence Algorithms.
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Ansari Minhaj U, Barrios Joshua P, Tastet Lionel, Jhawar Rohit, Cristin Luca, Rich Amy, Bibby Dwight, Fang Qizhi, Arya Farzin, Crudo Valentina, Nguyen Thuy, Shah Dipan J, Delling Francesca N, Tison Geoffrey H
Аннотация
AIMS: We aimed to develop and evaluate fully automated artificial intelligence (AI) system. for detection of mitral valve prolapse (MVP) and mitral regurgitation (MR) from echocardiographic studies.
METHODS AND RESULTS: We used a dataset of 24,869 echocardiographic studies from the University of California San Francisco (UCSF) to train a multi-view deep neural network (DNN) to detect MVP using apical 4-chamber, 2-chamber, and parasternal long-axis views. A separate dataset of 27,906 studies from UCSF was used to train a second multi-view DNN model to detect moderate-to-severe or severe MR using color Doppler in the same views. External validation was performed on echocardiographic MVP videos from Houston Methodist Hospital.The DNN model for MVP detection achieved an AUC of 0.917 (95% CI: 0.899-0.934), with stronger performance in those with mitral annular disjunction or bileaflet MVP. External validation for MVP detection in a geographically and demographically distinct population yielded an AUC of 0.835 (95% CI: 0.803-0.869). The DNN for detection of moderate-to-severe or severe MR in patients with concurrent MVP achieved an AUC of 0.877 (95% CI: (0.805-0.939).
CONCLUSIONS: 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.
METHODS AND RESULTS: We used a dataset of 24,869 echocardiographic studies from the University of California San Francisco (UCSF) to train a multi-view deep neural network (DNN) to detect MVP using apical 4-chamber, 2-chamber, and parasternal long-axis views. A separate dataset of 27,906 studies from UCSF was used to train a second multi-view DNN model to detect moderate-to-severe or severe MR using color Doppler in the same views. External validation was performed on echocardiographic MVP videos from Houston Methodist Hospital.The DNN model for MVP detection achieved an AUC of 0.917 (95% CI: 0.899-0.934), with stronger performance in those with mitral annular disjunction or bileaflet MVP. External validation for MVP detection in a geographically and demographically distinct population yielded an AUC of 0.835 (95% CI: 0.803-0.869). The DNN for detection of moderate-to-severe or severe MR in patients with concurrent MVP achieved an AUC of 0.877 (95% CI: (0.805-0.939).
CONCLUSIONS: 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.