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Peer-Reviewed Publication
Eur Heart J Digit Health2025;6(5):949-958.September 1, 2025Journal Article

Deep learning for atrioventricular regurgitation diagnosis: an external validation study.

Ido Cohen1,2, Jeffrey G Malins3, Michal Cohen-Shelly1,4,5, Yossi Asaf1,2, Michael Fiman4, Kobi Faierstein1,2, Lior Fisher1,2, Karin Sudri5, Ehud Raanani1,2,4, Ehud Schwammenthal1,2,4, Robert Klempfner1,2,4, Elad Maor1,2,4
1Leviev Cardiovascular Institute, Sheba Medical Center, Derech Sheba 2, Tel HaShomer, Ramat Gan 52621, Israel.
2Gray Faculty of Medical and Health Sciences, Tel Aviv University, Ramat Aviv, Tel Aviv 69978, Israel.
3Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
4Aisap.ai, Derech Sheba 2, Ramat Gan 5266202, Israel.
5ARC Innovation Center, Sagol AI Hub, Sheba Medical Center, Derech  Sheba  2, Ramat Gan  52621, Israel.
PubMed PMC DOI PDF

Abstract

AIMS: Mitral and tricuspid regurgitation (MR and TR) are common in older adults and associated with substantial morbidity and mortality. While transthoracic echocardiography (TTE) is the diagnostic gold standard, access remains limited in many care settings. Artificial intelligence (AI)-based echocardiographic analysis may help address this diagnostic gap. METHODS AND RESULTS: We externally valid…

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