Automated Detection of Clinically Significant Mitral Regurgitation from Single-View B-mode Echocardiography Using Deep Learning.

Roman A Sandler¹, Joseph Z Sokol², Shubhadarshini Pawar³, Anna Gilgur¹, Aashi Jhawer¹, Shirin Sadri³, Daniel B Sokol¹, Markos Muche¹, Aleksandar Stojmenski¹, Michael Guptan¹, Joshua Penn¹, Raj Makkar³, Aakriti Gupta⁴

¹iCardio.ai Corporation, Los Angeles, United States of America.

²iCardio.ai Corporation, Los Angeles, United States of America. Electronic address: https://twitter.com/JosephSokol4.

³Karsh Center for Interventional Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA.

⁴iCardio.ai Corporation, Los Angeles, United States of America; Karsh Center for Interventional Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA. Electronic address: aakriti.gupta@csmc.edu.

PubMed DOI

Abstract

INTRODUCTION: Mitral regurgitation (MR) is one of the most prevalent valvular heart diseases, and its diagnosis traditionally relies on Doppler echocardiography, which is subject to significant variability and technical challenges. We developed and externally validated MitralVision, a deep learning model for automated classification of clinically significant MR using single-view, B-mode echocardio…

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