Accuracy of a Deep Learning Model in Intracardiac Echocardiography.

Devi Nair¹, Jeffrey Winterfield², Jonathan C Hsu³, Rakesh Gopinathannair⁴, Larry Chinitz⁵, Naga Venkata K Pothineni⁴, Frederick T Han³, Bishnu P Dhakal⁶, Chirag Barbhaiya⁵, Amish S Dave⁶, Fermin Garcia⁷, Matthew C Hyman⁷, Travis Dahlen⁸, Kristi Tanouye⁹, Jason Yao⁸, Shubhadarshini Pawar¹⁰, Judith Buckland⁹, Anna Gilgur¹¹, Raphael Elspas¹¹, Aakriti Gupta¹², Roman A Sandler¹¹, Joseph Z Sokol¹¹

¹St. Bernards Heart & Vascular Center, Jonesboro, Arkansas, USA.

²Medical University of South Carolina (MUSC), Charleston, South Carolina, USA.

³University of California San Diego (UCSD), La Jolla, California, USA.

⁴Kansas City Heart Rhythm Institute, Kansas City, Missouri, USA.

⁵NYU Langone Health, New York, New York, USA.

⁶Houston Methodist Hospital, Houston, Texas, USA.

⁷University of Pennsylvania, Philadelphia, Pennsylvania, USA.

⁸Abbott Laboratories, Chicago, Illinois, USA.

⁹CardioServ, Boynton Beach, Florida, USA.

¹⁰Karsh Center for Interventional Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.

¹¹iCardio.ai Corporation, Los Angeles, California, USA.

¹²Karsh Center for Interventional Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA; iCardio.ai Corporation, Los Angeles, California, USA. Electronic address: aakriti.gupta@csmc.edu.

PubMed PMC DOI PDF

Abstract

BACKGROUND: Intracardiac echocardiography (ICE) is widely used during electrophysiology and structural heart procedures; however, image interpretation remains operator-dependent and procedural views are not standardized. Although artificial intelligence has been increasingly applied to transthoracic and transesophageal echocardiography, applications to ICE remain limited. OBJECTIVES: The objectiv…

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