Leveraging Comprehensive Echo Data to Power Artificial Intelligence Models for Handheld Cardiac Ultrasound

Leveraging Comprehensive Echo Data to Power Artificial Intelligence Models for Handheld Cardiac Ultrasound

Objective: To develop a fully end-to-end deep learning framework capable of estimating left ventricular ejection fraction (LVEF), estimating patient age, and classifying patient sex from echocardiographic videos, including videos collected using handheld cardiac ultrasound (HCU). Patients and Method...

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Main Authors: D.M. Anisuzzaman, PhD, Jeffrey G. Malins, PhD, John I. Jackson, PhD, Eunjung Lee, PhD, Jwan A. Naser, MBBS, Behrouz Rostami, PhD, Grace Greason, BA, Jared G. Bird, MD, Paul A. Friedman, MD, Jae K. Oh, MD, Patricia A. Pellikka, MD, Jeremy J. Thaden, MD, Francisco Lopez-Jimenez, MD, MSc, MBA, Zachi I. Attia, PhD, Sorin V. Pislaru, MD, PhD, Garvan C. Kane, MD, PhD
Format: Article
Language:English
Published: Elsevier 2025-03-01
Series:Mayo Clinic Proceedings: Digital Health
Online Access:http://www.sciencedirect.com/science/article/pii/S294976122500001X
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