From pixels to clinical insight: Computer vision and deep learning for automated left ventricular segmentation and ejection fraction prediction in pediatric echocardiography videos

Abstract

This study aims to explore the fusion of medical imaging and advanced machine learning in echocardiography, an essential diagnostic tool for heart assessments, especially in the pediatric domain. While echocardiography is widely used, challenges specific to the pediatric population emphasize the need for innovation. Beginning with investigating the heart’s complex structures, the journey of this research dives into reviewing the existing literature on pediatric echocardiography, addressing its advantages and limitations.

Central to the methodology is the first extensive pediatric echocardiographic video dataset available as of early 2023 from Stanford's Center for Artificial Intelligence in Medicine & Imaging, named EchoNet-Pediatric. Expert-annotated echocardiograms lay the foundation for computer vision algorithms and deep learning models on which they are trained and validated.

The results, based on key performance metrics, demonstrate the capabilities of integrating Artificial Intelligence in pediatric echocardiography, showcasing the synergy between traditional medical practices and cutting-edge advancements.