Machine Learning-Enhanced Echocardiography for the Detection of Coronary Artery Disease: A Scoping Review Protocol
Wagner Rios-García, Erick Barrientos-Ventura, Victoria E. Butrón-Verástegui, Daniela E. Oriundo-Arbizu, Kehit A. Velasquez-Taipe, Abigail D. Via-y-Rada-Torres, Alondra A. Rios-Garcia
Coronary artery disease (CAD) remains a leading cause of morbidity and mortality worldwide. Echocardiography is widely available and provides real-time structural and functional assessment, but diagnostic accuracy is limited by operator dependency. Machine learning (ML) and deep learning (DL) approaches have emerged as promising tools to enhance echocardiographic interpretation. However, evidence remains fragmented, and no comprehensive synthesis exists focusing exclusively on ML-enhanced echocardiography for CAD detection. This protocol outlines a scoping review to map the available evidence.