[Abstract]Echocardiography is a vital diagnostic tool for congenital heart disease (CHD), but its interpretation relies heavily on manual labor, is time-consuming and labor-intensive, and suffers from diagnostic accuracy influenced by the skill level of the sonographer. Furthermore, pediatric hearts are small, have rapid heart rates, and exhibit poor patient compliance, further complicating the examination and creating a gap between clinical expectations and reality. In recent years, artificial intelligence (AI) technology, particularly machine learning, have emerged as powerful image analysis tools. They have demonstrated significant effectiveness in assisting echocardiographic detection of pediatric CHD, highlighting their clinical value. This article reviews the progress of AI applications in pediatric CHD echocardiography and identifies the challenges and issues encountered.
