Artificial Intelligence-Assisted Fetal Echocardiography: Improving Early Detection and Neonatal Outcomes of Congenital Heart Disease - Patel, Karina; Agrawal, Devendra K (Cardiology and Cardiovascular Medicine, 2026)
Congenital Heart Disease (CHD) encompasses a range of structural abnormalities of the heart and great vessels present at birth and is associated with significant lifelong morbidity, including heart failure and arrhythmias. Early diagnosis is critical for optimizing perinatal management and improving outcomes. This review focuses on early fetal echocardiography, a specialized ultrasound technique that enables detection of cardiac abnormalities in utero, as early as 11–14 weeks of gestation, compared to traditional imaging performed at approximately 20 weeks. Advancements in early imaging have improved the identification of high-risk fetuses, particularly those with genetic predispositions or family history of CHD, allowing for earlier clinical decision-making and intervention planning. In select cases, prenatal detection facilitates in utero management of conditions such as aortic stenosis and hypoplastic left heart syndrome. Additionally, emerging applications of artificial intelligence (AI) have enhanced image analysis and diagnostic accuracy, supporting clinicians in the early recognition of CHD. Despite these advances, challenges remain, including variability in diagnostic accuracy in early gestation, risk of false positives, and limited access to specialized imaging technologies. Continued integration of AIdriven tools and the development of standardized screening protocols hold promise for improving diagnostic consistency and long-term outcomes in patients with CHD.