Machine learning algorithms are demonstrating clinical utility across the entire care spectrum for tetralogy of Fallot, the most prevalent cyanotic congenital heart defect affecting roughly 1 in 2,500 births. Deep learning models now enhance prenatal echocardiographic detection, while natural language processing extracts prognostic insights from electronic health records to predict complications like pulmonary regurgitation and right ventricular dysfunction that typically emerge decades after initial repair.
This technological integration addresses a critical gap in congenital cardiology, where patients require sophisticated lifelong monitoring but often transition between pediatric and adult care systems with fragmented oversight. AI-driven risk stratification could identify high-risk patients earlier, potentially preventing sudden cardiac death and optimizing timing for pulmonary valve replacement. The multimodal approach—combining imaging data, genomic markers, and continuous physiologic monitoring through wearables—represents a paradigm shift toward truly personalized medicine in complex congenital disease. However, the small patient populations inherent to rare diseases like tetralogy of Fallot create unique challenges for algorithm training and validation that differ markedly from AI applications in more common cardiovascular conditions.