Artificial Intelligence in Adult Congenital Heart Disease: Diagnostic and Therapeutic Applications and Future Directions

Résumé fourni par la source

Adult congenital heart disease (ACHD) constitutes a heterogeneous and expanding patient cohort with distinctive diagnostic and management challenges. Conventional detection methods are ineffective at reflecting lesion heterogeneity and the variability in risk profiles. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL) models, has revolutionized the potential for improving diagnosis, risk stratification, and personalized care across the ACHD spectrum. This narrative review discusses the current and future applications of AI in ACHD, including imaging interpretation, electrocardiographic analysis, risk stratification, procedural planning, and long-term care management. AI has been demonstrated as being highly accurate in congenital anomaly detection by various imaging modalities, automating measurement, and improving diagnostic consistency. Moreover, AI has been utilized in electrocardiography to detect previously undetected defects and estimate arrhythmia risk. Risk-prediction models based on clinical and imaging information can estimate stroke, heart failure, and sudden cardiac death as outcomes, thereby informing personalized therapy choices. AI also contributes to surgery and interventional planning through three-dimensional (3D) modelling and image fusion, while AI-powered remote monitoring tools enable the detection of early signals of clinical deterioration. While these insights are encouraging, limitations in data availability, algorithmic bias, a lack of prospective validation, and integration issues remain to be addressed. Ethical considerations of transparency, privacy, and responsibility should also be highlighted. Thus, future initiatives should prioritize data sharing, explainability, and clinician training to facilitate the secure and effective use of AI. The appropriate integration of AI can enhance decision-making, improve efficiency, and deliver individualized, high-quality care to ACHD patients.

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