Chu Thi HA, Thi Hai Yen TRAN, Minh Nguyet PHAM, Phuong Linh DANG, Duy Quan VO
The Turkish Journal of Pediatrics - 2026;68(5):725-740
Background. Background and objectives: Patent ductus arteriosus (PDA) is a common congenital cardiac condition, especially in premature infants, and is associated with significant morbidity and mortality. Artificial intelligence (AI) has emerged as a promising tool to aid clinicians in diagnosing PDA and guiding treatment decisions amidst this uncertainty. Methods. A literature search (up to December 2025) was conducted in PubMed, Scopus, and Web of Science for peer-reviewed studies on AI (including machine learning, deep learning, computer vision, and predictive analytics) applied to PDA. Results. Seventeen studies were included, predominantly involving preterm neonates. AI applications mainly targeted PDA diagnosis and outcome prediction using clinical and echocardiographic data. Imaging-based deep learning models generally outperformed tabular data approaches (with area under the curve often >0.80). Overall study quality was moderate, with limited external validation and data transparency. Conclusions. AI has the potential to transform patent ductus arteriosus management by enabling earlier detection, more consistent decision-making, and individualized treatment planning. Integration of AI into PDA care may reduce practice variability, improve clinical outcomes, and serve as a model for the broader application of artificial intelligence in medicine.
