Enhancing bronchopulmonary dysplasia prediction in preterm infants using artificial intelligence and multimodal data integration

Bronchopulmonary dysplasia (BPD) remains a prevalent respiratory condition among preterm infants, with its development influenced by a combination of perinatal and postnatal factors. The development of artificial intelligence (AI) and machine learning (ML) technologies has provided new ideas for building BPD prediction models based on multimodal data (such as clinical information, physiological signals, imaging data, biomarkers, and omics data). This article systematically reviewed the research progress of AI in BPD prediction, analyzed representative models and key tools (such as RTI BPD Outcome Estimator), and assessed their performance and limitations in actual clinical settings. It also sorted out the challenges faced by AI models in clinical translation, including data standardization, model interpretability, system integration capabilities, model update mechanisms, and ethical and legal issues. To address the clinical need of "moving from prediction to intervention", this article discussed the PALM translation framework (Predict-Act-Learn-Monitor) organized around key clinical nodes. In the future, it is necessary to strengthen multi-center data sharing, develop privacy protection technologies such as federated learning, and build a design, validation, integration, regulation, and feedback closed-loop management system to help AI models move from risk prediction to precise intervention, ultimately improving the clinical outcomes of children with BPD.