Dual swin transformer for assisting in the diagnosis and surgical prediction of necrotizing enterocolitis.

BACKGROUND: The diagnosis and surgical prediction of necrotizing enterocolitis (NEC) remain challenging. Our goal is to develop an interpretable multimodal artificial intelligence model to assist these key clinical decisions. METHODS: This retrospective study included 484 neonates (242 with NEC, 242 without NEC). We developed a dual Swin Transformer integrating abdominal X-rays (2D branch) and laboratory parameters (1D branch) via late fusion. The model was refined using an external data domain adaptation strategy (n = 50) and evaluated on independent internal and external test sets. The interpretability of the model was evaluated by Grad-CAM and SHAP. RESULTS: The optimized multimodal model showed high performance on the internal test set, achieving AUCs of 0.915 for NEC diagnosis and 0.920 for surgical prediction. On the independent external test set, it achieved AUCs of 0.903 (diagnosis) and 0.894 (surgical prediction), significantly outperforming baseline models. Interpretability analyses highlighted clinically relevant features, including intestinal pneumatosis and specific inflammatory markers (such as C-reactive protein) as key predictive factors. CONCLUSIONS: The dual Swin Transformer provides an accurate, interpretable, and adaptable multimodal tool that integrates radiographic and laboratory data to support NEC diagnosis and personalized surgical decision-making. IMPACT: This study developed a dual Swin Transformer, which integrates abdominal X-rays and laboratory data to provide a robust multimodal framework for the diagnosis and surgical prediction of necrotizing enterocolitis. By implementing an external data domain adaptation strategy, the study contributes to overcoming the key challenge of clinical heterogeneity and temporal variability in NEC cohorts. Using Grad-CAM and SHAP visualization to identify specific predictive characteristics improves model transparency and clinician trust. These findings provide an explainable and adaptable AI tool to support evidence-based and personalized clinical decision-making in neonatal intensive care.