Ultrasound combined with serological markers for predicting neonatal necrotizing enterocolitis: a machine learning approach

作者：Yi Yang, Shoulan Zhou, Xiaomin Liu, Yanhong Zhang, Liping Lin, Chenhan Zheng, Xiaohong Zhong · 发表于：Frontiers in Pediatrics · 年份：2025 · DOI：10.3389/fped.2025.1606571 · 被引用次数：3 · 研究领域：Infant Nutrition and Health、Preterm Birth and Chorioamnionitis、Neonatal and Maternal Infections

Background & aims: Neonatal necrotizing enterocolitis (NEC) remains a leading cause of morbidity and mortality in preterm infants. Current diagnostic methods, relying on clinical signs and radiography, often lack sensitivity for early detection. This study aimed to develop and validate a machine learning (ML) model integrating ultrasound and serological markers to improve NEC prediction in neonates. Methods: This retrospective, case-control study included 191 neonates (cases with Bell's stage ≥ II NEC and matched controls) admitted to a tertiary NICU. Data were extracted from electronic medical records, including demographics, clinical variables, ultrasound findings (bowel wall thickness, edema, gas location, peristalsis, seroperitoneum), and serological markers (WBC, neutrophil count, CRP, ALP, albumin, procalcitonin, platelet count, INR, hemoglobin). Twelve ML algorithms were evaluated using 10-fold cross-validation on a training set (70%). The optimal model was selected based on AUC-ROC and further optimized via hyperparameter tuning. Model performance was assessed on an independent validation set (30%). Explainable AI (XAI) using SHAP values was employed to identify key predictive features. Results: XGBoost demonstrated the highest performance (AUC = 0.97, 95% CI: 0.92-0.99) during cross-validation. The optimized XGBoost fusion model-Ultrasound combined Serological Predict NEC (USPN) achieved an AUC of 0.88 (95% CI: 0.76-0.99) in the validation set, with a sensitivity of ...