To explore or not: machine learning models for intraoperative decision on testicular exploration in infants under 3 months with incarcerated inguinal hernia.
Authors
Affiliations (4)
Affiliations (4)
- National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, 400014, China.
- Department of General Surgery and Trauma Surgery, Children's Hospital of Chongqing Medical University, Chongqing, 400014, China.
- National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, 400014, China. [email protected].
- Department of General Surgery and Trauma Surgery, Children's Hospital of Chongqing Medical University, Chongqing, 400014, China. [email protected].
Abstract
To develop a machine learning (ML) model for preoperative prediction of testicular necrosis risk in male infants under 3 months with incarcerated inguinal hernia (IIH), addressing the limitations of current assessment methods. We retrospectively analyzed 288 male infants under 3 months with IIH who underwent emergency surgery. Key preoperative variables (testicular blood flow, echotexture, incarceration duration, procalcitonin, neutrophil-to-lymphocyte ratio) were used to train ten ML models. Performance was evaluated using ROC AUC, PR AUC, accuracy, precision, recall, and F1-score. SHAP analysis assessed interpretability. The Gradient Boosting model performed best, achieving a ROC AUC of 0.940 and a recall of 0.889. SHAP identified absent testicular blood flow, heterogeneous echotexture, prolonged incarceration, and elevated procalcitonin and neutrophil-to-lymphocyte ratio as top predictors. This ML model predicts testicular necrosis risk preoperatively. By integrating color Doppler ultrasound, serological markers, and clinical data, it offers an interpretable tool to guide selective testicular exploration, potentially optimizing outcomes. However, given the single-center retrospective nature of this study, external validation in multi-center prospective cohorts is required before clinical implementation.