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Purpose <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusion <p>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.</p>

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To explore or not: machine learning models for intraoperative decision on testicular exploration in infants under 3 months with incarcerated inguinal hernia

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月1日

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  • Yonghua Fu,
  • Guobin Liu,
  • Zhendi Tang,
  • Jiajia Zhou,
  • Shikun Qiu,
  • Xin Ge,
  • Xiaofeng Song,
  • Quan Kang

摘要

Purpose

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.

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.

Results

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.

Conclusion

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.