An ultrasound-based machine learning model for predicting pelvic adhesions: A SHAP-enhanced XGBoost approach

This study is the first to develop and evaluate a machine learning (ML) model for predicting pelvic adhesions based on ultrasound features, utilizing the SHapley Additive Explanations (SHAP) framework for interpretability analysis.

This prospective study included 220 patients who underwent laparoscopic surgery and preoperative ultrasound assessments at our hospital between April 2023 and June 2024. Patients were randomly assigned to training and validation sets. A Least Absolute Shrinkage and Selection Operator regression was used to identify independent risk factors, followed by incorporation into an Extreme Gradient Boosting prediction model. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration plots, and a decision curve analysis.

The included patients were randomly divided into a training set and a validation set in a 7:3 ratio. The final model included four predictors-obstructed ovarian activity, surgical history, endometriosis, and gynecological inflammation-and demonstrated strong discriminatory performance, with an area under the ROC curve of 0.869 and 0.846 in the training and validation sets, respectively. The ML model demonstrated a sensitivity of 0.946 and a specificity of 0.597 in the training set, while in the validation set, it achieved a sensitivity of 1.000 and a specificity of 0.600. Calibration analyses showed good agreement between predicted and observed outcomes. The model exhibited high clinical utility. SHAP analysis revealed that endometriosis contributed most significantly to the predictions, followed by surgical history, obstructed ovarian activity, and gynecological inflammation.

The interpretable ML model developed in this study demonstrates strong predictive performance for assessing the risk of pelvic adhesions in patients prior to surgery. It can be utilized to accurately identify high-risk patients before the procedure, enabling the implementation of appropriate measures during surgery to reduce the occurrence of postoperative pelvic adhesions.