Machine learning as a clinical decision support tool for diagnosing superficial peritoneal endometriosis in women with dysmenorrhea and acyclic pelvic pain

Background: Superficial peritoneal endometriosis, despite being the most common type of lesion, presents the greatest challenge for non-invasive diagnosis, resulting in the majority being recognised surgically. Objective: To evaluate the performance of machine learning in predicting superficial peritoneal endometriosis in women with chronic dysmenorrhoea and pelvic pain without abnormal ultrasound findings. Design: Retrospective observational study. Subjects: 298 women with severe dysmenorrhea and persistent acyclic pelvic pain after at least 6 months of hormonal treatment who underwent laparoscopy, with imaging examinations showing no significant abnormal findings. Exposure: Data collected included clinical history, physical examination previously to the laparoscopy. Main Outcome Measures: Augmented backward elimination was used as a procedure to obtain a baseline interpretable binomial logistic model. The performance of various machine learning models, including Random Forest, Light Gradient Boosting Machine, Extreme Gradient Boosting, Extremely Randomised Trees, Categorical Boosting, Adaptive Boosting, Support Vector, Multilayer Perceptron, Naive Bayes, Voting, and Stacking ensemble meta-classifiers, in predicting superficial peritoneal endometriosis. Feature importance was assessed using Shapley Additive Explanations (SHAP) values. Results: The presence of irregular menstrual cycle, irritable bowel syndrome, bladder pain syndrome, abdominal trigger point, and pelvic floor tenderness were independently associated with the diagnosis of superficial peritoneal endometriosis. SHAP values indicated that a history of pelvic inflammatory disease also suggested endometriosis. The soft voting classifier, which includes Extreme Gradient Boosting and Naive Bayes algorithms, demonstrated the highest recall (79.3%), while the Support Vector classifier achieved the best specificity (74.2%). Conclusion: Irregular menstrual cycles, irritable bowel syndrome, bladder pain syndrome, abdominal trigger points, and pelvic floor tenderness are independent factors linked with intraoperative findings of superficial peritoneal endometriosis. Additional variables, such as a history of pelvic inflammatory disease, may further enhance preoperative diagnostic accuracy. Machine learning approaches show promise in predicting the disease through pre-operative clinical data in this population. This predictive capability can support personalised patient counselling and surgical decision-making.