Machine Learning-Based Detection of Endometriosis: A Retrospective Study in A Population of Iranian Female Patients.

Abstract

Background: Endometriosis, is a prevalent condition among women of childbearing age, characterized by the presence of ectopic endometrial glands. It is associated with pelvic pain and infertility. Unfortunately, the diagnosis of endometriosis is often delayed in many patients. While laparoscopic investigation is required for a definitive diagnosis, physical examination combined with ultrasonography can provide reasonably accurate detection. Machine learning (ML) techniques have shown promise tools in medical imaging and diagnostics. However, there is a lack of sufficient ML studies focusing on Iranian endometriosis female patients. In this study, we aimed to compare the diagnostic accuracy of different ML algorithms for endometriosis detection.

Materials and methods: In this retrospective study, our objective was to assess the diagnostic accuracy of different ML algorithms in classifying suspicious cases of endometriosis using ultrasonographic signs. Our data set consisted of 505 patients, among which 149 were confirmed cases of endometriosis. We divided the data set into training and test sets to train and evaluate the performance of the ML models. To ensure robust evaluation, we employed stratified 5-fold cross-validation and calculated the area under the receiver operating characteristic curve (AUC) as a measure of model performance.

Results: In the test set, a total of 37 out of 127 patients (29.1%) were diagnosed with endometriosis, while in the training set, 112 out of 378 patients (29.6%) were confirmed to have the condition. Sensitivities ranged from 59.5 to 75.7%, and specificities ranged from 71.7 to 83.3%. Notably, the SVM, Random Forest, Extra-Trees, and Gradient Boosting models exhibited the highest performance, with AUCs of 0.76.

Conclusion: Our study supports the use of ML models for the screening and diagnosis of endometriosis. The superior performance of the SVM, Random Forest, Extra-Trees, and Gradient Boosting models, as indicated by their high AUCs, suggests their potential as valuable tools in improving the accuracy of endometriosis detection.