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Updated: May 23, 2025

Establishment of an Experimental Mouse Model of Endometrioma to Study its Related Infertility

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Utilizing Artificial Intelligence: Machine Learning Algorithms to Develop a Preoperative Endometriosis Prediction

Danielle L Snyder¹, Silvana Sidhom², Corinne E Chatham¹

¹College of Medicine, University of Florida (Drs. Snyder, Chatham, and Tillotson), Gainesville, Florida.

Journal of Minimally Invasive Gynecology

|May 21, 2025

Summary

This summary is machine-generated.

Machine learning models can predict endometriosis using clinical data, identifying key symptoms like pain and tenderness for earlier diagnosis. This aids healthcare providers in recognizing and referring patients sooner.

Keywords:

Artificial intelligence Laparoscopy Machine learning Prediction model Symptoms

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Noninvasive Monitoring of Lesion Size in a Heterologous Mouse Model of Endometriosis

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Area of Science:

Gynecologic Surgery
Medical Informatics
Machine Learning in Medicine

Background:

Endometriosis diagnosis often involves invasive procedures.
Accurate preoperative prediction of endometriosis remains a challenge.
Clinical features hold potential for non-invasive diagnostic aids.

Purpose of the Study:

To evaluate machine learning algorithms (MLAs) for predicting endometriosis using clinical data.
To develop an accurate and explainable preoperative prediction model for endometriosis.
To identify key clinical predictors of endometriosis.

Main Methods:

Retrospective case-control study (2011-2022) at a tertiary referral center.
Analysis of 209 clinical features in 788 women aged 18-55 undergoing surgery.
XGBoost MLA used to predict endometriosis, with SHAP values for feature importance.

Main Results:

The XGBoost model achieved 83% accuracy, 96% sensitivity, and 0.81 ROC-AUC.
Key predictors identified include emesis, crampy pain, regular periods, dysmenorrhea severity, and retrocervical tenderness.
Pathology confirmed endometriosis in 83% of participants.

Conclusions:

MLAs show promise in predicting endometriosis preoperatively using clinical features.
Clinical predictors like retrocervical tenderness and pain characteristics can aid early recognition.
Further validation in diverse populations is needed for a widely applicable tool.