Related Experiment Video
Updated: Mar 27, 2026
12:18
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
8.2K
Using artificial intelligence to predict sexual health outcomes in endometriosis: a decision tree model algorithm.
Alexandre Vallée1, Anis Feki2, Jean-Marc Ayoubi3,4
1Department of Epidemiology and Public Health, Foch Hospital, Suresnes 92150, France.
Sexual Medicine
|March 25, 2026
Summary
A decision tree model identified key predictors of sexual dysfunction in women with endometriosis. This tool aids in early risk identification and personalized management for improved sexual health.
Area of Science:
- Reproductive Health
- Medical Informatics
- Clinical Decision Support
Background:
- Endometriosis significantly impacts female sexual function due to pain, hormonal changes, and psychological distress.
- Understanding predictors of sexual dysfunction is crucial for effective management.
Purpose of the Study:
- To develop and validate a decision tree model for identifying key predictors of sexual dysfunction in women with endometriosis.
- To stratify patients into high-risk and low-risk groups for sexual dysfunction.
Main Methods:
- A cross-sectional online survey was conducted with 1586 women diagnosed with endometriosis.
- A classification and regression tree model was trained and validated using sociodemographic, clinical data, and the Female Sexual Function Index (FSFI).
- Sexual dysfunction was defined as an FSFI score below 26.55.
Main Results:
- 86% of participants experienced sexual dysfunction.
- The decision tree model showed strong predictive performance (AUC=0.96 in training, 0.79 in validation).
- Key predictors identified include chronic pelvic pain, dyspareunia, heavy menstrual bleeding, infertility, BMI, digestive symptoms, education, and treatment history.
Conclusions:
- AI-driven decision tree models can effectively identify women at high risk for sexual dysfunction.
- These models support personalized risk stratification and clinical decision-making to improve sexual health outcomes in endometriosis patients.
