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Related Experiment Video

Updated: Aug 4, 2025

Establishment of an Experimental Mouse Model of Endometrioma to Study its Related Infertility
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Self-report symptom-based endometriosis prediction using machine learning.

Anat Goldstein1, Shani Cohen2

  • 1Department of Industrial Engineering and Management, Ariel University, 65 Ramat HaGolan St., Ariel, Israel. anatgo@ariel.ac.il.

Scientific Reports
|April 4, 2023
PubMed
Summary

This study developed a machine learning tool to predict endometriosis likelihood using symptom data. The aim is to shorten the lengthy diagnosis time for women experiencing early symptoms.

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Area of Science:

  • Gynecology
  • Medical Informatics
  • Machine Learning

Background:

  • Endometriosis affects 5-10% of reproductive-age women, yet diagnosis takes 6-10 years.
  • Current screening tools often rely on data from women near laparoscopy, missing early diagnostic opportunities.

Purpose of the Study:

  • To develop a self-diagnostic tool for early endometriosis detection based on symptoms.
  • To reduce the significant delay in endometriosis diagnosis.

Main Methods:

  • Machine learning models were trained using questionnaire data from diagnosed and undiagnosed women.
  • The study focused on symptoms experienced by women at the early stages of symptom onset.

Main Results:

  • The best-performing model achieved an AUC of 0.94, sensitivity of 0.93, and specificity of 0.95.
  • The study identified key symptoms and their effectiveness in predicting endometriosis.

Conclusions:

  • A symptom-based, machine learning-driven self-diagnostic tool can predict endometriosis likelihood.
  • This tool, intended for website integration, can shorten diagnosis time by prompting further examination for high-risk individuals.