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Machine learning for predicting elective fertility preservation outcomes.

Itai Braude1, Einat Haikin Herzberger1,2, Mor Semo1

  • 1Department of Obstetrics and Gynecology, Meir Medical Center, Kfar Saba, Israel.

Scientific Reports
|May 2, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning accurately predicts fertility preservation outcomes. Models using pre- and post-treatment data forecast oocyte yield, aiding treatment planning for women seeking fertility preservation.

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Area of Science:

  • Reproductive Medicine
  • Artificial Intelligence in Healthcare
  • Data Science in Clinical Applications

Background:

  • Elective fertility preservation is crucial for women facing medical treatments impacting fertility.
  • Predicting treatment outcomes, specifically oocyte yield, is essential for optimizing fertility preservation protocols.
  • Current prediction methods may lack the precision needed for personalized treatment strategies.

Purpose of the Study:

  • To evaluate the efficacy of machine learning models in predicting oocyte yield in women undergoing fertility preservation.
  • To identify key pre-treatment and post-treatment parameters that influence fertility preservation outcomes.
  • To compare the predictive accuracy of different machine learning algorithms against statistical regression.

Main Methods:

  • Retrospective analysis of 250 women undergoing elective fertility preservation (2019-2022).
  • Application of machine learning models (Random Forest Classifier, XGBoost Classifier) and statistical regression.
  • Prediction of oocyte count (OC) classes (Low, Medium, High) using pre-treatment and post-treatment data.

Main Results:

  • Random Forest Classifier achieved the highest accuracy, with post-treatment AUC of 87% and pre-treatment AUC of 77%.
  • XGBoost Classifier showed comparable performance with post-treatment AUC of 86% and pre-treatment AUC of 74%.
  • Key predictors included basal FSH, basal LH, antral follicle count (AFC), and estradiol levels on trigger day.

Conclusions:

  • Machine learning models demonstrate high accuracy in predicting fertility preservation treatment outcomes.
  • These models can effectively utilize clinical data to forecast oocyte retrieval numbers.
  • The findings support the integration of AI-driven predictive tools in fertility preservation counseling and management.