Enhancing frozen-thawed embryo transfer outcomes and treatment personalization through machine learning models.

ABSTRACT:

Background

Infertility affects millions globally, with significant social, emotional, and economic consequences. While frozen-thawed embryo transfer (FET) is a cornerstone of assisted reproductive technology, its clinical pregnancy success rates remain inconsistent (29.6-59.2%). Improving predictive accuracy and personalizing treatment strategies for FET outcomes could address critical unmet needs in reproductive medicine.

Objective

To develop and validate machine learning models to accurately predict clinical pregnancy outcomes following FET and to simulate personalized treatment strategies based on individual patient profiles.

Methods

A retrospective analysis of 1013 FET cycles across two medical centers was conducted. Four machine learning (ML) models-XGBoost, rand

SUBMITTER: Li J 

PROVIDER: S-EPMC12602804 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature