Machine learning-based preliminary screening tool for clinical pregnancy prediction: towards management of IVF/ICSI stages.

ABSTRACT:

Background

Accurate prediction of pregnancy outcomes in assisted reproductive technology (ART) remains a clinical challenge due to the complexity and heterogeneity of IVF/ICSI cycles. Existing models often focus on isolated treatment stages and rely on linear statistical assumptions, limiting their ability to support personalized care throughout the entire treatment process.

Methods

This retrospective study included 1,062 women who underwent IVF/ICSI between 2016 and 2021, with an additional temporal validation cohort of 250 patients treated in 2022. Two machine learning (ML) models were developed to predict clinical pregnancy outcomes during the pre-treatment and treatment phases. Model performance was evaluated using metrics including precision-recall curves, F1 score, cal

SUBMITTER: Huang S 

PROVIDER: S-EPMC12624961 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature