Improved clinical pregnancy rates in natural frozen-thawed embryo transfer cycles with machine learning ovulation prediction: insights from a retrospective cohort study.

ABSTRACT: This study aims to develop physician support software for determining ovulation time and assess its impact on pregnancy outcomes in natural cycle frozen embryo transfers (NC-FET). To develop, assess, and validate an ovulation prediction model, three datasets were used: REI Ovulation Determination dataset (500 cycles) split into training (309), validation (90), and test (101) sets; the Documented Ovulation dataset (101 cycles) with confirmed ovulation (documented follicular rupture and LH surge); and the Clinical Pregnancy Rates dataset (515 NC-FET cycles), categorized into "Matched" and "Mismatched" based on alignment with the model's ovulation determination. Pregnancy outcomes were compared between the groups. The ovulation prediction model exhibited 93.85% and 92.89% matching rates with

SUBMITTER: Luz A 

PROVIDER: S-EPMC11603157 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature