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

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 Determi...

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Bibliographic Details
Main Authors: Almog Luz, Ariel Hourvitz, Eden Moran, Nevo Itzhak, Shachar Reuvenny, Rohi Hourvitz, Michal Youngster, Micha Baum, Ettie Maman
Format: Article
Language:English
Published: Nature Portfolio 2024-11-01
Series:Scientific Reports
Subjects:
Ovulation
Machine learning
Frozen embryo transfer
Natural cycle
Prediction
Online Access:https://doi.org/10.1038/s41598-024-80356-8
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https://doi.org/10.1038/s41598-024-80356-8

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