Prediction of oocyte maturation rate in the GnRH antagonist flexible IVF protocol using a novel machine learning algorithm – A retrospective study

•This novel machine-learning algorithm demonstrates oocyte maturation rate prediction ability in IVF/ICSI protocols.•Application of machine learning allows for the integration of multiple parameters.•A machine learning algorithm for oocyte maturation prediction allows for development of a prognostic...

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Published in:European journal of obstetrics & gynecology and reproductive biology 2023-05, Vol.284, p.100-104
Main Authors: Houri, Ohad, Gil, Yotam, Danieli-Gruber, Shir, Shufaro, Yoel, Sapir, Onit, Hochberg, Alyssa, Ben-Haroush, Avi, Wertheimer, Avital
Format: Article
Language:English
Subjects:
Adult
Algorithms
Artificial Intelligence
Chorionic Gonadotropin - pharmacology
Estradiol
Female
Fertilization in Vitro - methods
GnRH antagonist
Gonadotropin-Releasing Hormone
Humans
IVF outcome
Machine learning
Oocyte maturation
Oocytes
Ovulation Induction - methods
Pregnancy
Pregnancy Rate
Prospective Studies
Retrospective Studies
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Summary:•This novel machine-learning algorithm demonstrates oocyte maturation rate prediction ability in IVF/ICSI protocols.•Application of machine learning allows for the integration of multiple parameters.•A machine learning algorithm for oocyte maturation prediction allows for development of a prognostic tool before final trigger for ovulation and retrieval. Oocyte maturation is affected by various patient and cycle parameters and has a key effect on treatment outcome. A prediction model for oocyte maturation rate formulated by using machine learning and neural network algorithms has not yet been described. A retrospective cohort study that included all women aged ≤ 38 years who underwent their first IVF treatment using a flexible GnRH antagonist protocol in a single tertiary hospital between 2010 and 2015. 462 patients met the inclusion criteria. Median maturation rate was approximately 80%. Baseline characteristics and treatment parameters of cycles with high oocyte maturation rate (≥80%, n = 236) were compared to cycles with low oocyte maturation rate (
ISSN:0301-2115
1872-7654
DOI:10.1016/j.ejogrb.2023.03.022