Research Question: Can machine learning tools predict the number of metaphase II (MII) oocytes and trigger day at the start of the ovarian stimulation cycle?
Design: A multicentre, retrospective study including 56,490 ovarian stimulation cycles (primary dataset) was carried out between 2020 and 2022 for analysis and feature selection. Of these, 13,090 were used to develop machine learning models for trigger day and the number of MII prediction, and another 5103 ovarian stimulation cycles (clinical validation dataset) from 2023 for clinical validation. Machine learning algorithms using deep learning were developed using optimal features from the primary dataset based on correlation.
Background: Recently, the potential detrimental effect that the duration of storage time may have on vitrified samples has raised some concerns, especially when some studies found an association between cryostorage length and decreased clinical results.
Objective: This study aimed to evaluate the effects of the storage time length of day-5 vitrified blastocysts in 2 study groups: freeze-all cycles and nonelective frozen embryo transfers.
Study Design: This was a retrospective study that included 58,001 vitrified/warmed day-5 blastocysts from 2 different populations, according to the reason for frozen embryo transfer.
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