Machine learning tool for predicting mature oocyte yield and trigger day from start of stimulation: towards personalized treatment.

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.

A Combination of Artificial Intelligence with Genetic Algorithms on Static Time-Lapse Images Improves Consistency in Blastocyst Assessment, An Interpretable Tool to Automate Human Embryo Evaluation: A Retrospective Cohort Study.

Background: In recent times, various algorithms have been developed to assist in the selection of embryos for transfer based on artificial intelligence (AI). Nevertheless, the majority of AI models employed in this context were characterized by a lack of transparency. To address these concerns, we aim to design an interpretable tool to automate human embryo evaluation by combining artificial neural networks (ANNs) and genetic algorithms (GA).

Corticosteroids, androgens, progestogens and oestrogens in the endometrial microenvironment, and their association with endometrial progression and function.

Research Question: How does the intracrine action of progestagens, oestrogens, androgens and corticosteroids affect endometrial tissue progression and function?

Design: In this prospective observational study, 76 patients (<50 years old, no uterine pathologies and at least one failed IVF cycle) undergoing endometrial biopsy collection for endometrial evaluation between 2018 and 2021 were included. The concentrations of 11 steroid metabolites (cortisone, cortisol, progesterone, oestrone, 2-methoxyestrone, oestradiol, oestriol, testosterone, androstenedione, 17α-hydroxyprogesterone and 17-hydroxypregnenolone) were measured by ultra-performance liquid chromatography-tandem mass spectrometry in the endometrial tissue samples collected during the mid-secretory phase. Endometrial dating and reproductive outcomes (relative to the next good-quality fresh or frozen embryo transfer after the biopsy) were analysed in relation to endometrial steroid concentrations using Barnard's test; correlations between metabolite concentrations were measured by Pearson's correlation co-efficient.

External validation of a fully automated evaluation tool: a retrospective analysis of 68,471 scored embryos.

Objective: To externally validate a fully automated embryo classification system for in vitro fertilization (IVF) treatments.

Design: Retrospective cohort study.

Setting: Clinic.