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

Abstract

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.

Results: A tool with two novel progressive machine learning algorithms using deep learning was able to predict the trigger day and number of MII oocytes: mean absolute error 1.60 (95% CI 1.56 to 1.64) and 3.75 (95% CI 3.65 to 3.86), respectively. The R² value for the algorithm to predict the number of MII in the interquartile (Q3-Q1/P75-P25) range was 0.88; the entire dataset was 0.70 after removing the outliers at the planning phase of the stimulation cycle, which shows high accuracy. The interquartile root mean square error was 1.10 and 0.66 for the trigger day and the number of oocytes algorithm, respectively.

Conclusion: The tool using deep learning algorithms has high prediction power for trigger day and number of MII outcomes, and can be retrieved from patients at the start of the ovarian stimulation cycle; however, inclusion of more data and validation from different clinics are needed.