ArticleJournal of assisted reproduction and genetics2025
Interpretable machine learning model for predicting MII oocyte retrieved following controlled ovarian stimulation: a retrospective cohort study of 24,976 IVF/ICSI cycles.
Article in Journal of assisted reproduction and genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Explainable Artificial Intelligence in Assisted Reproductive Technology: Bridging Prediction and Clinical Judgment.Biomedicines · 2026Review
- Ovarian tissue quality assessment and fertility preservation strategies enabled by multi-omics and artificial intelligence: current applications and clinical perspectives.Frontiers in physiology · 2026Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
purposeCan machine learning effectively predict the number of mature oocytes retrieved after controlled ovarian stimulation in patients undergoing in vitro fertilization or intracytoplasmic sperm injection?
designThis retrospective study included data from 24,976 infertility patients treated between 2018 and 2022 at a single reproductive center. After data preprocessing, feature selection was performed using correlation analysis, LASSO regression, and recursive elimination based on random forest. Eight machine learning models were developed to predict the number of mature oocytes retrieved. Model performance was evaluated using root mean square error (RMSE), mean absolute error (MAE), and R-squared (R
resultsSix predictors were identified: antral follicle count; follicle-stimulating hormone and estradiol at stimulation start; estradiol on the trigger day; number of large follicles on the trigger day; and age. The multilayer perceptron model achieved the highest accuracy, with RMSE of 3.675, MAE of 2.702, and R
conclusionsWe developed a machine learning model that accurately predicts mature oocyte yield. A web-based calculator was also created to enable individualized prediction and enhance clinical usability.
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