Can machine learning models predict oocyte yield during assisted conception?: a systematic review

2025/10/25 by Jessica Wilkinson, Julie J. Wilkinson, Kanishka Gogna +10 · 1 voice
Medicine · #Assisted Reproductive Technology and Twin Pregnancy #Reproductive Biology and Fertility

paper · pdf · doi:10.1016/j.rbmo.2025.105362

openalex created_date 2025/10/25 · openalex publication_date 2025/10/25 · crossref created 2025/10/25 · crossref issued 2026/04/01 · crossref published 2026/04/01 · crossref published-print 2026/04/01 · crossref deposited 2026/05/15 · crossref indexed 2026/05/15 · openalex updated_date 2026/07/23

Citations

• Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration
• PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies
• A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models
• Data sharing: using blockchain and decentralized data technologies to unlock the potential of artificial intelligence: What can assisted reproduction learn from other areas of medicine?
• PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews

Discussions

• This new review evaluates machine learning (ML) models (including a glossary of terminology), and examines their accuracy at predicting oocyte yield at transvaginal oocyte retrieval (TVOR), while high [bsky, 2 points, 0 comments]