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Machine Learning Prediction of Oocyte Yield Still in Early Development – RBMO
RBMO/LinkedIn
Mar 9, 2026, 17:26

Machine Learning Prediction of Oocyte Yield Still in Early Development – RBMO

Reproductive BioMedicine Online (RBMO) shared a post on LinkedIn:

“This new review provides an overview and evaluation of (ML) models published in the literature (including a glossary of terminology), and examines their accuracy at predicting oocyte yield at transvaginal oocyte retrieval (TVOR), while highlighting the current limitations in existing machine learning models.

Although several models demonstrated promising internal performance, they remain at high risk of bias, and the authors note that clinicians should be cautious in applying these models to practice until independently validated.

Overall, the authors say that machine learning-based prediction of oocyte yield remains in the early phase, and future development should prioritize external validation and transparent reporting.

Published open access, the paper can be read in full for free ahead of press from our April issue, now.”

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

Authors: Jessica Wilkinson, Kanishka Gogna, Meurig Gallagher, Alison Richardson, Adam Devall, Victoria Sephton, Alison Campbell, Rima Dhillon-Smith, Tom Bamford, Arri Coomarasamy

Machine Learning Prediction of Oocyte Yield Still in Early Development – RBMO

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Adam Devall Alison Campbell Alison Richardson Arri Coomarasamy Fertility Fertility News Jessica Wilkinson Kanishka Gogna Machine Learning Meurig Gallagher RBMO Reproductive BioMedicine Online reproductive health Reproductive Medicine Rima Dhillon-Smith Tom Bamford transvaginal oocyte retrieval Victoria Sephton
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