Jessica Wilkinson: My RBMO Article on Predicting Oocyte Yield With Machine Learning
Jessica Wilkinson, PHD Fellow at Care Fertility, shared a post on LinkedIn:
“I’m thrilled to share that my article, ‘Can machine learning models predict oocyte yield during assisted conception: a systematic review’, has just been published in Reproductive BioMedicine Online!
In this work, we explore how machine learning methods are being used to predict oocyte yield in assisted reproductive treatments which is an area with huge potential to improve clinical decision-making and personalise patient care. Our systematic review highlights current capabilities, gaps, and opportunities for future research in this rapidly evolving field.
What we did:
- Reviewed studies that applied ML models to predict the number of oocytes retrieved during controlled ovarian stimulation.
- Assessed model performance, methodological quality, and clinical relevance.
- Identified where further development is needed to support real-world clinical use.
I’m incredibly grateful to my co-authors and everyone who supported this project, this publication wouldn’t have been possible without you! Kanishka Gogna, Meurig Gallagher, Ally Richardson, Adam Devall, Victoria Sephton, Alison Campbell, Rima Dhillon-Smith, Tom Bamford and Arri Coomarasamy.
Read the full paper here.”
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
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