Machine learning for prediction of euploidy in human embryos: in search of the best-performing model and predictive features.

Journal: Fertility and sterility

Published Date: Jan 19, 2022

OBJECTIVE: To assess the best-performing machine learning (ML) model and features to predict euploidy in human embryos.

Stefanie De Gheselle

Department for Reproductive Medicine, Ghent University Hospital, Ghent, Belgium. Electronic address: Stefanie.DeGheselle@UZGENT.be.
Céline Jacques

Apricity, 13 Rue Paul Valéry, 75116 Paris, France.
Jérôme Chambost

Apricity, 13 Rue Paul Valéry, 75116 Paris, France.
Celine Blank

Department of Obstetrics and Gynecology, Catharina Hospital, Eindhoven, the Netherlands; Department of Electrical Engineering (Signal Processing Systems), Eindhoven Technical University, Eindhoven, the Netherlands; Department of Reproductive Medicine, Ghent University Hospital, Ghent, Belgium. Electronic address: celineblank@icloud.com.
Klaas Declerck

Department for Reproductive Medicine, Ghent University Hospital, Ghent, Belgium.
Ilse De Croo

Department for Reproductive Medicine, Ghent University Hospital, Ghent, Belgium.
Cristina Hickman

AI Team, Apricity, 14 Grays Inn Rd, London WC1 X 8HN, UK.; Institute of Reproductive and Developmental Biology, Imperial College London, Hammersmith Campus, Du Cane Road, London, W12 0HS, UK.
Kelly Tilleman

Department of Reproductive Medicine, Ghent University Hospital, Ghent, Belgium.

Bayes Theorem Cohort Studies Humans Logistic Models Machine Learning Retrospective Studies

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