Itay Erlich, Sotirios H. Saravelos, Cristina Hickman, Assaf Ben-Meir, Iris Har-Vardi, James A. Grifo, Semra Kahraman, Assaf Zaritsky
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Decoupling Implantation Prediction and Embryo Ranking in Machine Learning: The Impact of Clinical Data and Discarded Embryos
Decoupling Implantation Prediction and Embryo Ranking in Machine Learning
Itay Erlich, Assaf Zaritsky, and co-workers establish that optimizing a machine learning model to predict in vitro fertilization embryo implantation success by inclusion of clinical properties is not an optimal strategy for the task of embryo ranking (see article number 2400048). The reason for this is “shortcut learning”, the model relies on the clinical factor as a proxy for implantation – hampering its ability to approximate the embryo quality. The authors’ practical recommendation is to exclusively focus on the embryo intrinsic features for ranking.
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