The use of Machine Learning to assist in the selection of the "best" embryos for transfer in Human In Vitro Fertilisation

Abstract

IVF treatment typically involves the collection of 6-8 eggs from the ovaries of the woman, which are then fertilised in vitro with partner or donor sperm, resulting in 6-7 embryos. The law however, only permits 3 to be transferred to the woman's uterus. Currently, in Oxford, as in other IVF Units, these 3 embryos are selected on the basis of subjective criteria involving approximately 6 features of the embryo's morphology, and its follicle and oocyte of origin. However, more than 60 features, describing each embryo, oocyte, follicle and sperm sample, are available and recorded in our extensive laboratory database. Clearly, it is impossible for the embryologist to simultaneously assess all of these features, but state-of-the-art computer-aided induction techniques can. We have therefore developed a novel and exciting collaboration between the Nuffield Department of Obstetrics and Gynaecology (Dr I.L. Sargent) and the Oxford University Computing Laboratory  to use Machine Learning to develop rules for embryo selection.

In this approach, the learning program searches for discriminatory patterns by studying all of the features of specific batches of embryos from our database which either did, or did not, result in a live child. These patterns are then expressed in the form of "if-then" rules. Having established these rules retrospectively they can then be used prospectively in the IVF Clinic to identify the embryos which have the greatest potential to develop into a live child.

This proposal is motivated by promising initial results obtained by Dr R. Saith of this laboratory. Working in collaboration with a member of Dr Muggleton's group, in a study of 81 embryos Dr Saith has developed a limited set of "embryo selection" rules which could discriminate between 2 groups of "good" and "bad" embryos. The successful completion of this pilot study will permit the development of more sophisticated rules which take into account embryo-related as well as maternal and paternal factors. These could have a major impact on IVF success rates world wide.

Principal investigator

Research officer