During in vitro fertilization (IVF), a number of different embryos are produced from eggs and sperm. Then, embryologists choose which one of the embryos is most likely to lead to a successful pregnancy and transfer it to the patient. Embryologists make this choice by using their expertise to apply a set of widely accepted principles based on the appearance of the embryo.
In recent years there has been a lot of interest in using in this process. We developed one such AI system and tested it in a study of more than 1,000 IVF patients. Our system chose the same embryo as a human expert in about two-thirds of cases, and had an overall success rate only marginally lower.
The results are in . Can deep learning help IVF? Over the past few years, with colleagues in Sweden, we have been developing software to identify which embryos will have the best chance of IVF success. Our system uses , an AI method for finding patterns in large amounts of data.
While we were developing our system, we carried out retrospective studies comparing the system's choices with past real-world decisions made by embryologists. These early results suggested the deep learning system might do an even better job than a human expert. So the next step was to test the system properly with a randomized trial.
Our trial involved 1,066 patients at 14 fertility clinics in Australia and Europe (Denmark, Sweden and the United Kingdom). For each patient, both the deep learning system and a human expert selected an embry.