A Deep Learning Approach For Prediction Of Ivf Implantation Outcome From Day 3 And Day 5 Time-Lapse Human Embryo Image Sequences

Various protocols have been developed to improve the success rate of In Vitro Fertilization (IVF). Earlier protocols were based on embryonic cell quality on embryos' third day. Newer protocols rely on the blastocyst quality (day-5 embryo). Artificial intelligence (AI) systems for automatic human embryo quality assessment seem to be the natural trend towards improving IVF's outcome. AI systems can potentially reveal hidden relationships between embryos' various attributes. To this date, most AI systems assess single blastocyst images. This paper proposes a novel approach that predicts the embryo implantation outcome from their time-lapse images. This approach consists of two models. One model evaluates each embryo based on its day-3 attributes, while the second model assesses the same embryo's day-5 image sequence. A Data Length Schedular (DLS) algorithm is developed addressing variations in blastocyst stage sequences' lengths. With an accuracy of 76.9%, the proposed system beats state of the art by 6%.