Application of the Inception-ResNet-V2 algorithm to the analysis of embryo microscope images for the prediction model of assisted reproduction

The World Health Organization (WHO) estimates that approximately14 80 million men and women worldwide, with childbearing potential, need medical assistance due to fertility difficulties, which represents approximately 15% of the population. Similarly, about 15% of couples of maternal ages in Taiwan experience infertility problems. In clinical practice, in vitro fertilization (IVF) is the primary method of artificial reproduction. Using deep learning technology and an Inception- ResNetV2 model, we can create a reliable embryo classification and prediction system, which improves the selection of high-quality embryos and enhances pregnancy success rates. The classification and prediction model achieved 80% precision, AUC= 0.88, sensitivity 73% and 88% specificity. This exceeds the statistics of the Taiwanese National Health Service, where the average pregnancy rate for IVF in 2023 was 27.8 %. The results indicate that our model efficiently classifies embryos for successful implantation at a higher rate than the national statistics in Taiwan.