Human Blastocyst Classification after In Vitro Fertilization Using Deep Learning

Embryo quality assessment after in vitro fertilization (IVF) is primarily done visually by embryologists. Variability among assessors, however, remains one of the main causes of the low success rate of IVF. This study aims to develop an automated embryo assessment based on a deep learning model using 1084 images from 1226 embryos. We captured the images using an inverted microscope at day 3 after fertilization. The images were labelled based on Veeck criteria that differentiate embryos to grade 1 to 5 based on the size of the blastomere and the grade of fragmentation. We compare the grading results from trained embryologists with our deep learning model to evaluate the performance. Our best model from a fine-tuned ResNet50 results in 91.79% accuracy. The model presented could be developed into an automated embryo assessment method in point-of-care settings.