  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. This
study includes a total of 1084 images from 1226 embryos. The images were
captured by 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. Our deep
learning grading results were compared to the grading results from trained
embryologists to evaluate the model performance. Our best model from
fine-tuning a pre-trained ResNet50 on the dataset results in 91.79% accuracy.
The model presented could be developed into an automated embryo assessment
method in point-of-care settings.
