New publication: Automatic grading of human blastocysts from time-lapse imaging

The automatic algorithms for perform at least as good as the average embryologist for blastocyst grading and indirectly for predicting fetal heart beat as described above. Developing the algorithm based on time-lapse sequences lead to an improved accuracy compared to using only still images. Training of deep learning algorithms is only based on raw image sequences and requires no prior knowledge of embryology. Thus, the algorithm learns by itself to extract the temporal and the morphological features that are most important for prediction of blastocyst grading. It is important to note that in order to design and train a deep neural network, a substantial amount of data (in this case image sequences) is required.