Deep Learning-Based Model of Embryo Formation from Multi-Plane Time-Lapse Oocyte Images

IVF has become a standard fertility treatment. Deep learning has the potential to support automated embryo assessment from imaging data; however, most methods rely on single-plane or single time-point images. This study aims to develop a deep learning-based tool to support the workflow by predicting whether an individual mature oocyte will give rise to an embryo, using early multi-plane time-lapse imaging acquired in an intracytoplasmic sperm injection (ICSI) setting. Time-lapse stack images (11 focal planes) were acquired every 10 min and assembled into fixed-length sequences of ten time points per oocyte. A focal-plane fusion module compressed each stack into a 3-channel frame, which was embedded by a masked-autoencoder (MAE) pre-trained Vision Transformer. Frame embeddings were modeled with a bidirectional long short-term memory (LSTM) network and classified via a sigmoid output. The developed deep learning model was evaluated on a held-out test set of 67 oocytes. It correctly identified 11 of 12 embryo-forming oocytes (sensitivity: 0.917), critical to ensure potential embryos are not overlooked during IVF procedures. For non-embryo oocytes, 34 of 55 were correctly classified (specificity: 0.618). Although specificity is lower, the model still effectively reduces the number of oocytes to track without discarding potentially useful material.