  An important limitation to the development of Artificial Intelligence
(AI)-based solutions for In Vitro Fertilization (IVF) is the absence of a
public reference benchmark to train and evaluate deep learning (DL) models. In
this work, we describe a fully annotated dataset of 704 videos of developing
embryos, for a total of 337k images. We applied ResNet, LSTM, and ResNet-3D
architectures to our dataset and demonstrate that they overperform algorithmic
approaches to automatically annotate stage development phases. Altogether, we
propose the first public benchmark that will allow the community to evaluate
morphokinetic models. This is the first step towards deep learning-powered IVF.
Of note, we propose highly detailed annotations with 16 different development
phases, including early cell division phases, but also late cell divisions,
phases after morulation, and very early phases, which have never been used
before. We postulate that this original approach will help improve the overall
performance of deep learning approaches on time-lapse videos of embryo
development, ultimately benefiting infertile patients with improved clinical
success rates (Code and data are available at
https://gitlab.univ-nantes.fr/E144069X/bench_mk_pred.git).
