Towards deep learning-powered IVF: A large public benchmark for morphokinetic parameter prediction
This provides a foundational resource for the IVF research community, though it is incremental as it builds on existing deep learning techniques applied to a new domain-specific dataset.
The authors tackled the lack of a public benchmark for AI in IVF by creating a dataset of 704 embryo development videos with 337k images, demonstrating that deep learning models like ResNet and LSTM outperform algorithmic methods in annotating development phases.
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).