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).
翻译:对开发人工智能(AI)基础的体外受精(IVF)解决方案的一个重要限制是缺乏用于培训和评价深层学习(DL)模型的公开参考基准。在这项工作中,我们描述了一组完全附加说明的704个胚胎发育视频数据集,总共337k图像。我们用ResNet、LSTM和ResNet-3D结构来制作我们的数据集,并表明这些结构在自动注解阶段发育阶段的逻辑学方法方面过于完善。总体而言,我们提出第一个公共基准,使社区能够评价感光学模型。这是深入学习-动力IVF的第一步。值得注意的是,我们提出了16个不同发展阶段的高度详细的说明,包括早期细胞分裂阶段,还有迟发细胞分裂、模拟后阶段和非常早期的阶段,这些阶段以前从未使用过。我们假定,这一原始方法将有助于改进胚胎发育时间下降视频的深度学习方法的总体绩效,最终使育龄病人获得更好的临床成功率(Code and datas is https://gin_chin_Embran_Emban_Xtiv.Xtstes)。