Wang Guangyu, Wang Kai, Gao Yuanxu, Chen Longbin, Gao Tianrun, Ma Yuanlin, Jiang Zeyu, Yang Guoxing, Feng Fajin, Zhang Shuoping, Gu Yifan, Liu Guangdong, Chen Lei, Ma Li-Shuang, Sang Ye, Xu Yanwen, Lin Ge, Liu Xiaohong
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China.
College of Future Technology, Peking University and Peking-Tsinghua Center for Life Sciences, Beijing 100871, China.
Patterns (N Y). 2024 May 2;5(7):100985. doi: 10.1016/j.patter.2024.100985. eCollection 2024 Jul 12.
fertilization (IVF) has revolutionized infertility treatment, benefiting millions of couples worldwide. However, current clinical practices for embryo selection rely heavily on visual inspection of morphology, which is highly variable and experience dependent. Here, we propose a comprehensive artificial intelligence (AI) system that can interpret embryo-developmental knowledge encoded in vast unlabeled multi-modal datasets and provide personalized embryo selection. This AI platform consists of a transformer-based network backbone named IVFormer and a self-supervised learning framework, VTCLR (visual-temporal contrastive learning of representations), for training multi-modal embryo representations pre-trained on large and unlabeled data. When evaluated on clinical scenarios covering the entire IVF cycle, our pre-trained AI model demonstrates accurate and reliable performance on euploidy ranking and live-birth occurrence prediction. For AI vs. physician for euploidy ranking, our model achieved superior performance across all score categories. The results demonstrate the potential of the AI system as a non-invasive, efficient, and cost-effective tool to improve embryo selection and IVF outcomes.
体外受精（IVF）彻底改变了不孕症的治疗方式，造福了全球数百万对夫妇。然而，目前用于胚胎选择的临床实践在很大程度上依赖于对形态的目视检查，而这种检查具有高度可变性且依赖经验。在此，我们提出了一种全面的人工智能（AI）系统，该系统可以解读编码在大量未标记多模态数据集中的胚胎发育知识，并提供个性化的胚胎选择。这个AI平台由一个名为IVFormer的基于Transformer的网络主干和一个自监督学习框架VTCLR（视觉-时间对比表征学习）组成，用于训练在大量未标记数据上预训练的多模态胚胎表征。当在涵盖整个IVF周期的临床场景中进行评估时，我们预训练的AI模型在整倍体排名和活产发生预测方面表现出准确可靠的性能。在整倍体排名方面，将AI与医生进行比较时，我们的模型在所有评分类别中均表现出卓越的性能。结果证明了该AI系统作为一种非侵入性、高效且经济高效的工具，在改善胚胎选择和IVF结果方面的潜力。
