Rajendran Suraj, Rehani Eeshaan, Phu William, Zhan Qiansheng, Malmsten Jonas E, Meseguer Marcos, Miller Kathleen A, Rosenwaks Zev, Elemento Olivier, Zaninovic Nikica, Hajirasouliha Iman
Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine of Cornell University, New York, NY, USA.
Caryl and Israel Englander Institute for Precision Medicine, The Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA.
Nat Commun. 2025 Jul 11;16(1):6235. doi: 10.1038/s41467-025-61116-2.
Embryo assessment in in vitro fertilization (IVF) involves multiple tasks-including ploidy prediction, quality scoring, component segmentation, embryo identification, and timing of developmental milestones. Existing methods address these tasks individually, leading to inefficiencies due to high costs and lack of standardization. Here, we introduce FEMI (Foundational IVF Model for Imaging), a foundation model trained on approximately 18 million time-lapse embryo images. We evaluate FEMI on ploidy prediction, blastocyst quality scoring, embryo component segmentation, embryo witnessing, blastulation time prediction, and stage prediction. FEMI attains area under the receiver operating characteristic (AUROC) > 0.75 for ploidy prediction using only image data-significantly outpacing benchmark models. It has higher accuracy than both traditional and deep-learning approaches for overall blastocyst quality and its subcomponents. Moreover, FEMI has strong performance in embryo witnessing, blastulation-time, and stage prediction. Our results demonstrate that FEMI can leverage large-scale, unlabelled data to improve predictive accuracy in several embryology-related tasks in IVF.
体外受精（IVF）中的胚胎评估涉及多项任务，包括倍性预测、质量评分、成分分割、胚胎识别以及发育里程碑的时间确定。现有方法分别处理这些任务，由于成本高昂且缺乏标准化，导致效率低下。在此，我们引入了FEMI（成像基础体外受精模型），这是一个基于约1800万张延时胚胎图像训练的基础模型。我们在倍性预测、囊胚质量评分、胚胎成分分割、胚胎监测、囊胚形成时间预测和阶段预测方面对FEMI进行了评估。FEMI仅使用图像数据进行倍性预测时，其受试者操作特征曲线下面积（AUROC）> 0.75，显著超过基准模型。在整体囊胚质量及其子成分方面，它比传统方法和深度学习方法都具有更高的准确性。此外，FEMI在胚胎监测、囊胚形成时间和阶段预测方面表现出色。我们的结果表明，FEMI可以利用大规模未标记数据提高体外受精中几个胚胎学相关任务的预测准确性。
