2-AIN-506, 2-AIN-252: Seminar in Bioinformatics (2), (4)
Summer 2025
Abstrakt

Suraj Rajendran, Eeshaan Rehani, William Phu, Qiansheng Zhan, Jonas E. Malmsten, Marcos Meseguer, Kathleen A. Miller, Zev Rosenwaks, Olivier Elemento, Nikica Zaninovic, Iman Hajirasouliha. A foundational model for in vitro fertilization trained on 18 million time-lapse images. Nat Commun, 16(1):6235. 2025.

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Download from publisher: https://doi.org/10.1038/s41467-025-61116-2 PubMed

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Abstract:

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.