A foundational model for in vitro fertilization trained on 18 million time-lapse images

A foundational model for in vitro fertilization trained on 18 million time-lapse images

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...

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Main Authors: Suraj Rajendran, Eeshaan Rehani, William Phu, Qiansheng Zhan, Jonas E. Malmsten, Marcos Meseguer, Kathleen A. Miller, Zev Rosenwaks, Olivier Elemento, Nikica Zaninovic, Iman Hajirasouliha
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-025-61116-2
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https://doi.org/10.1038/s41467-025-61116-2

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