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一个用于体外受精的基本模型在1800万张时间差图像上训练

Suraj Rajendran^1,2,3, Eeshaan Rehani^1,2,4, William Phu^1,2

¹Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine of Cornell University, New York, NY, USA.

Nature communications

|July 11, 2025

概括

一个新的AI模型,FEMI,分析了体外受精 (IVF) 的胚胎图像,以改善育,质量和发育时间的预测. 这种基本模型提高了多个胚胎学任务的准确性.

科学领域:

胚胎学胚胎学
人工智能的人工智能
在体外受精中进行体外受精.

背景情况:

在试管婴儿中对胚胎的评估需要多个单独的任务,如性预测和质量评分.
由于高成本和缺乏标准化,目前的方法效率低下.

研究的目的:

介绍FEMI (成像基础试管婴儿模型),这是一种用于试管婴儿胚胎成像的新型基础模型.
评估FEMI在各种胚胎学任务中的表现.

主要方法:

训练有素的FEMI对大约1800万个时间延迟胚胎图像进行了训练.
评估了FEMI的任务,包括皮质预测,芽细胞质量评分,组件细分,胚胎见证和发育阶段/时间预测.

主要成果:

FEMI获得了AUROC> 0.75的性预测,表现优于基准.
与传统和深度学习方法相比,在胚胎细胞质量评分方面表现出更高的准确性.
在胚胎见证和发育预测方面表现强.

结论:

FEMI有效地利用大规模的未标记数据来提高试管婴儿的预测准确性.
这个基础模型为多个胚胎学任务提供了一种标准化和高效的方法.

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