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深度学习方法用于预测人类胚胎的发展时间缩短视频

Akriti Sharma¹, Alexandru Dorobantiu², Saquib Ali³

¹Department of Computer Science, Oslo Metropolitan University, Oslo, Norway.

|September 2, 2025

概括

这项研究引入了一个预测胚胎发育的人工智能系统,有助于早期胚胎质量评估辅助生殖技术. 人工智能可以预测形态变化,改善胚胎的选择.

科学领域:

胚胎学
人工智能
生殖医学

背景情况:

在辅助生殖技术 (ART) 中,胚胎质量评估对于选择可行的胚胎和确定最佳移植时间至关重要.
目前的人工智能工具可以自动评估,但缺乏对未来胚胎发育的预测能力.
人工智能系统需要能够预测胚胎形态的变化.

研究的目的:

开发一个能够预测胚胎形态动态的AI系统.
帮助胚胎学家在早期评估和选择胚胎进行移植.
预测胚胎的未来形态变化超出目前的观察能力.

主要方法:

人工智能系统分析过去的胚胎发育 (2小时) 以预测未来的形态变化 (长达23小时).
使用卷积式LSTM层的新型预测模型使胚胎发育的递归预测成为可能.
该模型分析了之前的视频序列变化以预测形态.

主要成果:

人工智能系统准确地预测了胚胎在分裂 (2日) 和胚胎 (4日) 阶段的发育.
提供了关于细胞分裂过程和胚胎细胞形成的宝贵见解.
"转移"类胚胎的预测显示,与"避免"类胚胎相比,细胞膜更清晰,扭曲程度更小.

结论:

人工智能系统为胚胎质量提供了早期见解,有助于对转移和避免的评估.
胚胎学家可以利用预测来想象和理解胚胎的形态变化.
提高图像质量可以提高这种预测性AI方法的临床相关性.

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Embryonic Stem Cells

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Embryonic stem (ES) cells were first discovered in mice in 1981 by Martin Evans. In 1998, James Thomson identified a method to isolate embryonic stem cells from humans. Human embryonic stem cells (hESCs) are obtained from 3-5 day old embryos that remain unused after an in vitro fertilization procedure.
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