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使用联合学习来开发一种人工智能模型,从ICSI前的卵细胞图像中预测可用的胚胎细胞形成

J M M Hall¹, T V Nguyen², A W Dinsmore³

¹Life Whisperer Diagnostics (a subsidiary of Presagen), San Francisco, CA, USA, and Adelaide, Australia; Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, Adelaide, Australia; Adelaide Business School, The University of Adelaide, Adelaide, Australia.

Reproductive biomedicine online

|October 21, 2024

概括

联合学习开发了一个人工智能 (AI) 模型用于卵细胞能力评估. 这种人工智能模型显示了通过预测可用的胚胎细胞形成来提高体外受精 (IVF) 成功率的潜力.

科学领域:

生殖医学和人工智能 (AI).
在体外受精 (IVF) 和卵细胞评估.

背景情况:

评估卵细胞能力对于试管婴儿成功至关重要.
目前用于卵细胞评估的方法有局限性.

研究的目的:

开发一种人工智能模型,用联合学习来评估卵细胞能力.
评估AI模型在剥离的卵细胞图像上的预测性能.

主要方法:

联合学习被用来训练一个AI模型在10,677个卵细胞图像从八个试管婴儿诊所.
人工智能模型评估了基于预测可用的胚胎细胞形成的卵细胞能力.

主要成果:

人工智能模型实现了曲线下的面积 (AUC) 高达0.65,具有高灵敏度 (83-88%),但特异性较低 (26-36%).
除了混变量,AUC提高了高达14%.
人工智能模型的得分与形态特征相关,并预测可用的胚胎细胞形成 (AUC 0.77).

结论:

使用联合学习的AI模型可以评估卵细胞能力,保护患者数据.
人工智能模型可以预测可用的胚胎细胞形成,这是一个关键的试管婴儿成功因素.
临床应用包括选择性受精和指导治疗决策.

关键词:

人工智能的人工智能是人工智能.计算机视觉计算机视觉计算机视觉胚胎发育的发展在这个过程中,IVF IVF.医学成像医学成像卵子细胞评估评估

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