通过计算机辅助形态学评估对胚胎性状态的分类
Gunawan Bondan Danardono1, Nining Handayani1, Claudio Michael Louis1
1IRSI Research and Training Centre, Jakarta, Indonesia (Mr Danardono, Ms Handayani, Mr Louis, Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini).
AJOG global reports
|August 30, 2023
概括
这项研究开发了一种人工智能模型,以非侵入性地预测胚胎形状,减少对形形的侵入性植入前遗传测试的需求. 该模型实现了74%的准确性,为试管婴儿决策提供了一个有希望的替代方案.
科学领域:
- 生殖医学 生殖医学
- 医疗保健中的人工智能
- 胚胎学 胚胎学
背景情况:
- 预植入基因测试 (PGT-A) 评估胚胎的染色体状况,但涉及侵入性活检.
- 目前的PGT-A方法有缺点,包括侵入性和有限的长期效果数据.
研究的目的:
- 开发一种使用机器学习的计算机辅助预测模型,以尽量减少侵入性PGT-A.
- 根据形态特征来预测胚胎性状态,以改善试管婴儿决策.
主要方法:
- 利用图像处理技术,包括图像增强和特征提取.
- 开发并比较多个机器学习和深度学习算法用于预测建模.
- 用于图像分析的面向梯度和主要组件分析的运用直方图.
主要成果:
- 一个人工智能模型成功地被开发出来,可以预测胚胎的 ploidy 状态.
- 梯度增强算法以0.74准确度展示了卓越的性能.
- 实现了0.83个形精度和0.84个形预测值 (回忆).
结论:
- 与人类观察相比,机器辅助技术为胚胎评估提供了一个新的视角.
- 开发的模型作为未来更先进的计算机辅助预测系统在IVF的基础.
- 建议进行进一步的研究,以改进人工智能模型,并探索它们对体外受精的长期影响.
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