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相关概念视频

In Vitro Fertilization01:24

In Vitro Fertilization

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In vitro fertilization (IVF) is a form of assisted reproductive technology where an egg is fertilized with sperm in a controlled laboratory environment before transferring the resulting embryo into the uterus. This process is designed to help individuals and couples experiencing difficulties conceiving.
The IVF process begins with ovarian stimulation, during which reproductive endocrinologists prescribe hormonal medications to stimulate the ovaries to produce multiple eggs instead of the single...
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结合输入深度学习管道用于胚胎选择以使用光显微镜图像和额外功能进行体外受精.

Krittapat Onthuam1,2, Norrawee Charnpinyo1, Kornrapee Suthicharoenpanich1

  • 1International School of Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand.

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概括

这项研究引入了在体外受精中对胚胎生存能力分类的深度学习管道,改进了主观形态评估. 开发的模型取得了显著的准确性,为胚胎选择提供了更客观的方法.

关键词:
美国有线电视新闻网 (CNN)这是一把手枪.深度学习是一种深度学习.胚胎图像 胚胎图像 胚胎图像胚胎形态学 胚胎形态学在体外受精体外受精.

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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:

  • 目前的体外受精 (IVF) 胚胎选择依赖于胚胎学家的主观形态评估.
  • 这种手动评估可能导致胚胎生存能力分类的变化和潜在的不准确性.

研究的目的:

  • 开发和评估基于深度学习的管道,用于客观的胚胎生存能力分类.
  • 将显微镜图像与临床和伪特征集成,以提高预测准确度.

主要方法:

  • 使用组合输入创建了一个深度学习管道:微观胚胎图像和患者数据 (年龄,伊斯坦布尔分级分数).
  • 使用基于卷积的转移学习模型 (EfficientNet-B0) 和具有生成对抗网络 (GAN) 的自我监督学习 (SimCLR).
  • 使用Optuna进行超参数优化,用于模型调整.

主要成果:

  • 最好的模型,一个优化的EfficientNet-B0,获得F1得分65.02%,准确率69.04%,灵敏度56.76%,AUC为66.98%.
  • 深度学习方法在准确性和可比AUC方面具有优势,与现有的组合方法相比.

结论:

  • 开发的深度学习管道为IVF中的传统主观胚胎评估提供了一个有希望的,客观的替代方案.
  • 这种人工智能驱动的方法有可能提高胚胎选择的效率和准确性,以获得更好的试管婴儿结果.