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相关概念视频

In Vitro Fertilization01:24

In Vitro Fertilization

239
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...
239

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Updated: Jun 23, 2025

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一个人工智能算法选择最可行的胚胎,考虑到试管婴儿实验室当前的流程.

Mahdi-Reza Borna1, Mohammad Mehdi Sepehri1, Behnam Maleki2,3

  • 1Department of IT Engineering, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran.

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概括

人工智能 (AI) 通过客观分析多个图像来改善体外受精 (IVF) 中的胚胎选择. 这种人工智能工具DeepEmbryo比人类专家更准确地预测怀孕结果,提高了试管婴儿成功率.

关键词:
人工智能的人工智能是人工智能.深度学习是一种深度学习.胚胎选择 胚胎选择在体外受精 in-vitro受精医学图像 医学图像 医学图像

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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:

  • 在体外受精 (IVF) 过程中对胚胎的形态评估是主观的,并限制了成功率.
  • 目前的胚胎选择方法依赖于有限的视觉参数,导致不理想的结果.
  • 人工智能 (AI) 提供了对胚胎选择的客观方法,有可能提高试管婴儿成功率.

研究的目的:

  • 开发和评估一个人工智能工具,用胚胎图像来预测怀孕结果.
  • 评估多个胚胎图像与单个图像对怀孕预测的有用性.
  • 将基于人工智能的胚胎选择的表现与经验丰富的胚胎学家的表现进行比较.

主要方法:

  • 从2017年至2020年收集了252个试管婴儿胚胎的时间延迟视频.
  • 在特定的种植后时间提取的钥匙框 (19,43,67).
  • 应用卷积神经网络 (CNN) 架构与转移学习,比较单图像和多图像AI模型与胚胎学家的评估.

主要成果:

  • 使用三张图像 (DeepEmbryo) 的AI模型与单图像模型相比,显示出更高的怀孕预测准确性.
  • 在预测怀孕结果方面,DeepEmbryo的表现优于五位经验丰富的胚胎学家.
  • 通过转移学习,CNN架构在预测怀孕机会方面达到高达75.0%的准确性.

结论:

  • 开发了DeepEmbryo,这是一款利用三个静态胚胎图像进行客观妊娠预测的AI工具.
  • DeepEmbryo利用现有试管婴儿实验室流程的容易获取的图像.
  • 基于人工智能的工具显示了提高怀孕预测和改善未来试管婴儿结果的巨大潜力.