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相关概念视频

In Vitro Fertilization01:24

In Vitro Fertilization

229
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...
229

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相关实验视频

Updated: Jun 11, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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以患者为中心的体外受精预后咨询使用机器学习为实用主义者.

Mylene W M Yao1, Julian Jenkins2, Elizabeth T Nguyen1

  • 1R&D Department, Univfy, Los Altos, California.

Seminars in reproductive medicine
|October 8, 2024
PubMed
概括
此摘要是机器生成的。

机器学习 (ML) 可以改善体外受精 (IVF) 预后,以改善患者的决策. 个性化的机器学习模型,结合人类的专业知识,可以扩大生育护理的准入,提高健康结果.

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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
  • 生物统计学 生物统计学

背景情况:

  • 试管受精 (IVF) 是有效的,但未得到充分利用.
  • 准确,可理解的试管婴儿预后对于患者的考虑至关重要.
  • 现有的预后方法可能无法满足对个性化预测的需求.

研究的目的:

  • 审查用于试管婴儿预后的机器学习 (ML) 模型.
  • 讨论 ML 模型的开发,验证和部署,以便在护理中心使用.
  • 探索ML与人类专业知识的整合,以改善生育护理的准入.

主要方法:

  • 基于ML的IVF预后模型的文献综述.
  • 对规模化ML实施的数据和模型管道的分析.
  • 考虑临床ML实施因素,以获得点护理成功.

主要成果:

  • 通过使用治疗前数据,ML提供个性化的预后.
  • 需要专门的专业知识来开发和部署ML模型.
  • 护理点的实施需要仔细考虑临床因素.

结论:

  • 基于ML的试管婴儿预后可以提高患者的理解和治疗决策.
  • 将人类专业知识与ML结合起来,可以扩大生育护理的可用性.
  • 利用ML可以在生殖医学中实现显著的健康,社会和经济效益.