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相关概念视频

Oogenesis02:07

Oogenesis

In human women, oogenesis produces one mature egg cell or ovum for every precursor cell that enters meiosis. This process differs in two unique ways from the equivalent procedure of spermatogenesis in males. First, meiotic divisions during oogenesis are asymmetric, meaning that a large oocyte (containing most of the cytoplasm) and minor polar body are produced as a result of meiosis I, and again following meiosis II. Since only oocytes will go on to form embryos if fertilized, this unequal...

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相关实验视频

Updated: May 7, 2026

Analysis of Chromosome Segregation, Histone Acetylation, and Spindle Morphology in Horse Oocytes
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机器学习模型可以在辅助受孕期间预测卵细胞产量吗? :一个系统的审查.

Jessica Wilkinson1, Kanishka Gogna1, Meurig Gallagher2

  • 1Department of Metabolism and Systems Science, University of Birmingham, Edgbaston, Birmingham, UK.

Reproductive biomedicine online
|March 4, 2026
PubMed
概括

使用机器学习预测卵细胞产量有助于个性化生育治疗. 然而,目前的模型在临床使用之前需要进一步验证和透明报告.

关键词:
人工智能的人工智能是人工智能.淋巴激素的剂量 淋巴激素的剂量机器学习是机器学习.模型模型模型模型模型卵细胞检索 卵细胞检索预测模型的预测模型.

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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:

  • 精确预测卵细胞产量对于优化辅助生殖中淋巴腺素剂量至关重要.
  • 对刺激的过度或不充分的反应会带来风险,并可能影响治疗的成功.
  • 机器学习 (ML) 模型是新兴的工具,可以帮助预测卵细胞产量.

研究的目的:

  • 评估ML模型的准确性和临床准备性,以预测卵细胞产量.
  • 评估现有的ML研究中的质量和偏差风险,以预测卵细胞产量.

主要方法:

  • 在OVID MEDLINE,EMBASE和Cochrane图书馆进行了系统的文献搜索.
  • 包括九项研究 (八项追溯,一项前性),涉及62,354个循环.
  • 研究质量和偏差风险使用TRIPOD和PROBAST标准进行评估.

主要成果:

  • 报告的准确性各不相同,平均绝对误差在0.62至4.13之间.
  • 神经网络模型通常表现出卓越的性能.
  • 没有研究报告了外部验证,限制了概括性;显著的异质性排除了元分析.

结论:

  • 目前用于卵细胞产量预测的ML模型显示出有希望的内部性能,但存在高偏差风险.
  • 数据处理中缺乏透明度和缺乏外部验证,阻碍了可复制性和临床应用.
  • 临床医生应谨慎行事;进一步的发展必须优先考虑外部验证和透明的报告,以确保可靠的临床整合.