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基于时间间隔图像和机器学习的胚胎评估算法的诊所之间的性能比较.

Martin N Johansen1, Erik T Parner2, Mikkel F Kragh3,4

  • 1Vitrolife A/S, Jens Juuls Vej 18-20, 8260, Viby J, Denmark. mnjohansen@vitrolife.com.

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概括

这项研究引入了年龄标准化来比较人工智能胚胎活力预测在IVF诊所. 该方法减少了由不同母亲年龄分布引起的性能变化,使得诊所的比较更加公平.

关键词:
人工智能的人工智能是人工智能.选择胚胎的选择模型性能 模型性能时间缩影是时间缩影.

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科学领域:

  • 生殖医学是一种生殖医学.
  • 医疗保健中的人工智能
  • 生物统计学 生物统计学

背景情况:

  • 人工智能 (AI) 模型用于胚胎生存率预测,在改善试管婴儿结果方面表现有前途.
  • 不同的试管婴儿诊所对母亲年龄分布的变化可能会对AI模型性能评估产生重大影响.
  • 由于人口统计学差异,在诊所之间直接比较人工智能模型的性能可能会产生误导性.

研究的目的:

  • 评估不同母亲年龄分布对人工智能模型性能对胚胎生存率预测的影响.
  • 提出和验证一种方法,在IVF诊所中对AI模型性能指标进行年龄标准化.

主要方法:

  • 追溯分析4805个新鲜和冷单个胚胎细胞移植 (5-6天的潜伏期).
  • 基于胎儿心跳结果的ROC曲线下面面积 (AUC) 的AI模型歧视性性能评估.
  • 开发AUC的年龄标准化方法,根据临床特定的母亲年龄对胚胎进行加权,相对于共同的参考人群.

主要成果:

  • 在标准化之前,观察到临床特定AUC (0.58-0.69) 的显著变化.
  • 年龄标准化将AUCs的临床间差异降低了16%.
  • 三家诊所在标准化后显示了类似的AUC,这表明该方法减轻了人口影响.

结论:

  • 拟议的年龄标准化方法有效地减轻了IVF诊所之间的AI模型性能指标的变化.
  • 这种方法可以在不同患者群体中更公平,更准确地比较胚胎活力预测模型.
  • 标准化对于在生殖医学中对人工智能工具进行可靠的基准测试至关重要.