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商业AI算法的外部测试用于检测乳腺癌在查乳房扫描时的检测.

John Brandon Graham-Knight1, Pengkun Liang1, Wenna Lin1

  • 1Department of Medical Physics, BC Cancer-Kelowna, 399 Royal Ave, Kelowna, BC, Canada V1Y 5L3.

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概括

一个商业人工智能 (AI) 系统在加拿大大规模查队列中展示了可普遍化的乳腺癌检测性能. 然而,人工智能的表现在各个子组中各不相同,尤其是化.

关键词:
人工智能的人工智能偏见和公平的公平乳腺癌 乳腺癌 乳腺癌乳房学 乳房学 乳房学模型测试 模型测试质量保证 / 质量保证查检查 查检查 查检查查性乳房镜检查 乳房镜检查 乳房镜检查技术评估 技术评估

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科学领域:

  • 放射学 放射学是一门学科.
  • 医疗成像医学成像
  • 医疗保健中的人工智能

背景情况:

  • 乳腺癌查计划的目标是早期检测.
  • 数字造乳镜是一种标准的查工具.
  • 人工智能 (AI) 越来越多地被用于医学图像分析.

研究的目的:

  • 评估商业人工智能系统对乳腺癌检测的性能.
  • 在一个大型的外部查队伍中评估AI的概括性.
  • 分析跨人口,临床和成像特征的AI性能变化.

主要方法:

  • 来自加拿大不列颠哥伦比亚省的136,700张数字乳房造影的回顾性分析.
  • 使用ROC曲线下的面积 (AUC) 评估AI算法性能.
  • 与放射学家对AI灵敏度和特异性的比较.

主要成果:

  • 人工智能算法在乳腺癌检测方面实现了0.93的整体AUC.
  • 根据乳腺密度,AI的表现有显著差异 (D的AUC为0.84,A的AUC为0.96).
  • 人工智能在建筑扭曲 (0.96) 方面表现更高,但在化 (0.87) 方面表现较低.
  • 放射科医生的敏感性 (92.6%) 最初超过AI (89.4%),在2年的随访中没有差异.

结论:

  • 商业人工智能系统可用于加拿大乳腺癌查.
  • 人工智能性能受到特定的成像特征的影响,例如化和建筑扭曲.
  • 需要进一步的研究来优化AI在不同患者子组的性能.