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使用生成性人工智能对成像关节置换注册表进行种族差异分析:推进骨科数据公平性

Bardia Khosravi1,2, Pouria Rouzrokh1,2, Bradley J Erickson2

  • 1Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA.

Arthroplasty today
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概括
此摘要是机器生成的。

生成型深度学习在盆腔放射图中发现了种族差异,揭示了六个关键的解剖变异. 这有助于通过突出医学成像数据中的潜在偏差来开发公平的医疗保健AI.

关键词:
这是一个偏见的偏见.数据集策划数据集策划股权资本 股权资本可以解释的可解释性.生成性AI是一种人工智能.

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科学领域:

  • 医学成像分析 医学成像分析
  • 医疗保健中的人工智能
  • 放射学上的差异

背景情况:

  • 医疗数据集可能包含偏见,影响深度学习模型并导致不公平的临床应用.
  • 了解医学数据中的种族偏见对于创建公平的医疗保健技术至关重要.
  • 本研究使用生成式深度学习研究了关节整形术患者的种族间的放射性差异.

研究的目的:

  • 探索和理解盆腔放射图中的种族差异.
  • 识别和可视化种族群体之间的放射特征的系统差异.
  • 利用生成性人工智能用于医学成像中的偏差检测.

主要方法:

  • 从全关节整形术患者的盆腔放射图的回顾性分析.
  • 利用无声扩散概率模型来生成基于人口统计和成像特征的合成放射图.
  • 生成过渡视频,比较白人和非裔美国人的骨盆,由专家外科医生和放射科医生分析.

主要成果:

  • 分析了480,407张盆腔放射图,并指出白人患者占主导地位.
  • 生成模型产生了高质量的图像,Fréchet初始距离为6.8.
  • 鉴定了六个不同的放射性特征:交互骨距离,骨关节炎程度,阻塞孔形状,股骨部轴角度,骨盆环形状和股骨皮层厚度.

结论:

  • 生成模型在揭示医学成像数据集中的差异方面表现有前途.
  • 基于种族的放射性差异的可视化有助于识别AI模型中的偏见.
  • 这种方法支持开发更公平的医疗保健AI.