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深度学习增强骨关节炎分级标准化标准化

Lacksaya Nagarajan1, Aadyant Khatri1, Arnav Sudan1

  • 1Department of Textile and Fibre Engineering, Indian Institute of Technology Delhi, New Delhi, India.

Tissue engineering. Part A
|November 11, 2023
PubMed
概括

自动化的深度学习模型现在可以使用软骨组织学图像对骨关节炎 (OA) 的严重程度进行分级,克服了手动分级的局限性. 这种AI方法提供了对膝关节OA进展的准确和标准化的评估.

关键词:
在OA分级上,OA的分级是:骨关节炎的严重程度软骨组织病理学深度学习是一种深度学习.评级标准化标准化等级标准化图像的分类图像的分类.骨关节炎是一种关节炎.

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科学领域:

  • 生物医学成像学 生物医学成像学
  • 医学中的人工智能
  • 组织病理学 组织病理学

背景情况:

  • 从软骨组织学中手动分类关节炎 (OA) 是主观的,容易出错.
  • 手动分级的观察者间的变化导致了OA严重程度评估的模两可.
  • 深度学习 (DL) 为客观和自动图像分类提供了一个潜在的解决方案.

研究的目的:

  • 评估训练深度神经网络 (DNN) 的可行性,以使用组织学图像自动分级膝关节OA严重程度.
  • 开发和验证基于OA评分修改的曼金评分系统的DL模型.
  • 为了比较不同DL架构的性能,用于软骨组织学图像分类.

主要方法:

  • 基于Safranin-O染色,状细胞排列和形态学的简化分级系统被开发出来.
  • 组织学图像被,标记,并分为四个OA等级 (0-3).
  • 使用五倍交叉验证方法评估了四个DL架构,DenseNet121被选为最佳模型.

主要成果:

  • DenseNet121的验证准确率约为84%,科恩的卡帕得分为0.632.
  • 该模型显示出出色的区分能力,ROC-AUC值在0.89到0.99.9之间.
  • 来自DL模型的自动评分与专家医疗评估保持一致.

结论:

  • 深度学习模型可以准确地从组织学图像中解释软骨退化,用于OA分级.
  • 这种使用组织学图像的自动化方法为OA评估提供了一个基本的标准化方法.
  • 这项研究表明,在基于组织学的OA分级中,向人工智能驱动的标准化转向范式转变.