深度学习辅助膝关节骨关节炎在普通放射图上自动分级:多视图X射线图像的价值和先前知识
Wei Li1,2, Zhongli Xiao1,2, Jin Liu1,2
1Department of Radiology, the Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.
Quantitative imaging in medicine and surgery
|June 7, 2023
概括
深度学习算法使用X射线准确地检测和评分膝关节关节炎 (OA). 结合多个观点和先前知识,可显著提高膝关节OA检测的诊断性能.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 膝关节骨关节炎 (OA) 对健康构成重大风险,需要准确的诊断和分类才能有效治疗.
- 目前的诊断方法依赖于专家对普通放射图的解释.
研究的目的:
- 评估深度学习 (DL) 算法在检测和分级膝关节OA中的性能.
- 确定多视图图像和先前知识对DL算法的诊断准确性的影响.
主要方法:
- 对1,846名患者的4,200张膝关节X射线图像进行了回顾性分析.
- 专家放射科医生的凯尔格伦-劳伦斯 (K-L) 评分是黄金标准.
- 四个DL模型进行了比较,它们在使用多视图图像和自动区域分割方面存在差异.
主要成果:
- 结合多视图图像和先前知识的DL模型获得了最高的性能 (AUC 0.96/0.95).
- 这种先进的DL模型的整体精度为0.96,超过了经验丰富的放射科医生0.86.6的精度.
- 前后和侧图像与先前区域细分的整合明显影响了诊断结果.
结论:
- 深度学习模型可以根据K-L分级准确地检测和分类膝关节OA.
- 使用多视图X射线图像和事先的知识显著提高了膝盖OADL算法的分类效率.
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