基于 X 射线图像的膝关节骨关节炎严重程度自动分级：一种分层分类方法。

Automatic knee osteoarthritis severity grading based on X-ray images using a hierarchical classification method.

机构信息

School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China.

Department of Orthopedics Surgery, Orthopedic Research Institute, and Center for High Altitude Medicine, West China Hospital, Sichuan University, Chengdu, China.

出版信息

Arthritis Res Ther. 2024 Nov 18;26(1):203. doi: 10.1186/s13075-024-03416-4.

DOI:10.1186/s13075-024-03416-4

PMID:39558425

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11571664/

Abstract

BACKGROUND

This study aims to develop a hierarchical classification method to automatically assess the severity of knee osteoarthritis (KOA).

METHODS

This retrospective study recruited 4074 patients. Clinical diagnostic indicators and clinical diagnostic processes were applied to develop a hierarchical classification method that involved four sub-task classifications. These four sub-task classifications were the classification of Kellgren-Lawrence (KL) grade 0-2 and KL grade 3-4, KL grade 3 and KL grade 4, KL grade 0 and KL grade 1-2, and KL grade 1 and KL grade 2, respectively. To extract the features of clinical diagnostic indicators, four U-Net models were first used to segment the total joint space (TJS), the lateral joint space (LJS), the medial joint space (MJS), and osteophytes, respectively. Based on the segmentation result of TJS, the region of knee subchondral bone was generated. Then, geometric features were extracted based on segmentation results of the LJS, MJS, TJS, and osteophytes, while radiomic features were extracted from the knee subchondral bone. Finally, the geometric features, radiomic features, and combination of geometric features and radiomic features were used to construct the geometric model, radiomic model, and combined model in KL grading, respectively. A strict decision strategy was used to evaluate the performance of the hierarchical classification method in all X-ray images of testing cohort.

RESULTS

The U-Net models achieved relatively satisfying performances in the segmentation of the TJS, the LJS, the MJS, and the osteophytes with the dice similarity coefficient of 0.88, 0.86, 0.88, and 0.64 respectively. The combined models achieved the best performance in KL grading. The accuracy of combined models was 98.50%, 81.65%, 82.07%, and 74.10% in the classification of KL grade 0-2 and KL grade 3-4, KL grade 3 and KL grade 4, KL grade 0 and KL grade 1-2, and KL grade 1 and KL grade 2, respectively. For all X-ray images of the testing cohort, the accuracy of the hierarchical classification method was 65.98%.

CONCLUSION

The hierarchical classification method developed in the current study is a feasible approach to assess the severity of KOA.

摘要

背景

本研究旨在开发一种分层分类方法，以自动评估膝骨关节炎（KOA）的严重程度。

方法

本回顾性研究纳入了 4074 名患者。应用临床诊断指标和临床诊断流程，开发了一种分层分类方法，包括四个子任务分类。这四个子任务分类分别为 Kellgren-Lawrence（KL）分级 0-2 和 KL 分级 3-4、KL 分级 3 和 KL 分级 4、KL 分级 0 和 KL 分级 1-2 以及 KL 分级 1 和 KL 分级 2。为了提取临床诊断指标的特征，首先使用四个 U-Net 模型分别对全关节间隙（TJS）、外侧关节间隙（LJS）、内侧关节间隙（MJS）和骨赘进行分割。基于 TJS 的分割结果，生成膝关节软骨下骨的区域。然后，基于 LJS、MJS、TJS 和骨赘的分割结果提取几何特征，并从膝关节软骨下骨提取放射组学特征。最后，将几何特征、放射组学特征以及几何特征和放射组学特征的组合用于构建 KL 分级中的几何模型、放射组学模型和组合模型。使用严格的决策策略评估分层分类方法在测试队列的所有 X 射线图像中的性能。

结果

U-Net 模型在 TJS、LJS、MJS 和骨赘的分割中均取得了较为满意的性能，其 Dice 相似系数分别为 0.88、0.86、0.88 和 0.64。组合模型在 KL 分级中表现最佳。在 KL 分级 0-2 和 KL 分级 3-4、KL 分级 3 和 KL 分级 4、KL 分级 0 和 KL 分级 1-2 以及 KL 分级 1 和 KL 分级 2 的分类中，组合模型的准确率分别为 98.50%、81.65%、82.07%和 74.10%。对于测试队列的所有 X 射线图像，分层分类方法的准确率为 65.98%。