Jian Pan, Yuangang Wu, Zhenchao Tang, Kaibo Sun, Mingyang Li, Jiayu Sun, Jiangang Liu, Jie Tian, Bin Shen
{"title":"基于X光图像的膝关节骨关节炎严重程度自动分级分层分类法。","authors":"Jian Pan, Yuangang Wu, Zhenchao Tang, Kaibo Sun, Mingyang Li, Jiayu Sun, Jiangang Liu, Jie Tian, Bin Shen","doi":"10.1186/s13075-024-03416-4","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>This study aims to develop a hierarchical classification method to automatically assess the severity of knee osteoarthritis (KOA).</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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%.</p><p><strong>Conclusion: </strong>The hierarchical classification method developed in the current study is a feasible approach to assess the severity of KOA.</p>","PeriodicalId":8419,"journal":{"name":"Arthritis Research & Therapy","volume":"26 1","pages":"203"},"PeriodicalIF":4.9000,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571664/pdf/","citationCount":"0","resultStr":"{\"title\":\"Automatic knee osteoarthritis severity grading based on X-ray images using a hierarchical classification method.\",\"authors\":\"Jian Pan, Yuangang Wu, Zhenchao Tang, Kaibo Sun, Mingyang Li, Jiayu Sun, Jiangang Liu, Jie Tian, Bin Shen\",\"doi\":\"10.1186/s13075-024-03416-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>This study aims to develop a hierarchical classification method to automatically assess the severity of knee osteoarthritis (KOA).</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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%.</p><p><strong>Conclusion: </strong>The hierarchical classification method developed in the current study is a feasible approach to assess the severity of KOA.</p>\",\"PeriodicalId\":8419,\"journal\":{\"name\":\"Arthritis Research & Therapy\",\"volume\":\"26 1\",\"pages\":\"203\"},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571664/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Arthritis Research & Therapy\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1186/s13075-024-03416-4\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Arthritis Research & Therapy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s13075-024-03416-4","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Medicine","Score":null,"Total":0}
Automatic knee osteoarthritis severity grading based on X-ray images using a hierarchical classification method.
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.
期刊介绍：
Established in 1999, Arthritis Research and Therapy is an international, open access, peer-reviewed journal, publishing original articles in the area of musculoskeletal research and therapy as well as, reviews, commentaries and reports. A major focus of the journal is on the immunologic processes leading to inflammation, damage and repair as they relate to autoimmune rheumatic and musculoskeletal conditions, and which inform the translation of this knowledge into advances in clinical care. Original basic, translational and clinical research is considered for publication along with results of early and late phase therapeutic trials, especially as they pertain to the underpinning science that informs clinical observations in interventional studies.
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