Application of Artificial Intelligence in rheumatic disease classification: an example of ankylosing spondylitis severity inspection model
Background The development of the Artificial Intelligence (AI)-based severity inspection model for ankylosing spondylitis (AS) could support health professionals to rapidly assess the severity of the disease, enhance proficiency, and reduce the demands of human resources. This paper aims to develop...
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Taylor & Francis Group
2025-12-01
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| Series: | Annals of Medicine |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/07853890.2025.2512131 |
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| author | Chih-Wei Chen Hao-Hung Tsai Chao-Yuan Yeh Cheng-Kun Yang Hsi-Kai Tsou Pui-Ying Leong James Cheng-Chung Wei |
| author_facet | Chih-Wei Chen Hao-Hung Tsai Chao-Yuan Yeh Cheng-Kun Yang Hsi-Kai Tsou Pui-Ying Leong James Cheng-Chung Wei |
| author_sort | Chih-Wei Chen |
| collection | DOAJ |
| description | Background The development of the Artificial Intelligence (AI)-based severity inspection model for ankylosing spondylitis (AS) could support health professionals to rapidly assess the severity of the disease, enhance proficiency, and reduce the demands of human resources. This paper aims to develop an AI-based severity inspection model for AS using patients’ X-ray images and modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS).Methods The numerical simulation with AI is developed following the progress of data preprocessing, building and testing the model, and then the model. The training data is preprocessed by inviting three experts to check the X-ray images of 222 patients following the Gold Standard. The model is then developed through two stages, including keypoint detection and mSASSS evaluation. The two-stage AI-based severity inspection model for AS was developed to automatically detect spine points and evaluate mSASSS scores. At last, the data obtained from the developed model was compared with those from experts’ assessment to analyse the accuracy of the model. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki.Results The spine point detection at the first stage achieved 1.57 micrometres in mean error distance with the ground truth, and the second stage of the classification network can reach 0.81 in mean accuracy. The model can correctly identify 97.4% patches belonging to mSASSS score 3, while those belonging to score 0 can still be classified into scores 1 or 2.Conclusion The automatic severity inspection model for AS developed in this paper is accurate and can support health professionals in rapidly assessing the severity of AS, enhancing assessment proficiency, and reducing the demands of human resources. |
| format | Article |
| id | doaj-art-0e65ddf9f8824bb292db31300dc2d9dd |
| institution | DOAJ |
| issn | 0785-3890 1365-2060 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Annals of Medicine |
| spelling | doaj-art-0e65ddf9f8824bb292db31300dc2d9dd2025-08-20T03:11:05ZengTaylor & Francis GroupAnnals of Medicine0785-38901365-20602025-12-0157110.1080/07853890.2025.2512131Application of Artificial Intelligence in rheumatic disease classification: an example of ankylosing spondylitis severity inspection modelChih-Wei Chen0Hao-Hung Tsai1Chao-Yuan Yeh2Cheng-Kun Yang3Hsi-Kai Tsou4Pui-Ying Leong5James Cheng-Chung Wei6Data Finance Innovation (DFI) Research Center, National Yang Ming Chiao Tung University, Hsinchu, TaiwanDepartment of Medical Imaging and Radiological Sciences, Chung Shan Medical University, Taichung, TaiwanaetherAI Co., Ltd, Taipei, TaiwanaetherAI Co., Ltd, Taipei, TaiwanFunctional Neurosurgery Division, Neurological Institute, Taichung Veterans General Hospital, Taichung, TaiwanInstitute of Medicine, Chung Shan Medical University, Taichung, TaiwanInstitute of Medicine, Chung Shan Medical University, Taichung, TaiwanBackground The development of the Artificial Intelligence (AI)-based severity inspection model for ankylosing spondylitis (AS) could support health professionals to rapidly assess the severity of the disease, enhance proficiency, and reduce the demands of human resources. This paper aims to develop an AI-based severity inspection model for AS using patients’ X-ray images and modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS).Methods The numerical simulation with AI is developed following the progress of data preprocessing, building and testing the model, and then the model. The training data is preprocessed by inviting three experts to check the X-ray images of 222 patients following the Gold Standard. The model is then developed through two stages, including keypoint detection and mSASSS evaluation. The two-stage AI-based severity inspection model for AS was developed to automatically detect spine points and evaluate mSASSS scores. At last, the data obtained from the developed model was compared with those from experts’ assessment to analyse the accuracy of the model. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki.Results The spine point detection at the first stage achieved 1.57 micrometres in mean error distance with the ground truth, and the second stage of the classification network can reach 0.81 in mean accuracy. The model can correctly identify 97.4% patches belonging to mSASSS score 3, while those belonging to score 0 can still be classified into scores 1 or 2.Conclusion The automatic severity inspection model for AS developed in this paper is accurate and can support health professionals in rapidly assessing the severity of AS, enhancing assessment proficiency, and reducing the demands of human resources.https://www.tandfonline.com/doi/10.1080/07853890.2025.2512131Ankylosing spondylitisArtificial Intelligenceseverity inspection modelX-ray images |
| spellingShingle | Chih-Wei Chen Hao-Hung Tsai Chao-Yuan Yeh Cheng-Kun Yang Hsi-Kai Tsou Pui-Ying Leong James Cheng-Chung Wei Application of Artificial Intelligence in rheumatic disease classification: an example of ankylosing spondylitis severity inspection model Annals of Medicine Ankylosing spondylitis Artificial Intelligence severity inspection model X-ray images |
| title | Application of Artificial Intelligence in rheumatic disease classification: an example of ankylosing spondylitis severity inspection model |
| title_full | Application of Artificial Intelligence in rheumatic disease classification: an example of ankylosing spondylitis severity inspection model |
| title_fullStr | Application of Artificial Intelligence in rheumatic disease classification: an example of ankylosing spondylitis severity inspection model |
| title_full_unstemmed | Application of Artificial Intelligence in rheumatic disease classification: an example of ankylosing spondylitis severity inspection model |
| title_short | Application of Artificial Intelligence in rheumatic disease classification: an example of ankylosing spondylitis severity inspection model |
| title_sort | application of artificial intelligence in rheumatic disease classification an example of ankylosing spondylitis severity inspection model |
| topic | Ankylosing spondylitis Artificial Intelligence severity inspection model X-ray images |
| url | https://www.tandfonline.com/doi/10.1080/07853890.2025.2512131 |
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