Application of Artificial Intelligence in rheumatic disease classification: an example of ankylosing spondylitis severity inspection model.
Authors
Affiliations (11)
Affiliations (11)
- Data Finance Innovation (DFI) Research Center, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
- National Council for Sustainable Development (NCSD), Executive Yuan, Taiwan Govt., Taiwan.
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan.
- Faculty of Engineering Sciences, University College London (UCL),London, UK.
- Department of Medical Imaging and Radiological Sciences, Chung Shan Medical University, Taichung, Taiwan.
- aetherAI Co., Ltd, Taipei, Taiwan.
- Functional Neurosurgery Division, Neurological Institute, Taichung Veterans General Hospital, Taichung, Taiwan.
- Department of Rehabilitation, Jen-Teh Junior College of Medicine, Nursing and Management, Miaoli County, Taiwan.
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan.
- Division of Allergy, Immunology and Rheumatology, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan.
- Graduate Institute of Integrated Medicine, China Medical University, Taichung, Taiwan.
Abstract
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). 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. 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. 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.