An optimal deep learning model for the scoring of radiographic damage in patients with ankylosing spondylitis

Yen-Ju Chen; Der-Yuan Chen; Haw-Chang Lan; An-Chih Huang; Yi-Hsing Chen; Wen-Nan Huang; Hsin-Hua Chen

doi:10.1177/1759720X241285973

An optimal deep learning model for the scoring of radiographic damage in patients with ankylosing spondylitis

Ther Adv Musculoskelet Dis. 2024 Oct 7:16:1759720X241285973. doi: 10.1177/1759720X241285973. eCollection 2024.

Authors

Yen-Ju Chen^{1

2

3}, Der-Yuan Chen^{4

5

6}, Haw-Chang Lan⁷, An-Chih Huang⁸, Yi-Hsing Chen^{1

9

10}, Wen-Nan Huang^{1

9

10

11}, Hsin-Hua Chen^{12

9

10

11

13

14

15}

Affiliations

¹ Division of Allergy, Immunology and Rheumatology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan.
² Institute of Clinical Medicine, National Yang Ming University, Taipei, Taiwan.
³ Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan.
⁴ Institute of Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan.
⁵ Rheumatology and Immunology Center, China Medical University Hospital, Taichung, Taiwan.
⁶ College of Medicine, China Medical University, Taichung, Taiwan.
⁷ Department of Diagnostic Radiology, Tungs' Taichung MetroHarbor Hospital, Taichung, Taiwan.
⁸ Advanced Tech BU, Acer Incorporated, New Taipei City, Taiwan.
⁹ Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan.
¹⁰ School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
¹¹ Department of Business Administration, Ling Tung University, Taichung, Taiwan.
¹² Division of Allergy, Immunology and Rheumatology, Department of Internal Medicine, Taichung Veterans General Hospital, 1650 Taiwan Boulevard Sect. 4, Taichung 40705, Taiwan.
¹³ Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung, Taiwan.
¹⁴ Institute of Biomedical Science and Rong Hsing Research Center for Translational Medicine, Big Data Center, National Chung Hsing University, Taichung, Taiwan.
¹⁵ Institute of Public Health and Community Medicine Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.

Abstract

Background: Detecting vertebral structural damage in patients with ankylosing spondylitis (AS) is crucial for understanding disease progression and in research settings.

Objectives: This study aimed to use deep learning to score the modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS) using lateral X-ray images of the cervical and lumbar spine in patients with AS in Asian populations.

Design: A deep learning model was developed to automate the scoring of mSASSS based on X-ray images.

Methods: We enrolled patients with AS at a tertiary medical center in Taiwan from August 1, 2001 to December 30, 2020. A localization module was used to locate the vertebral bodies in the images of the cervical and lumbar spine. Images were then extracted from these localized points and fed into a classification module to determine whether common lesions of AS were present. The scores of each localized point were calculated based on the presence of these lesions and summed to obtain the total mSASSS score. The performance of the model was evaluated on both validation set and testing set.

Results: This study reviewed X-ray image data from 554 patients diagnosed with AS, which were then annotated by 3 medical experts for structural changes. The accuracy for judging various structural changes in the validation set ranged from 0.886 to 0.985, whereas the accuracy for scoring the single vertebral corner in the test set was 0.865.

Conclusion: This study demonstrated a well-trained deep learning model of mSASSS scoring for detecting the vertebral structural damage in patients with AS at an accuracy rate of 86.5%. This artificial intelligence model would provide real-time mSASSS assessment for physicians to help better assist in radiographic status evaluation with minimal human errors. Furthermore, it can assist in a research setting by offering a consistent and objective method of scoring, which could enhance the reproducibility and reliability of clinical studies.

Keywords: ankylosing spondylitis; artificial intelligence; deep learning; mSASSS; radiographic damage.