ArticleArthritis research & therapy2023
Automatic evaluation of atlantoaxial subluxation in rheumatoid arthritis by a deep learning model.
Article in Arthritis research & therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in rheumatoid arthritis: current applications and future perspectives.Frontiers in medicine · 2026Pooled it
- Use of Artificial Intelligence in Rheumatoid Arthritis: Advancements and Novel Perspectives.Journal of clinical medicine · 2026Review
- Comparison of multistage and single-stage framework for automated landmark localization and radiologic measurement: a case of the C1-2 complex on cervical spine lateral radiographs.BMC medical imaging · 2026Article
- Research on a deep learning-based model for measurement of X-ray imaging parameters of atlantoaxial joint.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2025Article
- Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human.Journal of rheumatic diseases · 2025Review
- Current application, possibilities, and challenges of artificial intelligence in the management of rheumatoid arthritis, axial spondyloarthritis, and psoriatic arthritis.Therapeutic advances in musculoskeletal disease · 2025Review
- A machine learning based quantification system for automated diagnosis of lumbar spondylolisthesis on spinal X-rays.Heliyon · 2024Article
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThis work aims to develop a deep learning model, assessing atlantoaxial subluxation (AAS) in rheumatoid arthritis (RA), which can often be ambiguous in clinical practice.
methodsWe collected 4691 X-ray images of the cervical spine of the 906 patients with RA. Among these images, 3480 were used for training the deep learning model, 803 were used for validating the model during the training process, and the remaining 408 were used for testing the performance of the trained model. The two-dimensional key points' detection model of Deep High-Resolution Representation Learning for Human Pose Estimation was adopted as the base convolutional neural network model. The model inferred four coordinates to calculate the atlantodental interval (ADI) and space available for the spinal cord (SAC). Finally, these values were compared with those by clinicians to evaluate the performance of the model.
resultsAmong the 408 cervical images for testing the performance, the trained model correctly identified the four coordinates in 99.5% of the dataset. The values of ADI and SAC were positively correlated among the model and two clinicians. The sensitivity of AAS diagnosis with ADI or SAC by the model was 0.86 and 0.97 respectively. The specificity of that was 0.57 and 0.5 respectively.
conclusionsWe present the development of a deep learning model for the evaluation of cervical lesions of patients with RA. The model was demonstrably shown to be useful for quantitative evaluation.
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