Automatic evaluation of atlantoaxial subluxation in rheumatoid arthritis by a deep learning model

Background This work aims to develop a deep learning model, assessing atlantoaxial subluxation (AAS) in rheumatoid arthritis (RA), which can often be ambiguous in clinical practice. Methods We collected 4691 X-ray images of the cervical spine of the 906 patients with RA. Among these images, 3480 wer...

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Bibliographic Details
Published in:Arthritis research & therapy 2023-09, Vol.25 (1), p.1-181, Article 181
Main Authors: Okita, Yasutaka, Hirano, Toru, Wang, Bowen, Nakashima, Yuta, Minoda, Saki, Nagahara, Hajime, Kumanogoh, Atsushi
Format: Article
Language:English
Subjects:
Anabolic steroids
Analysis
Arthritis
Artificial intelligence
Asymptomatic
Atlantoaxial subluxation
Cervical spine
Datasets
Deep learning
Disease
Dosage and administration
Drug therapy
Hospitals
Machine learning
Methods
Neural networks
Patient outcomes
Rheumatoid arthritis
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Summary:Background This work aims to develop a deep learning model, assessing atlantoaxial subluxation (AAS) in rheumatoid arthritis (RA), which can often be ambiguous in clinical practice. Methods We 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. Results Among 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. Conclusions We 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. Keywords: Rheumatoid arthritis, Deep learning, Machine learning, Cervical spine, Atlantoaxial subluxation
ISSN:1478-6362
1478-6354
1478-6362
DOI:10.1186/s13075-023-03172-x