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

Abstract

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: Atlantoaxial subluxation; Cervical spine; Deep learning; Machine learning; Rheumatoid arthritis.

Fig. 1

Detection of four coordinates for atlantodental interval and space available for the spinal cord. Representative images processed by the model. The four coordinates mandatory for atlantodental interval and space available for the spinal cord calculations detected by the model are shown in red, purple, green, and yellow dots from the ventral side

Fig. 2

Evaluation of model performance. a Correlations of values of atlantodental interval (ADI, upper column) or space available for the spinal cord (SAC, lower column) among the model, clinician 1, and clinician 2. b Distribution of the difference of  ADI (left three columns) and  SAC (right three columns) among the model, clinician 1, and clinician 2. Each graph is scaled by the range of ADI or  SAC. The values were statistically examined by equivalence test with hypothesis value zero with a range of ± 1.5 mm for ADI and ± 2.0 mm for SAC