A Deep Learning Classification of Metacarpophalangeal Synovial Proliferation in Rheumatoid Arthritis by Ultrasound Images

Objective: To evaluate if an automatic classification of Rheumatoid arthritis (RA) metacarpophalangeal joint conditions in ultrasound images is feasible by deep learning (DL) method, to provide a more objective, automated and fast way of RA diagnosis in clinical setting. Materials and Methods: DenseNet-based DL model was used, both training and testing are implemented in TensorFlow 1.13.1 with Keras DL libraries. The area under curve (AUC), accuracy, sensitivity and specificity values with 95% CIs were reported. The statistical analysis was performed by using scikit-learn libraries in Python 3.7. Results: A total of 1337 RA ultrasound images were acquired from 208 patients, the number of images is 313, 657, 178 and 189 in OESS Grade L0, L1, L2, and L3 respectively. In Classification Scenario 1 S.P.-no vs. S.P.-yes, three experiments with region of interest of size 192*448 (Group 1), 96*224 (Group 2) and 96*224 stacked with pre-segmented annotated mask of S.P. area (Group 3) as input achieve an AUC of 0.863 (95% CI: 0.809, 0.917), 0.861 (95% CI: 0.805, 0.916) and 0.886 (95% CI: 0.836, 0.936), respectively. In Classification Scenario 2 Healthy vs. Diseased, experiments in Group 1, Group 2 and Group 3 achieve an AUC of 0.848 (95% CI: 0.799, 0.896), 0.864 (95% CI: 0.819, 0.909) and 0.916 (95% CI: 0.883, 0.952), respectively. Conclusion: We combined Dense Net model with ultrasound images for RA condition assessment. The feasibility of using DL to create an automatic RA condition classification system was also demonstrated. The proposed method can be an alternative to the initial screening of RA patients. Funding Statement: None. Declaration of Interests: The authors declare that they have no competing interests. Ethics Approval Statement: The study was approved by the ethics committee of Shenzhen people’s Hospital.