A deep learning classification of metacarpophalangeal joints synovial proliferation in rheumatoid arthritis by ultrasound images. Issue 2 (17th January 2022)

• Record Type: Journal Article
• Authors: Wu, Min; Wu, Huaiuy; Wu, Lili; Cui, Chen; Shi, Siyuan; Xu, Jinfeng; Liu, Yan; Dong, Fajin
• Is Part Of: Journal of clinical ultrasound. Volume 50:Issue 2(2022)
• Page Start: 296
• Page End: 301
• Publication Date: 2022-01-17
• Subjects: artificial intelligence -- deep learning -- rheumatoid arthritis -- synovitis -- ultrasonography; Ultrasonics in medicine -- Periodicals; 616.07543
• DOI: 10.1002/jcu.23143
• Languages: English
• ISSNs: 0091-2751

Abstract

Abstract: 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 and 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 SP‐no versus SP‐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 SP 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 versus 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 DenseNet 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. Abstract : The OMERACT‐EULAR Synovitis Scoring (OESS) system has been introduced to standardize the use of ultrasound in Rheumatoid arthritis (RA). Automatic classification of RA metacarpophalangeal joint conditions in ultrasound images is feasible by Deep Learning (DL) method. The feasibility of using DL to create an automatic RA condition classification system. The proposed method can be an alternative to the initial screening of RA patients.