Journal
RMD OPEN
Volume 8, Issue 2, Pages -Publisher
BMJ PUBLISHING GROUP
DOI: 10.1136/rmdopen-2022-002458
Keywords
arthritis; rheumatoid; synovitis; inflammation
Categories
Funding
- Singularity Biomed, Sant Cugat del Valles, Spain
Ask authors/readers for more resources
In this study, a machine learning-based computational method was used to automatically assess joint inflammation in rheumatoid arthritis (RA) through thermal imaging of the hands. The method, called Thermographic Joint Inflammation Score (ThermoJIS), was trained using data from patients with different types of arthritis and osteoarthritis, and evaluated based on patients with RA. The results showed that ThermoJIS correlated moderately with ultrasound scores and was able to detect joint inflammation even in patients in clinical remission. This opens up an opportunity to develop new tools for routine detection of joint inflammation.
Objectives Sensitive detection of joint inflammation in rheumatoid arthritis (RA) is crucial to the success of the treat-to-target strategy. In this study, we characterise a novel machine learning-based computational method to automatically assess joint inflammation in RA using thermography of the hands, a fast and non-invasive imaging technique. Methods We recruited 595 patients with arthritis and osteoarthritis, as well as healthy subjects at two hospitals over 4 years. Machine learning was used to assess joint inflammation from the thermal images of the hands using ultrasound as the reference standard, obtaining a Thermographic Joint Inflammation Score (ThermoJIS). The machine learning model was trained and tuned using data from 449 participants with different types of arthritis, osteoarthritis or without rheumatic disease (development set). The performance of the method was evaluated based on 146 patients with RA (validation set) using Spearman's rank correlation coefficient, area under the receiver-operating curve (AUROC), average precision, sensitivity, specificity, positive and negative predictive value and F1-score. Results ThermoJIS correlated moderately with ultrasound scores (grey-scale synovial hypertrophy=0.49, p<0.001; and power Doppler=0.51, p<0.001). The AUROC for ThermoJIS for detecting active synovitis was 0.78 (95% CI, 0.71 to 0.86; p<0.001). In patients with RA in clinical remission, ThermoJIS values were significantly higher when active synovitis was detected by ultrasound. Conclusions ThermoJIS was able to detect joint inflammation in patients with RA, even in those in clinical remission. These results open an opportunity to develop new tools for routine detection of joint inflammation.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available