Assessment of inflammation in patients with rheumatoid arthritis using thermography and machine learning: a fast and automated technique

Abstract

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.

Keywords: arthritis, rheumatoid; inflammation; synovitis.

Figure 1

Eight-bit grey-scale thermal images of the hands of a patient with rheumatoid arthritis from the validation set after noise reduction, background removal and contrast enhancement.

Figure 2

Association between the Thermographic Joint Inflammation Score (ThermoJIS) and ultrasound. (A) Correlation between the grey-scale synovial hypertrophy (GS) sum score and the ThermoJIS; (B) Correlation between the power Doppler (PD) sum score and the ThermoJIS.

Figure 3

Analysis of the area under the receiver operating curve (AUROC) of the Thermographic Joint Inflammation Score (ThermoJIS) for the detection of active synovitis. (A) Considering the entire validation set (AUROC, 0.78; 95% CI 0.71 to 0.86, p<0.001); (B) Considering ThermoJIS values lower than 3.46 and greater than 5.65 (AUROC, 0.86; 95% CI 0.78 to 0.95, p<0.001). TPR, True Positive Rate; FPR, False Positive Rate.

Figure 4

Probability of presenting active synovitis at different ThermoJIS intervals in the validation set. The baseline probability (dashed line) is the proportion of patients with active synovitis in the set, that is, the random probability. ThermoJIS, Thermographic Joint Inflammation Score.

Figure 5

ThermoJIS distributions according to clinical remission criteria in patients with and without active synovitis. DAS28-CRP Rem (DAS28-CRP <2.6), CDAI Rem (CDAI ≤2.8), SDAI Rem (SDAI ≤3.3), and Boolean Rem (all ≤1: 28 tender joint count, 28 swollen joint count, C-reactive protein (mg/dL) and Patient Global Assessment). *p<0.05; **p<0.01; ***p<0.001. CDAI, Clinical Disease Activity Index; DAS28, 28-joint count Disease Activity Score; GS, grey-scale synovial hypertrophy; PD, power Doppler; SDAI, Simplified Disease Activity index; ThermoJIS, Thermographic Joint Inflammation Score.