ArticlePloS one2026
Texture-enhanced thermography for joint inflammation detection using consumer-grade thermal cameras.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly identification of active synovitis is essential for treat-to-target management in inflammatory arthritis. While MRI and ultrasound are highly sensitive, their clinical use is constrained by high costs, operator dependence, and lack of portability. Infrared thermography (IRT) is rapid and contactless, but conventional temperature-based approaches are sensitive to ambient conditions and inter-individual baseline variability, and contralateral comparison is unreliable in bilaterally involved disease. This study evaluates whether consumer-grade thermal cameras, augmented with texture-based features and complementary differencing strategies, can detect and differentiate joint inflammation.
methodsThermal images of knees, ankles, wrists, and metacarpophalangeal (MCP) joints were acquired from 239 participants (167 with inflammatory arthritis, osteoarthritis (OA), or fibromyalgia; 72 healthy controls). Beyond absolute temperature, higher-order texture features (e.g., entropy, skewness) were extracted to quantify spatial heterogeneity in heat distributions. To reduce environmental and inter-individual variability, two normalization strategies were applied: contralateral left-right (L-R) differencing for unilateral disease and a novel anterior-posterior (A-P) differencing for bilateral or subclinical presentations. Group comparisons used the Mann-Whitney U test (p < 0.05), and effect sizes were summarized using Cohen's d.
resultsCompared with non-differenced features, contralateral differencing markedly strengthened discrimination for unilateral inflammation, with very large effects at the knee (d = 2.15) and ankle (d = 1.83). Texture features, specifically entropy and skewness, outperformed absolute temperature in detecting latent (subclinical) wrist inflammation via A-P differencing ([Formula: see text]). Thermographic signatures also supported differential characterization, distinguishing inflammatory arthritis from OA ([Formula: see text]) and fibromyalgia ([Formula: see text]). Furthermore, A-P gradients showed anatomical specificity, with inflammation reducing gradients in wrists but increasing them in MCP joints (d = 0.64).
conclusionTexture-enhanced thermography, combined with L-R and A-P differencing, shows promise as an accessible approach for identifying active and subclinical joint inflammation. This approach addresses the limitations of bilateral disease in traditional thermography and widens the clinical applicability of IRT screening.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.