Partners

Abstract:
Reliable assessment of rheumatoid arthritis (RA) using infrared thermography requires a measurement framework capable of characterizing the dynamic thermal response to controlled excitation while limiting sensitivity to acquisition conditions, inter-subject variability, and computational instability. This study presents a measurement-oriented Active Dynamic Thermography (ADT) framework in which a dual-stream late-fusion stage combines radiomic descriptors with convolutional representations of the acquired thermal response. The
measurand was defined as the spatio-temporal thermal response of the hand during post-stimulus rewarming, interpreted as an indirect functional indicator of superficial microvascular and subcutaneous vascularbed activity associated with inflammatory processes. Controlled cold excitation provided a standardized thermal perturbation for observing vascular recovery dynamics, while radiometric verification and protocol
standardization were used to reduce non-biological acquisition variability.
The proposed framework was evaluated on thermographic recordings acquired from 187 patients with clinically confirmed RA and 292 healthy controls. To assess the robustness of the diagnostic output to cohort partitioning, experiments were repeated across ten independent subject-disjoint realizations. FUSION achieved a mean accuracy of 91.0%, balanced accuracy of 91.4%, Matthews correlation coefficient (MCC) of 0.819, ROC-AUC of 0.954, and PR-AUC of 0.901. Compared with the evaluated standalone baselines, FUSION showed higher mean performance and lower descriptive inter-seed variability. Ranking and distributional
analyses suggested improved stability. Paired Wilcoxon comparisons showed significant improvements over the standalone CNN baseline for several discriminative metrics after adjustment, whereas differences relative to the stronger INR baseline did not reach the adjusted significance threshold.
The results support the feasibility of a measurement-oriented ADT framework in which complementary thermal descriptors are integrated within a computational decision stage. The fusion component should therefore be interpreted as part of the broader thermal measurement chain rather than as the measurement principle itself. Further test-retest, inter-device, and external validation is required to establish physical repeatability, reproducibility, and clinical generalizability.

Keywords:
Metrology, Infrared thermography, Sensor fusion, Measurement stability, Calibration, Radiomics, Rheumatoid arthritis

Abstract:
Rheumatoid arthritis (RA) remains a condition in which complementary, non-invasive assessment tools are actively explored. While previous thermography studies have focused mainly on temperature dynamics or
texture features, the diagnostic value of information-theoretic complexity measures is still not well understood. This study evaluates three such measures, Lempel–Ziv complexity (LZC), permutation complexity (PC), and
belief permutation entropy (BPE), for distinguishing RA patients from healthy individuals, with emphasis on the impact of different symbolic encoding strategies under no-cooling and cooling conditions. A dataset of 477 hand thermograms (291 healthy controls, 186 RA patients) was analyzed using four encoding schemes: binary, slope-direction, zero-crossing, and multilevel thresholding. All statistical conclusions were assessed at the
subject level using median aggregation per participant, with multiplicity-adjusted testing on protocol-matched cohorts to avoid within-subject dependence and availability bias. The primary endpoint was subject-level
discrimination quantified by effect size and ROC–AUC. Results indicate that the diagnostic utility of complexity measures in hand thermography strongly depends on both encoding choices and the acquisition protocol. Under
no-cooling conditions, several LZC variants and PC showed statistically significant but small group differences after BH-FDR correction (|

Keywords:
Rheumatoid arthritis, Infrared thermography, Information theory, Lempel–Ziv complexity, Permutation entropy, Belief permutation entropy, Symbolic encoding

Abstract:
Rheumatoid arthritis (RA) is a chronic autoimmune disease driven by synovial immunopathology, where innate immune activity and neurobiological remodeling necessitate timely and precise diagnostic interventions. While
thermography provides a non-invasive window into the altered perfusion and thermal dynamics associated with such joint inflammation, its clinical adoption has been hindered by the computational demands of traditional
AI. We address this by proposing a novel Spiking Neural Network (SNN) framework that aligns diagnostic automation with the event-driven nature of physiological signals. By encoding spatial temperature patterns into
temporally structured spike trains, our approach introduces a biologically inspired static-to-dynamic translation, where temporal structure is computationally derived from spatial thermal distributions rather than directly measured inter-frame dynamics. To ensure statistical rigor, a strict patient-level data split was applied to a dataset of
291 healthy controls and 186 RA patients. We evaluated three SNN paradigms:Tempotron, Surrogate Gradient Learning (SGL), and Bio-Inspired Active Learning (BAL) to optimize the trade-off between diagnostic precision and efficiency. The Tempotron learning rule achieved a peak validation accuracy of up to 90.62% on a fixed patient-level split, demonstrating superior sensitivity to spatio-temporal signatures, while SGL offered the most efficient training convergence (563 s). Notably, our framework exhibits strong potential for reduced energy demands compared to traditional frame-based architectures. As one of the first studies to explore the intersection of neuromorphic computing and thermographic signatures associated with synovial inflammation, this study demonstrates the potential of spiking neural networks as lightweight and biologically inspired tools for automated RA screening in resource-constrained settings.

Keywords:
Spiking neural networks (SNN), Rheumatoid arthritis (RA), Thermographic imaging, Bio-inspired learning algorithms, Green AI, Neuromorphic computing