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Streszczenie:
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.

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