Prevention of complications related to diabetic foot (DF) can now be performed using smartphone-connected thermal cameras. However, the absolute error associated with these devices remains particularly high, compromising measurement reliability, especially under variable environmental conditions. To address this, we introduce a physiologically motivated two-region segmentation task (forehead + plantar foot) to enable stable temperature correction. First, we developed a fully automated joint method for this task, building upon a new multimodal thermal–RGB dataset constructed with detailed annotation procedures. Five deep learning methods (U-Net, U-Net++, SegNet, DE-ResUnet, and DE-ResUnet++) were evaluated and compared to traditional baselines (Adaptive Thresholding and Region Growing), demonstrating the clear advantage of data-driven approaches. The best performance was achieved by the DE-ResUnet++ architecture (Dice score: 98.46%). Second, we validated the correction approach through a clinical study. Results showed that the variance of corrected temperatures was reduced by half compared to absolute values (p < 0.01), highlighting the effectiveness of the correction approach. Furthermore, corrected temperatures successfully distinguished DF patients from healthy controls (p < 0.01), unlike absolute temperatures. These findings suggest that our approach could enhance the performance of smartphone-connected thermal devices and contribute to the early prevention of DF complications.

Objective: Insoles play a crucial role in foot comfort, with their effect on foot temperature being a key factor. This study aims to evaluate and compare the effect of walking with two different insole types—dimple insoles versus a conventional insole—on foot temperature changes in patients with diabetic neuropathy. Methods: Thermal imaging was used to measure the foot temperature of nine participants immediately before and after walking 250 m in each insole. Temperature variations were analyzed for the whole foot across four specific regions to assess and compare the effect of each insole on foot temperature. Results: The Wilcoxon Signed-Rank Test revealed that contralateral temperature differences between the left and right feet after walking ($\left|∆T_{After}\right|$) were significantly ($p<0.05$) lower in dimple insoles compared to the conventional insoles. This effect was particularly strong in the midfoot and toe regions. Conclusions: The results indicate that insole type can influence foot contralateral temperature differences after walking. These findings provide valuable insights for selecting insoles based on thermal data and can have implications in improving patient outcomes.