{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T14:19:59Z","timestamp":1771337999666,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T00:00:00Z","timestamp":1769385600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>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\u2013RGB 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 &lt; 0.01), highlighting the effectiveness of the correction approach. Furthermore, corrected temperatures successfully distinguished DF patients from healthy controls (p &lt; 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.<\/jats:p>","DOI":"10.3390\/jsan15010013","type":"journal-article","created":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T08:14:23Z","timestamp":1769415263000},"page":"13","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["AI Correction of Smartphone Thermal Images: Application to Diabetic Plantar Foot"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-6629-9599","authenticated-orcid":false,"given":"Hafid","family":"Elfahimi","sequence":"first","affiliation":[{"name":"IRF-SIC Laboratory, Ibn Zohr University, Agadir 80000, Morocco"}]},{"given":"Rachid","family":"Harba","sequence":"additional","affiliation":[{"name":"PRISME Laboratory, Orl\u00e9ans University, 45072 Orl\u00e9ans, France"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4222-6253","authenticated-orcid":false,"given":"Asma","family":"Aferhane","sequence":"additional","affiliation":[{"name":"IRF-SIC Laboratory, Ibn Zohr University, Agadir 80000, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2756-1399","authenticated-orcid":false,"given":"Hassan","family":"Douzi","sequence":"additional","affiliation":[{"name":"IRF-SIC Laboratory, Ibn Zohr University, Agadir 80000, Morocco"}]},{"given":"Ikram","family":"Damoune","sequence":"additional","affiliation":[{"name":"Faculty of Medicine and Pharmacy, Ibn Zohr University, Agadir 80000, Morocco"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.2522\/ptj.20080020","article-title":"Epidemiology of diabetes and diabetes-related complications","volume":"88","author":"Deshpande","year":"2008","journal-title":"Phys. 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