Plantar Thermogram Analysis Using Deep Learning for Diabetic Foot Risk Classification
6
Issued Date
2025-01-01
Resource Type
eISSN
19322968
Scopus ID
2-s2.0-85218711669
Journal Title
Journal of Diabetes Science and Technology
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Diabetes Science and Technology (2025)
Suggested Citation
Panamonta V., Jerawatana R., Ariyaprayoon P., Looareesuwan P., Ongphiphadhanakul B., Sriphrapradang C., Chailurkit L., Ongphiphadhanakul B. Plantar Thermogram Analysis Using Deep Learning for Diabetic Foot Risk Classification. Journal of Diabetes Science and Technology (2025). doi:10.1177/19322968251316563 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/105510
Title
Plantar Thermogram Analysis Using Deep Learning for Diabetic Foot Risk Classification
Corresponding Author(s)
Other Contributor(s)
Abstract
Aims: Thermography is a noninvasive method to identify patients at risk of diabetic foot ulcers. In this study, we employed thermography and deep learning to stratify patients with diabetes at risk of developing foot ulcers. Methods: We prospectively recorded clinical data and plantar thermograms for adult patients with diabetes who underwent diabetic foot screening. A total of 153 thermal images were analyzed using a deep learning algorithm to determine the risk of diabetic foot ulcers. The neural network was trained using a balanced dataset consisting of 98 thermal images (49 normal and 49 abnormal), with 80% allocated for training and 20% for validation. The trained model was then validated on a separate testing dataset consisting of 55 thermal images (42 normal and 13 abnormal). The neural network was trained to prioritize higher sensitivity in identifying at-risk feet for screening purposes. Results: Participants had a mean age of 63.1 ± 12.6 years (52.3% female), and 62.1% had been diagnosed with diabetes for more than 10 years. The average body mass index was 27.5 ± 5.6 kg/m2. Of the thermal images, 91 were classified as category 0 and 62 as categories 1 to 3, according to the diabetic foot risk classification system of the International Working Group on the Diabetic Foot. Using five-fold cross-validation, the neural network model achieved an overall accuracy of 71.8 ± 4.9%, a sensitivity of 81.2 ± 10.0%, and a specificity of 64.0 ± 7.4%. Additionally, the Matthews correlation coefficient was 0.46 ± 0.08% Conclusions: These results suggest that thermography combined with deep learning could be developed for screening purposes to stratify patients at risk of developing diabetic foot ulcers.