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• W4387361845 abstract "Abstract Disclosure: V. Panamonta: None. R. Jerawatana: None. P. Ariyaprayoon: None. P. Looareesuwan: None. B. Ongphiphadhanakul: None. C. Sriphrapradang: None. B. Ongphiphadhanakul: None. Background: Diabetic foot ulcers are a major complication of diabetes. They are also the leading cause of nontraumatic lower extremity amputation. The incidence of diabetic foot ulcers is highest in those patients with poor glycemic control and neuropathy. In people with diabetic neuropathy, the feet temperature may change due to decreased blood flow and nerve damage. Thermography has been proposed as a noninvasive modality to identify patients at risk for diabetic foot ulcers. In this study, we used thermography and deep learning to stratify patients with diabetes at risk for developing a foot ulcer. Methods: We prospectively record clinical data and plantar thermogram in adult diabetic patients underwent diabetic foot screening at outpatient clinics at Ramathibodi hospital, Bangkok, Thailand during September to December 2022. Altogether, there were 245 thermal images were analyzed using a deep learning algorithm to determine the risk for diabetic foot ulcers by transfer learning using the pre-trained VGG16 model which is a convolutional neural network for image recognition. Twenty percent of the images were set aside for testing purposes while 20% of the images served as the validation set during training of the neural network. The neural network was trained and weighed more toward higher sensitivity of identifying at risk feet for screening purpose. Results: The study sample consisted of individuals with a mean age of 62.5±12.5 years, 57.1% who had diabetes for more than 10 years. The majority of the participants were females (57.1%). The average body mass index (BMI) was 27.8±9.6 (kg/m2). There were 186 thermal images classified as category 0 and 59 images classified as categories 1 to 3 according to the American Diabetes Association risk classification. The trained neural network achieved 75 % accuracy with 95 % sensitivity and 67 % specificity for classifying thermograms as higher than normal risk in the training dataset. For the testing dataset, the sensitivity and specificity was 75% and 41%, respectively. The accuracy, however, decreased to 50% due mainly to the misclassification of normal thermograms from abnormal. Conclusions: These results suggest that thermography combined with deep learning could potentially be developed for screening purpose to stratify patients at risk of developing diabetic foot ulcers. Further research is needed to validate the results in larger datasets and to explore alternative algorithms for predicting diabetic foot ulcers. Presentation: Thursday, June 15, 2023" @default.
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• W4387361845 date "2023-10-01" @default.
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• W4387361845 title "THU260 Development Of Plantar Thermography For Screening Of Diabetic Neuropathy" @default.
• W4387361845 doi "https://doi.org/10.1210/jendso/bvad114.696" @default.
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