Plantar Thermogram Analysis Using Deep Learning for Diabetic Foot Risk Classification.

IF 4.1 Q2 ENDOCRINOLOGY & METABOLISM
Vipawee Panamonta, Ratanaporn Jerawatana, Prapai Ariyaprayoon, Panu Looareesuwan, Benyapa Ongphiphadhanakul, Chutintorn Sriphrapradang, Laor Chailurkit, Boonsong Ongphiphadhanakul
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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.

基于深度学习的足底热图分析用于糖尿病足风险分类。
目的:热成像是一种无创的方法来识别糖尿病足溃疡的风险。在这项研究中,我们采用热成像和深度学习对有足部溃疡风险的糖尿病患者进行分层。方法:我们前瞻性地记录了接受糖尿病足筛查的成年糖尿病患者的临床资料和足底热像图。研究人员使用深度学习算法分析了153张热图像,以确定糖尿病足溃疡的风险。神经网络使用由98张热图像(49张正常和49张异常)组成的平衡数据集进行训练,其中80%用于训练,20%用于验证。然后在由55张热图像(42张正常图像和13张异常图像)组成的单独测试数据集上验证训练后的模型。神经网络被训练为优先考虑更高的灵敏度,以识别有风险的脚,以进行筛查。结果:参与者的平均年龄为63.1±12.6岁(52.3%为女性),62.1%被诊断患有糖尿病超过10年。平均体重指数为27.5±5.6 kg/m2。根据国际糖尿病足工作组的糖尿病足风险分类系统,91张热图像被分类为0类,62张被分类为1至3类。经五重交叉验证,神经网络模型的总体准确率为71.8±4.9%,灵敏度为81.2±10.0%,特异性为64.0±7.4%。Matthews相关系数为0.46±0.08。结论:这些结果表明,热成像结合深度学习可以用于筛查目的,以分层有糖尿病足溃疡风险的患者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Diabetes Science and Technology
Journal of Diabetes Science and Technology Medicine-Internal Medicine
CiteScore
7.50
自引率
12.00%
发文量
148
期刊介绍: The Journal of Diabetes Science and Technology (JDST) is a bi-monthly, peer-reviewed scientific journal published by the Diabetes Technology Society. JDST covers scientific and clinical aspects of diabetes technology including glucose monitoring, insulin and metabolic peptide delivery, the artificial pancreas, digital health, precision medicine, social media, cybersecurity, software for modeling, physiologic monitoring, technology for managing obesity, and diagnostic tests of glycation. The journal also covers the development and use of mobile applications and wireless communication, as well as bioengineered tools such as MEMS, new biomaterials, and nanotechnology to develop new sensors. Articles in JDST cover both basic research and clinical applications of technologies being developed to help people with diabetes.
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