Muralidhara Shishir, Lucieri Adriano, Dengel Andreas, Ahmed Sheraz
Smart Data & Knowledge Services (SDS), German Research Center for Artificial Intelligence (DFKI) GmbH, Trippstadter Strasse 122, 67663 Kaiserslautern, Rhineland-Palatinate Germany.
Computer Science Department, TU Kaiserslautern, Erwin-Schroedinger-Strasse 52, 67663 Kaiserslautern, Rhineland-Palatinate Germany.
Health Inf Sci Syst. 2022 Aug 26;10(1):21. doi: 10.1007/s13755-022-00194-8. eCollection 2022 Dec.
Diabetic foot is a common complication associated with diabetes mellitus (DM) leading to ulcerations in the feet. Due to diabetic neuropathy, most patients have reduced sensitivity to pain. As a result, minor injuries go unnoticed and progress into ulcers. The timely detection of potential ulceration points and intervention is crucial in preventing amputation. Changes in plantar temperature are one of the early signs of ulceration. Previous studies have focused on either binary classification or grading of DM severity, but neglect the holistic consideration of the problem. Moreover, multi-class studies exhibit severe performance variations between different classes.
We propose a new convolutional neural network for discrimination between non-DM and five DM severity grades from plantar thermal images and compare its performance against pre-trained networks such as AlexNet and related works. We address the lack of data and imbalanced class distribution, prevalent in prior work, achieving well-balanced classification performance.
Our proposed model achieved the best performance with a mean accuracy of 0.9827, mean sensitivity of 0.9684 and mean specificity of 0.9892 in combined diabetic foot detection and grading.
To the best of our knowledge, this study sets a new state-of-the-art in plantar foot thermogram detection and grading, while being the first to implement a holistic multi-class classification and grading solution. Reliable automatic thermogram grading is a first step towards the development of smart health devices for DM patients.
糖尿病足是糖尿病（DM）常见的并发症，可导致足部溃疡。由于糖尿病神经病变，大多数患者对疼痛的敏感度降低。因此，小伤口往往未被察觉，进而发展为溃疡。及时检测潜在的溃疡点并进行干预对于预防截肢至关重要。足底温度变化是溃疡的早期迹象之一。以往的研究要么集中于糖尿病严重程度的二元分类，要么是分级，但忽略了对该问题的整体考虑。此外，多类别研究在不同类别之间表现出严重的性能差异。
我们提出了一种新的卷积神经网络，用于从足底热图像中区分非糖尿病和五个糖尿病严重程度等级，并将其性能与预训练网络（如AlexNet）及相关工作进行比较。我们解决了先前工作中普遍存在的数据不足和类别分布不均衡的问题，实现了均衡的分类性能。
在糖尿病足检测和分级的综合任务中，我们提出的模型取得了最佳性能，平均准确率为0.9827，平均敏感度为0.9684，平均特异性为0.9892。
据我们所知，本研究在足底热成像检测和分级方面创造了新的最先进水平，同时首次实现了整体多类别分类和分级解决方案。可靠的自动热成像分级是朝着为糖尿病患者开发智能健康设备迈出的第一步。
