Academic Unit of Radiology, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK.
Diabetes Research Department, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK.
Neuroinformatics. 2023 Jan;21(1):35-43. doi: 10.1007/s12021-022-09603-5. Epub 2022 Aug 26.
Functional magnetic resonance imaging (fMRI) has been shown successfully to assess and stratify patients with painful diabetic peripheral neuropathy (pDPN). This supports the idea of using neuroimaging as a mechanism-based technique to individualise therapy for patients with painful DPN. The aim of this study was to use deep learning to predict treatment response in patients with pDPN using resting state functional imaging (rs-fMRI). We divided 43 painful pDPN patients into responders and non-responders to lidocaine treatment (responders n = 29 and non-responders n = 14). We used rs-fMRI to extract functional connectivity features, using group independent component analysis (gICA), and performed automated treatment response deep learning classification with three-dimensional convolutional neural networks (3D-CNN). Using gICA we achieved an area under the receiver operating characteristic curve (AUC) of 96.60% and F1-Score of 95% in a ten-fold cross validation (CV) experiment using our described 3D-CNN algorithm. To our knowledge, this is the first study utilising deep learning methods to classify treatment response in pDPN.
功能磁共振成像（fMRI）已成功用于评估和分层糖尿病周围神经病理性疼痛（pDPN）患者。这支持了使用神经影像学作为一种基于机制的技术来对患有疼痛性 DPN 的患者进行个体化治疗的想法。本研究旨在使用深度学习技术，通过静息态功能磁共振成像（rs-fMRI）预测 pDPN 患者的治疗反应。我们将 43 名患有疼痛性 pDPN 的患者分为利多卡因治疗的反应者和无反应者（反应者 n = 29，无反应者 n = 14）。我们使用 rs-fMRI 提取功能连接特征，使用组独立成分分析（gICA），并使用三维卷积神经网络（3D-CNN）进行自动治疗反应深度学习分类。在使用我们描述的 3D-CNN 算法的十折交叉验证（CV）实验中，gICA 实现了 96.60%的受试者工作特征曲线（AUC）和 95%的 F1 评分。据我们所知，这是首次利用深度学习方法对 pDPN 的治疗反应进行分类的研究。
