Deep Learning Classification of Treatment Response in Diabetic Painful Neuropathy: A Combined Machine Learning and Magnetic Resonance Neuroimaging Methodological Study

Abstract

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.

Artificial intelligence in endocrinology: a comprehensive review

“…We conducted a meticulous examination of the remaining 36 full-text articles. Ultimately, 17 articles [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] met the predefined inclusion criteria. The flowchart of the included studies is presented in Fig.…”

“…ML and DL principles can be applied to predict treatment responses among patients affected by the same pathology. Teh et al introduced a novel approach employing DL to predict the treatment response in individuals suffering from painful diabetic peripheral neuropathy (pDPN) [ 22 ]. They used resting-state functional magnetic resonance imaging (rs-fMRI) to extract functional connectivity features by means of group independent component analysis (gICA).…”