Using Deep Learning Models to Predict the Risk of Peripheral Neuropathy on Diabetic Patients

Abstract

To establish a suitable deep learning model in order to predict the risk of diabetic Peripheral Neuropathy (DPN) by using fundus photography images of type II diabetic patients with artificial intelligence (AI) approach. From the year 2013 to 2017, by using a diabetes care database established by the Department of Endocrinology and Metabolism of Kaohsiung Datong Hospital (KMTTH) and the Affiliated Hospital of Kaohsiung Medical University (KMUH), a corresponding retrospective study of fundus photographic images of patients with type II diabetes mellitus was performed. Patients with Type II diabetes who have undergone clinical routine treatment and have routine ophthalmoscopy were analyzed and classified according to the results of the Nerve Conduction Velocity (NCV) method. Soon after the patient's personal information was removed, the image is preprocessed by adaptive histogram equalization (CLAHE) to limit the contrast variation. These preprocessed images were then divided into training, validation, and test sets. Another two sets of Rotated image data were also incorporated for enhancements to build prediction models through four deep learning architectures: InceptionNet, VGGNet, ResNet, and ConvMixer DPN model, respectively. In this study, a classification model for predicting the severity of DPN was successfully established through four deep learning architectures. The accuracies of the four DPN prediction models established in this study were 0.94, 0.90, 0.97, and 0.96; AUC values have achieved at 0.92, 0.93, 0.95, and 0.96; specificity analyses were 1.00, 0.92, 1.00, and 0.98; The combined sensitivity values of mild and moderate to severe DPN reached 0.84, 0.90, 0.90, and 0.92, respectively. An AI-assisted diagnostic model was successfully established to predict the severity of diabetic peripheral neuropathy (DPN), which could determine whether the patient has DPN from the retinal fundus images obtained after ophthalmoscopy and with its associated severity; therefore, it is an efficient, non-invasive method of DPN detection.