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BRIEF RESEARCH REPORT article

Front. Imaging
Sec. Imaging Applications
Volume 4 - 2025 | doi: 10.3389/fimag.2025.1542128
This article is part of the Research Topic Deep Learning for Medical Imaging Applications View all 8 articles

Vision Transformers for Automated Detection of Diabetic Peripheral Neuropathy in Corneal Confocal Microscopy Images

Provisionally accepted

The final, formatted version of the article will be published soon.

    Early detection and management of diabetic peripheral neuropathy (DPN) are critical to reducing associated morbidity and mortality. Corneal Confocal Microscopy (CCM) facilitates the imaging of corneal nerves to detect early and progressive nerve damage in DPN. However, its wider adoption has been limited by the subjectivity and time-intensive nature of manual nerve fiber quantification.This study investigates the diagnostic utility of state-of-the-art Vision Transformer (ViT) models for the binary classification of CCM images to distinguish between healthy controls and individuals with DPN. The ViT model's performance was also compared to ResNet50, a convolutional neural network (CNN) previously applied for DPN detection using CCM images. Using a dataset of approximately 700 CCM images, the ViT model achieved an AUC of 0.99, a sensitivity of 98%, a specificity of 92%, and an F1-score of 95%, outperforming previously reported methods.These findings highlight the potential of the ViT model as a reliable tool for CCM-based DPN diagnosis, eliminating the need for time-consuming manual image segmentation. Moreover, the results reinforce CCM's value as a non-invasive and precise imaging modality for detecting nerve damage, particularly in neuropathy-related conditions such as DPN.

    Keywords: artificial intelligence, Diabetic neuropathy, Corneal confocal microscopy, image classification, Disease diagnosis

    Received: 09 Dec 2024; Accepted: 08 Jan 2025.

    Copyright: © 2025 Ben Rabah, Petropoulos, Malik and Serag. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Chaima Ben Rabah, Weill Cornell Medicine- Qatar, Ar-Rayyan, Qatar

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