Deep learning-based automated tool for diagnosing diabetic peripheral neuropathy.

Background: Diabetic peripheral neuropathy (DPN) is a common complication of diabetes, and its early identification is crucial for improving patient outcomes. Corneal confocal microscopy (CCM) can non-invasively detect changes in corneal nerve fibers (CNFs), making it a potential tool for the early diagnosis of DPN. However, the existing CNF analysis methods have certain limitations, highlighting the need to develop a reliable automated analysis tool.

Methods: This study is based on data from two independent clinical centers. Various popular deep learning (DL) models have been trained and evaluated for their performance in CCM image segmentation using DL-based image segmentation techniques. Subsequently, an image processing algorithm was designed to automatically extract and quantify various morphological parameters of CNFs. To validate the effectiveness of this tool, it was compared with manually annotated datasets and ACCMetrics, and the consistency of the results was assessed using Bland--Altman analysis and intraclass correlation coefficient (ICC).

Results: The U2Net model performed the best in the CCM image segmentation task, achieving a mean Intersection over Union (mIoU) of 0.8115. The automated analysis tool based on U2Net demonstrated a significantly higher consistency with the manually annotated results in the quantitative analysis of various CNF morphological parameters than the previously popular automated tool ACCMetrics. The area under the curve for classifying DPN using the CNF morphology parameters calculated by this tool reached 0.75.

Conclusions: The DL-based automated tool developed in this study can effectively segment and quantify the CNF parameters in CCM images. This tool has the potential to be used for the early diagnosis of DPN, and further research will help validate its practical application value in clinical settings.