Automated program using convolutional neural networks for objective and reproducible selection of corneal confocal microscopy images

Abstract

Objective: Diabetic peripheral neuropathy (DPN) is a common complication of diabetes, posing a significant risk for foot ulcers and amputation. Corneal confocal microscopy (CCM) is a rapid, noninvasive method to assess DPN by analysing corneal nerve fibre morphology. However, selecting high-quality representative images remains a critical challenge.

Methods: In this study, we propose a fully automated CCM image-selection algorithm based on deep learning feature extraction using ResNet-18 and unsupervised clustering. The algorithm consistently identifies representative images by balancing non-redundancy and representativeness, ensuring objectivity and reproducibility.

Results: When validated against manual selection by researchers with varying expertise levels, the algorithm demonstrated superior performance in distinguishing DPN and reduced inter-observer variability. It completed the analysis of hundreds of images within 1 s, significantly enhancing diagnostic efficiency. Compared with traditional manual selection, the proposed method achieved higher diagnostic accuracy for key morphological parameters, including corneal nerve fibre density, length, and branch density.

Conclusion: The algorithm is open source and compatible with standard CCM workflows, offering researchers and clinicians a robust and efficient tool for DPN diagnosis. Further, multicentre studies are needed to validate these findings in diverse populations.

Keywords: Deep learning; confocal microscopy; diabetic neuropathy.

Figure 1.

The overall design process and graphic summary of this study.

Figure 2.

Example of CCM image quality classification. Image A is considered a high-quality image, while the remaining images, B, C, and D, are regarded as low-quality. (A) The nerve fibres are clear, with a distinct contrast against the background. (B) The centre of the image shows an indentation caused by excessive contact of the lens with the cornea, leading to discontinuity of the nerve fibres. (C) The rapid movement of the eyeball causes distortion in the image, preventing an accurate representation of the nerve fibre morphology. (D) The majority of the image is focused on the corneal endothelium, failing to display the nerve plexus beneath the base. CCM: corneal confocal microscopy.

Figure 3.

ROC curve analysis of diagnostic performance for DPN using different feature fusion weights. DPN: diabetic peripheral neuropathy; ROC: receiver operating characteristic.

Figure 4.

Diagnostic performance of automated selecting using AiCCM for DPN. DPN: diabetic peripheral neuropathy; AiCCM: AiCCMetrics.

Figure 5.

Diagnostic performance of automated selecting using ACCM for DPN. DPN: diabetic peripheral neuropathy; ACCM: ACCMetrics.

Figure 6.

Comparison of classification performance between automated and manual selecting for DPN diagnosis. DPN: diabetic peripheral neuropathy.