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

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.