Multiple-Image Deep Learning Analysis for Neuropathy Detection in Corneal Nerve Images

Automated classification of corneal confocal images from healthy subjects and diabetic subjects with neuropathy.

Over the years, in vivo confocal microscopy has established itself as a rapid and noninvasive method for clinical assessment of the cornea. In particular, images of the subbasal nerve plexus are useful to detect pathological conditions. Currently, clinical information is derived through a manual or semiautomated process that traces corneal nerves and achieves their descriptors (eg, density and tortuosity). This is tedious and subjective. To overcome this limitation, a method based on a convolutional neural network (CNN) for the classification of images from healthy subjects and diabetic subjects with neuropathy is proposed. The CNN simultaneously analyzes 3 nonoverlapping images, from the central region of the cornea. The algorithm automatically extracts features, without the need for neither nerve tracing nor parameter extraction nor montage/mosaicking, and provides an overall classification for each image trio.

On a dataset composed by images from 50 healthy subjects and 50 subjects with neuropathy, the algorithm achieves a classification accuracy of 96%. The proposed method improves the results obtained using a traditional method that traces nerves and evaluates their density and tortuosity.

The proposed method provides a completely automated analysis of corneal confocal images. Results demonstrate the potentiality of the CNN in identifying clinically useful features for corneal nerves by analysis of multiple images.