Deep Learning-Based Analysis of Macaque Corneal Sub-Basal Nerve Fibers in Confocal Microscopy Images

PurposeTo describe and assess different deep learning-based methods for automated measurement of macaque corneal sub-basal nerves using in vivo confocal microscopy (IVCM).MethodsThe automated assessment of corneal nerve fiber length (CNFL) in IVCM images is of increasing clinical interest. These measurements are important biomarkers in a number of diseases including diabetes mellitus, human immunodeficiency virus, Parkinson’s disease and multiple sclerosis. Animal models of these and other diseases play an important role in understanding the disease processes as efforts toward developing new and effective therapeutics are made. And while automated methods exist for nerve fiber analysis in clinical data, differences in anatomy and image quality make the macaque data more challenging and has motivated the work reported here.Toward this goal, nerves in macaque corneal IVCM images were manually labelled using an ImageJ plugin (NeuronJ). Different deep convolutional neural network (CNN) architectures were evaluated for accuracy relative to the ground truth manual tracings. The best performing model was used on separately acquired macaque ICVM images to additionally compare inter-reader variability.ConclusionsDeep learning-based segmentation of sub-basal nerves in IVCM images shows excellent correlation to manual segmentations in macaque data. The technique is indistinguishable across readers and paves the way for more widespread adoption of objective automated analysis of sub-basal nerves in IVCM.Translational RelevanceQuantitative measurements of corneal sub-basal nerves are important biomarkers for disease screening and management. This work reports on different approaches that, in using deep learning-based techniques, leverage state of the art analysis methods to demonstrate performance akin to human graders. In application, the approach is robust, rapid and objective, offering utility to a variety of clinical studies using IVCM.

Intraepidermal Nerve Fiber Analysis in Human Patients and Animal Models of Peripheral Neuropathy: A Comparative Review

Analysis of intraepidermal nerve fibers (IENFs) in skin biopsy samples has become a standard clinical tool for diagnosing peripheral neuropathies in human patients. Compared to sural nerve biopsy, skin biopsy is safer, less invasive, and can be performed repeatedly to facilitate longitudinal assessment. Intraepidermal nerve fiber analysis is also more sensitive than conventional nerve histology or electrophysiological tests for detecting damage to small-diameter sensory nerve fibers. The techniques used for IENF analysis in humans have been adapted for large and small animal models and successfully used in studies of diabetic neuropathy, chemotherapy-induced peripheral neuropathy, HIV-associated sensory neuropathy, among others. Although IENF analysis has yet to become a routine end point in nonclinical safety testing, it has the potential to serve as a highly relevant indicator of sensory nerve fiber status in neurotoxicity studies, as well as development of neuroprotective and neuroregenerative therapies. Recently, there is also interest in the evaluation of IENF via skin biopsy as a biomarker of small fiber neuropathy in the regulatory setting. This article provides an overview of the anatomic and pathophysiologic principles behind IENF analysis, its use as a diagnostic tool in humans, and applications in animal models with focus on comparative methodology and considerations for study design.