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.

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Faculty Opinions recommendation of Intraepidermal nerve fiber analysis in human patients and animal models of peripheral neuropathy: A comparative review.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.736029924.793571737 ◽

2020 ◽

Author(s):

Paul Farquhar-Smith ◽

Richard Gordon-Williams

Keyword(s):

Peripheral Neuropathy ◽

Animal Models ◽

Nerve Fiber ◽

Fiber Analysis ◽

Comparative Review ◽

Nerve Fiber Analysis ◽

Intraepidermal Nerve Fiber

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Advantages of an Automated Method Compared With Manual Methods for the Quantification of Intraepidermal Nerve Fiber in Skin Biopsy

Journal of Neuropathology & Experimental Neurology ◽

10.1093/jnen/nlab045 ◽

2021 ◽

Author(s):

Marta Francisca Corrà ◽

Mafalda Sousa ◽

Inês Reis ◽

Fabiana Tanganelli ◽

Nuno Vila-Chã ◽

...

Keyword(s):

Skin Biopsy ◽

Nerve Fiber ◽

High Reliability ◽

Nerve Fibers ◽

Fiber Density ◽

Automated Method ◽

Manual Counting ◽

Intraepidermal Nerve Fiber Density ◽

Lower Variability ◽

Intraepidermal Nerve Fiber

Abstract Intraepidermal nerve fiber density (IENFD) measurements in skin biopsy are performed manually by 1–3 operators. To improve diagnostic accuracy and applicability in clinical practice, we developed an automated method for fast IENFD determination with low operator-dependency. Sixty skin biopsy specimens were stained with the axonal marker PGP9.5 and imaged using a widefield fluorescence microscope. IENFD was first determined manually by 3 independent observers. Subsequently, images were processed in their Z-max projection and the intradermal line was delineated automatically. IENFD was calculated automatically (fluorescent images automated counting [FIAC]) and compared with manual counting on the same fluorescence images (fluorescent images manual counting [FIMC]), and with classical manual counting (CMC) data. A FIMC showed lower variability among observers compared with CMC (interclass correlation [ICC] = 0.996 vs 0.950). FIMC and FIAC showed high reliability (ICC = 0.999). A moderate-to-high (ICC = 0.705) was observed between CMC and FIAC counting. The algorithm process took on average 15 seconds to perform FIAC counting, compared with 10 minutes for FIMC counting. This automated method rapidly and reliably detects small nerve fibers in skin biopsies with clear advantages over the classical manual technique.

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

10.1101/758433 ◽

2019 ◽

Cited By ~ 2

Author(s):

Jonathan D. Oakley ◽

Daniel B. Russakoff ◽

Megan E. McCarron ◽

Rachel L. Weinberg ◽

Jessica M. Izzi ◽

...

Keyword(s):

Deep Learning ◽

Confocal Microscopy ◽

Nerve Fiber ◽

Ground Truth ◽

Automated Analysis ◽

Nerve Fibers ◽

Fiber Analysis ◽

Quantitative Measurements ◽

Nerve Fiber Analysis

AbstractPurposeTo 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.

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