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Local Anesthetics: Differential Sensitivity of Nerve Fibers

Local Anesthetics: Differential Sensitivity of Nerve Fibers

Local anesthetics (LAs) block the sodium channels of nerve trunks, sensory nerve endings, and neuromuscular junctions. Although LAs can block all kinds of nerves, the sensitivity of nerve fibers differs according to nerve types and structures. LAs are known to block myelinated fibers faster than unmyelinated ones. Also, they block pain or sensory neurons at low concentrations without affecting the motor neurons involved in muscle contractions. This helps relieve labor pain without affecting the...

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Related Experiment Video

Updated: Jan 10, 2026

Corneal Confocal Microscopy: A Novel Non-invasive Technique to Quantify Small Fibre Pathology in Peripheral Neuropathies

Corneal Confocal Microscopy: A Novel Non-invasive Technique to Quantify Small Fibre Pathology in Peripheral Neuropathies

Published on: January 3, 2011

Deep Learning-Assisted Differentiation of Four Peripheral Neuropathies Using Corneal Confocal Microscopy.

Chaima Ben Rabah¹, Ioannis N Petropoulos², Mark Stettner³

¹AI Innovation Lab, Weill Cornell Medicine, Doha, Qatar.

Annals of Clinical and Translational Neurology

|November 22, 2025

Summary

This summary is machine-generated.

NeuropathAI, an AI system, rapidly diagnoses four common peripheral neuropathies using corneal images. This interpretable deep learning model aids in early detection and differentiation of conditions like diabetic peripheral neuropathy (DPN).

Keywords:

artificial intelligence corneal confocal microscopy corneal nerve disease diagnosis peripheral neuropathy

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Three-dimensional Imaging and Analysis of Mitochondria within Human Intraepidermal Nerve Fibers

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Corneal Confocal Microscopy: A Novel Non-invasive Technique to Quantify Small Fibre Pathology in Peripheral Neuropathies

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Published on: January 3, 2011

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Three-dimensional Imaging and Analysis of Mitochondria within Human Intraepidermal Nerve Fibers

Three-dimensional Imaging and Analysis of Mitochondria within Human Intraepidermal Nerve Fibers

Published on: September 29, 2017

Area of Science:

Ophthalmology
Neurology
Artificial Intelligence

Background:

Peripheral neuropathies cause significant patient disability.
Current diagnostic methods are limited, leading to late or missed diagnoses.
Specialized testing facilities are often inaccessible.

Purpose of the Study:

To develop an interpretable deep learning system, NeuropathAI, for rapid, automated diagnosis of peripheral neuropathies.
To differentiate between diabetic peripheral neuropathy (DPN), chemotherapy-induced peripheral neuropathy (CIPN), chronic inflammatory demyelinating polyneuropathy (CIDP), and HIV-associated sensory neuropathy (HIV-SN).

Main Methods:

A deep learning-based multiclass classification system was developed.
The system analyzes corneal nerve images for diagnosis.
Model performance was evaluated using AUC, F1-score, sensitivity, specificity, and precision.

Main Results:

NeuropathAI achieved high accuracy with an overall AUC of 96.75%.
The system demonstrated excellent discrimination for CIDP (97% AUC), CIPN (93.1% AUC), HIV-SN (99.7% AUC), and DPN (96.9% AUC).
Heatmap visualizations confirmed model interpretability by localizing to areas of nerve fiber loss.

Conclusions:

NeuropathAI enables rapid and accurate diagnosis of four prevalent peripheral neuropathies.
AI-driven corneal image analysis shows significant potential for diagnosing and differentiating peripheral neuropathies.
This technology addresses a critical clinical gap in diagnosing peripheral neuropathies.