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Local Anesthetics: Differential Sensitivity of Nerve Fibers01:24

Local Anesthetics: Differential Sensitivity of Nerve Fibers

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Related Experiment Video

Updated: Jun 20, 2026

Three-dimensional Imaging of Nociceptive Intraepidermal Nerve Fibers in Human Skin Biopsies
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The AI Innerve Algorithm: Automatic Reproducible Reading for Intraepidermal Nerve Fiber Density Analysis, a New Tool

C Labeyrie1,2, A Lorenzo de Brionne3, O Trassard4

  • 1Neurology Department, French Reference Center for Rare Peripheral Neuropathies, Bicetre Hospital, APHP, Le Kremlin Bicêtre, France.

European Journal of Neurology
|April 8, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) tool Innerve enhances reliability for measuring intraepidermal nerve fiber density (IENFD) in skin biopsies. This AI-assisted reading improves diagnostic efficiency for small-fiber neuropathy.

Keywords:
artificial intelligenceneuropathic painneuropathologyskin biopsysmall‐fiber neuropathy

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Area of Science:

  • Neurology
  • Pathology
  • Biotechnology

Background:

  • Intraepidermal nerve fiber density (IENFD) assessment in skin biopsies is crucial for diagnosing small-fiber neuropathy.
  • Traditional visual assessment of IENFD can be time-consuming and subject to observer variability.

Purpose of the Study:

  • To evaluate the reliability and reading times of the Innerve artificial intelligence (AI) algorithm for IENFD analysis compared to traditional visual assessment.
  • To measure both intra-observer and inter-observer correlations for AI-assisted and visual readings.

Main Methods:

  • Retrospective analysis of 40 skin biopsy slides with PGP9.5/col4 double-immunofluorescence labeling.
  • Slides were scanned and read by two observers using visual assessment (VR1, VR2) and AI-assisted reading (AR) with the Innerve algorithm.
  • Intraclass correlation coefficients (ICC) and reading times were calculated to assess reliability and efficiency.

Main Results:

  • High intra-observer reliability was observed for both visual reading (ICC 0.95-0.99) and AI-assisted reading (ICC 0.96-0.99).
  • Inter-observer reliability was also high, with ICC values of 0.88 (VR1), 0.87 (VR2), and 0.93 (AR).
  • AI-assisted reading was faster for one observer (1.96x) and slightly slower for another (1.03x), indicating potential efficiency gains.

Conclusions:

  • The Innerve AI tool demonstrates high reliability and is a valuable aid for IENFD determination in skin biopsies.
  • AI-assisted analysis simplifies and potentially speeds up the diagnostic process for small-fiber neuropathy.
  • Innerve offers an innovative and reliable solution for a previously challenging diagnostic task.