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Related Concept Videos

Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
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Related Experiment Video

Updated: May 21, 2026

In Vivo Electrophysiological Measurement of Compound Muscle Action Potential from the Forelimbs in Mouse Models of Motor Neuron Degeneration
06:35

In Vivo Electrophysiological Measurement of Compound Muscle Action Potential from the Forelimbs in Mouse Models of Motor Neuron Degeneration

Published on: June 15, 2018

Deep Learning outperforms physicians in myopathy and neuropathy classification based on Needle Electromyography

Ilhan Yoo1, Jaesung Yoo2, Dongmin Kim3

  • 1Department of Neurology, Nowon Eulji Medical Center, Eulji University School of Medicine, Nowon-gu, Seoul, Republic of Korea.

Plos One
|May 19, 2026
PubMed
Summary

A deep learning model accurately aids in diagnosing neuromuscular diseases using needle electromyography (nEMG) signals. This AI system surpasses physician accuracy, offering a practical tool for faster and more precise patient classification.

Related Experiment Videos

Last Updated: May 21, 2026

In Vivo Electrophysiological Measurement of Compound Muscle Action Potential from the Forelimbs in Mouse Models of Motor Neuron Degeneration
06:35

In Vivo Electrophysiological Measurement of Compound Muscle Action Potential from the Forelimbs in Mouse Models of Motor Neuron Degeneration

Published on: June 15, 2018

Area of Science:

  • Neurology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Needle electromyography (nEMG) is crucial for diagnosing neuromuscular disorders.
  • Current nEMG analysis is labor-intensive and susceptible to human bias, potentially leading to diagnostic inaccuracies.

Purpose of the Study:

  • To validate a deep learning (DL) system for classifying nEMG signals into normal, myopathy, and neuropathy categories.
  • To compare the diagnostic performance of the DL system against experienced electromyographers.

Main Methods:

  • A DL model was trained and validated on 376 nEMG signals from 57 patients using nested k-fold cross-validation.
  • The DL model utilized minimal preprocessing for classifying patients.

Main Results:

  • The DL model achieved higher median classification performance (accuracy 0.70, precision 0.70, sensitivity 0.70, specificity 0.85) compared to six electromyographers (accuracy 0.55, precision 0.60, sensitivity 0.54, specificity 0.78).
  • Model interpretability confirmed classification based on relevant signal features.
  • Despite higher overall accuracy, the DL model had more unanimously misclassified cases than physicians.

Conclusions:

  • Deep learning presents a fast, accurate, and practical approach to assist physicians in diagnosing neuromuscular diseases via nEMG analysis.
  • The validated DL system shows potential to improve diagnostic efficiency and accuracy in clinical practice.