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Classification of Skeletal Muscle Fibers

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.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...

Classification of Skeletal Muscle Relaxants

Classification of Skeletal Muscle Relaxants

Skeletal muscle relaxants are a group of drugs that can reduce muscle stiffness and induce temporary paralysis to relieve pain. These agents can act centrally to reduce muscle tone or spasms in painful conditions such as multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), or spinal injuries; they are called antispasmodics or spasmolytics.
Peripherally acting skeletal muscle relaxants interfere with the neurotransmission at the neuromuscular end plate to induce paralysis during...

Skeletal Muscle Anatomy

Skeletal Muscle Anatomy

Skeletal muscle is the most abundant type of muscle in the body. Tendons are the connective tissue that attaches skeletal muscle to bones. Skeletal muscles pull on tendons, which in turn pull on bones to carry out voluntary movements.

Naming Skeletal Muscles

Naming Skeletal Muscles

The naming of the approximately 700 muscles in the human body is based on a set of criteria designed to provide descriptive information about each muscle, making it easier to identify and remember them.
The key factors used in naming muscles include:

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Related Experiment Video

Updated: Jan 12, 2026

Semi-automated Analysis of Mouse Skeletal Muscle Morphology and Fiber-type Composition

Semi-automated Analysis of Mouse Skeletal Muscle Morphology and Fiber-type Composition

Published on: August 31, 2017

PathViT Model for Automated Disease Classification from Skeletal Muscle Histopathology.

Taymaz Akan¹, Sait Alp², Richa Aishwarya³

¹Department of Medicine, Louisiana State University Health Sciences Center at Shreveport, Shreveport, Louisiana; Department of Software Engineering, Faculty of Engineering, Istanbul Topkapı University, Istanbul, Turkey.

The American Journal of Pathology

|November 8, 2025

Summary

This summary is machine-generated.

PathViT, a deep-learning model, accurately distinguishes healthy from diseased muscle fibers, improving diagnostic speed and consistency for skeletal muscle pathology. This AI tool reduces manual analysis, enhancing biomedical research and clinical applications.

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Semi-automated Analysis of Mouse Skeletal Muscle Morphology and Fiber-type Composition

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Published on: August 31, 2017

A Rapid Automated Protocol for Muscle Fiber Population Analysis in Rat Muscle Cross Sections Using Myosin Heavy Chain Immunohistochemistry

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Immunolabelling Myofiber Degeneration in Muscle Biopsies

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Published on: December 5, 2019

Area of Science:

Biomedical Engineering
Computational Pathology
Digital Health

Background:

Manual analysis of skeletal muscle pathology from histological images is time-consuming and subjective.
Inter- and intra-user variability in manual analysis impacts diagnostic accuracy and consistency.
Current methods require manual cell counting, segmentation, and thresholding, increasing labor intensity.

Purpose of the Study:

To develop an automated deep-learning model, PathViT, for skeletal muscle pathology analysis.
To reduce human intervention, subjectivity, and variability in muscle fiber classification.
To significantly decrease analysis time compared to conventional manual methods.

Main Methods:

Utilized wheat germ agglutinin staining and digital histopathology of skeletal muscle.
Employed a transformer-based deep-learning model (PathViT) for automated classification.
Compared PathViT performance against state-of-the-art deep-learning models.

Main Results:

PathViT achieved 96% accuracy in classifying healthy versus diseased muscle fibers.
The model outperformed other deep-learning models in distinguishing muscle pathologies.
PathViT demonstrated enhanced scalability and decreased variability in analysis.

Conclusions:

PathViT offers a powerful, automated solution for skeletal muscle pathology analysis.
The model improves diagnostic accuracy and consistency, reducing reliance on manual methods.
PathViT has potential as a valuable tool for biomedical research and clinical settings.