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Functional Classification of Joints

Functional Classification of Joints

Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...

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Updated: Oct 7, 2025

Synovial Fluid Analysis to Identify Osteoarthritis

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Published on: October 20, 2022

Rheumatoid Arthritis Synovial Inflammation Quantification Using Computer Vision.

Steven Guan¹, Bella Mehta^2,3, David Slater¹

¹The MITRE Corporation, McLean, Virginia.

ACR Open Rheumatology

|January 11, 2022

Summary

This summary is machine-generated.

Computer vision accurately quantifies nuclei density in rheumatoid arthritis (RA) synovial tissue. This robust measure of inflammation correlates with traditional assessments, offering a new tool for RA research.

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Published on: October 20, 2013

Area of Science:

Rheumatology
Digital Pathology
Computational Biology

Background:

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by synovial inflammation.
Accurate quantification of inflammatory burden in synovial tissue is crucial for understanding RA pathogenesis and progression.
Current methods for assessing synovial inflammation can be subjective or labor-intensive.

Purpose of the Study:

To develop and validate a computer vision-based method for quantifying nuclei density in RA synovial tissue.
To assess the correlation between computer-vision-derived nuclei density and established measures of RA inflammation.

Main Methods:

Computer vision algorithms were adapted to quantify nuclei density in hematoxylin and eosin-stained synovial tissue images.
Algorithm performance was validated by a pathologist, assessing sensitivity and specificity.
Nuclei density was compared with semiquantitative histology scores, gene expression data (RNA sequencing), and clinical markers of disease activity.

Main Results:

The algorithm detected a median of 112,657 nuclei per sample with 97% sensitivity and 100% specificity.
Higher nuclei density correlated significantly with increased lymphocytic inflammation, plasma cells, and lining hyperplasia.
Nuclei density showed significant correlations with 915 differentially expressed genes and elevated clinical markers (CRP, ESR, RF, CCP antibody).

Conclusions:

Nuclei density, quantified by computer vision, is a reliable and objective measure of inflammatory burden in RA.
This automated approach provides a robust, scalable method for assessing synovial inflammation in RA.
Nuclei density measurement offers a valuable tool that integrates histological, molecular, and clinical data in RA research.