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Related Concept Videos

Growth of Cartilage and Bone Tissue01:27

Growth of Cartilage and Bone Tissue

Chondrocytes form a temporary cartilaginous model by dividing and secreting a thick gel-like extracellular matrix. Once the chondrocytes undergo programmed cell death, osteoblasts enter the site of the cartilaginous model. The process of replacing the temporary cartilaginous model with bone in an ordered manner is called endochondral ossification. In endochondral ossification, not all of the cartilage is replaced by bone tissue. Some cartilage that performs a protective and supportive function...
Structural Classification of Joints01:20

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
Classification of Bones01:18

Classification of Bones

The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The long...
Functional Classification of Joints01:09

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 immobile...

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Related Experiment Video

Updated: May 26, 2026

Real-time Visualization and Analysis of Chondrocyte Injury Due to Mechanical Loading in Fully Intact Murine Cartilage Explants
08:42

Real-time Visualization and Analysis of Chondrocyte Injury Due to Mechanical Loading in Fully Intact Murine Cartilage Explants

Published on: January 7, 2019

Multivariate analysis of cartilage degradation using the support vector machine algorithm.

Ping-Chang Lin1, Onyi Irrechukwu, Remy Roque

  • 1Magnetic Resonance Imaging and Spectroscopy Section, National Institute on Aging, National Institutes of Health, Baltimore, Maryland 21224, USA.

Magnetic Resonance in Medicine
|December 20, 2011
PubMed
Summary
This summary is machine-generated.

Support vector machine analysis of MRI parameters improves early osteoarthritis detection in cartilage. Combining T(1), magnetization transfer rate (k(m)), and apparent diffusion coefficient (ADC) offers superior tissue characterization over individual measures.

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Related Experiment Videos

Last Updated: May 26, 2026

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Area of Science:

  • Biomedical Engineering
  • Radiology
  • Biophysics

Background:

  • Osteoarthritis (OA) diagnosis relies on imaging, but early cartilage degradation presents challenges due to overlapping MRI parameter values.
  • Current MRI techniques struggle to differentiate subtle changes in cartilage matrix composition and structure in early OA.
  • Improved methods are needed for accurate characterization of early cartilage degradation to enable timely intervention.

Purpose of the Study:

  • To evaluate the efficacy of multivariate support vector machine (SVM) analysis for enhanced tissue characterization in early OA.
  • To determine if combinations of MRI parameters improve the classification of cartilage degradation compared to individual parameters.
  • To assess the correlation of SVM-derived degradation probabilities with biochemical measurements of cartilage integrity.

Main Methods:

  • Bovine nasal cartilage samples underwent controlled degradation.
  • Multiple MRI parameters were measured, including T(1), T(2), magnetization transfer rate (k(m)), and apparent diffusion coefficient (ADC).
  • Support vector machine (SVM) models were applied to combinations of these parameters for classification and correlation analyses.

Main Results:

  • SVM analysis using specific parameter combinations, notably (T(1), k(m), ADC), demonstrated superior classification performance.
  • The area under the ROC curve for detecting extensive and mild degradation reached 1.00 and 0.94, respectively, with the (T(1), k(m), ADC) set.
  • SVM-derived degradation probabilities showed strong correlations (r(2) = 0.79-0.88) with biochemical tissue measurements, outperforming individual MRI parameters like T(1) (r(2) = 0.53-0.64).

Conclusions:

  • Multivariate SVM analysis significantly enhances the characterization of early cartilage degradation in MRI studies.
  • The parameter combinations (T(1), k(m)) and (T(1), k(m), ADC) show promise for accurate early OA detection.
  • This approach offers improved diagnostic capabilities for early-stage osteoarthritis by providing more robust tissue characterization.