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Related Concept Videos

Functional Classification of Joints01:09

Functional Classification of Joints

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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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Structural Classification of Joints01:20

Structural Classification of Joints

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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...
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Knee Joint01:23

Knee Joint

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The knee joint is the most complicated joint in the body. It consists of three articulations– two tibiofemoral and one patellofemoral. As is characteristic of synovial joints, the knee joint has a thin articular capsule that partially surrounds this joint cavity. Additionally, several ligaments, muscles, and cartilaginous structures support the movement of the knee.
A total of seven ligaments support the knee joint. The patellar ligament, which is also attached to the quadriceps femoris...
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Related Experiment Video

Updated: Mar 12, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Knee osteoarthritis classification using Optimized Granger Causality Inspired Graph Neural Network with Accelerated

M Ganesh Kumar1, L Bharathi2, M Senthil3

  • 1Department of Electronics and Communication Engineering, QIS College of Engineering and Technology, Ongole, Andhra Pradesh, India.

The Knee
|March 10, 2026
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Summary

A new method, KOAC-GCIGNN-AcME-SBOA, improves knee osteoarthritis (KOA) classification accuracy and precision. This advanced technique enhances early detection and grading of KOA, leading to better patient outcomes.

Keywords:
Granger Causality Inspired Graph Neural NetworkMulti-Synchro-Squeezing TransformMulti-Window Savitzky-Golay FilterSecretary Bird Optimization Algorithm

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Orthopedics

Background:

  • Knee osteoarthritis (KOA) is a degenerative joint disease causing pain and mobility loss.
  • Cartilage wear leads to bone-on-bone friction and reduced knee function.
  • Accurate KOA classification is crucial for effective treatment strategies.

Purpose of the Study:

  • To propose a novel method for knee osteoarthritis (KOA) classification.
  • To enhance the accuracy and efficiency of KOA detection and severity grading.
  • To introduce an optimized Graph Neural Network model for KOA analysis.

Main Methods:

  • Utilized the Osteoarthritis Initiative (OAI) database for input images.
  • Applied Multi-Window Savitzky-Golay Filter (MWSGF) for image pre-processing.
  • Employed Feature Affine Residual Network (FA-ResNet) for feature extraction.
  • Implemented Granger Causality Inspired Graph Neural Network with Accelerated Model-agnostic Explanations (GCIGNN-AcME) for classification.
  • Optimized GCIGNN-AcME using Secretary Bird Optimization Algorithm (SBOA).

Main Results:

  • The KOAC-GCIGNN-AcME-SBOA model demonstrated significant improvements in accuracy, precision, and sensitivity.
  • Achieved higher performance metrics compared to existing methods like FAFT-CNN-KOD, KAC-DNN-ORM, and KOD-CNN-XR.
  • The method successfully classified KOA into five grades: Grade 0 (healthy) to Grade 4 (severe).

Conclusions:

  • The developed KOAC-GCIGNN-AcME-SBOA method is effective for KOA classification.
  • The proposed approach offers a promising advancement in diagnosing and grading knee osteoarthritis.
  • Successful implementation highlights the potential of AI in orthopedic diagnostics.