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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
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Classification of Bones01:18

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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.
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The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...
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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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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.
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Related Experiment Video

Updated: Jan 13, 2026

Software-Assisted Quantitative Measurement of Osteoarthritic Subchondral Bone Thickness
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Osteoarthritis Severity Classification in Knee X-Rays Using Optimized Deep Learning Approaches.

Irfan Atik1, Ozlem Polat2, Seda Atik3

  • 1Department of Radiology, Faculty of Medicine, Sivas Cumhuriyet University, Sivas, Turkey. irfanatik_91@hotmail.com.

Journal of Imaging Informatics in Medicine
|January 6, 2026
PubMed
Summary

This study developed a deep learning system using knee X-rays to classify osteoarthritis (OA) severity. The optimized DenseNet169 model accurately distinguishes OA stages, aiding early diagnosis and treatment decisions.

Keywords:
Deep learningKnee X-rayKnee osteoarthritisOptimization

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Orthopedics

Background:

  • Osteoarthritis (OA) is a prevalent degenerative joint disease impacting quality of life and mobility, particularly in older adults.
  • Accurate early classification of OA severity is crucial for effective treatment and disease progression management.
  • Knee joint X-rays are a primary diagnostic tool for OA, necessitating advanced analytical methods.

Purpose of the Study:

  • To introduce a deep learning-based system for classifying osteoarthritis severity using knee joint X-ray images.
  • To evaluate the performance of EfficientNetB1, DenseNet169, and Xception architectures optimized with the Gray Wolf Optimization (GWO) algorithm for OA classification.
  • To enable early and accurate determination of OA severity for informed clinical decision-making.

Main Methods:

  • Utilized three deep learning architectures: EfficientNetB1, DenseNet169, and Xception.
  • Employed the Gray Wolf Optimization (GWO) algorithm to optimize hyperparameters of the fully connected layers.
  • Conducted five-class (asymptomatic to severe OA) and binary (mild vs. severe OA) classification on a dataset of 1000 knee X-ray images.

Main Results:

  • The DenseNet169 model achieved the highest performance, with 74% accuracy in five-class OA classification.
  • In binary classification distinguishing mild from severe OA, the DenseNet169 model reached 93.75% accuracy.
  • Optimized models demonstrated high accuracy and effectiveness in distinguishing OA levels and severity.

Conclusions:

  • Deep learning models, particularly DenseNet169 optimized with GWO, show significant potential for accurate OA severity classification from knee X-rays.
  • The developed system can assist specialists in early OA diagnosis, facilitating timely and appropriate treatment and surgical planning.
  • Accurate classification of moderate and severe OA stages is vital for determining the need for surgical intervention.