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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
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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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Related Experiment Video

Updated: May 4, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Unsupervised Machine Learning Drives Dynamic Alignment Classification in Navigated Total Knee Arthroplasty.

Alexa K Pius¹, Prudhvi Tej Chinimilli², Laurent D Angibaud²

¹Department of Orthopaedic Surgery, Stanford University School of Medicine, Redwood City, California.

The Journal of Arthroplasty

|May 2, 2026

Summary

This summary is machine-generated.

This study introduces a novel machine learning method to classify dynamic hip-knee angle (dHKA) during total knee arthroplasty (TKA). Most patients maintained their alignment profile post-surgery, correlating with better outcomes.

Keywords:

K-means clustering computer-assisted orthopaedic surgery (CAOS)dynamic hip knee angle (dHKA) angle total knee arthroplasty (TKA)unsupervised machine learning

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Individualized Stem-positioning in Calcar-guided Short-stem Total Hip Arthroplasty

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Published on: February 27, 2018

Area of Science:

Orthopaedic Surgery
Biomedical Engineering
Machine Learning in Healthcare

Background:

Current total knee arthroplasty (TKA) classifications are static and do not reflect dynamic limb behavior during gait.
A novel intraoperative method using an intra-articular device and computer-assisted orthopaedic surgery (CAOS) system measures dynamic hip-knee angle (dHKA).

Purpose of the Study:

To develop a machine learning (ML) model for classifying patient-specific dHKA profiles intraoperatively.
To analyze the stability of these dHKA profiles before and after the femoral cut in a tibia-first TKA workflow.
To assess the correlation between dHKA profile preservation and early functional outcomes.

Main Methods:

Reviewed 1,890 tibia-first TKA cases across 11 surgeons, recording dHKA at 12 flexion angles pre- and post-femoral cut.
Utilized a K-means clustering model trained on pre-cut data to identify alignment profiles, then applied it to post-cut data.
Analyzed a subset of 141 cases for the association between cluster preservation and one-year Knee Injury and Osteoarthritis Outcome Score for Joint Replacement (KOOS Jr.) scores.

Main Results:

Identified four distinct dHKA clusters (valgus/neutral, neutral, low-to-moderate varus, moderate-to-high varus).
Overall, 69.4% of cases maintained their pre-cut cluster after the femoral cut, with surgeon variation (61-88%).
In the outcomes subset, 72.3% preserved their cluster, which was associated with improved KOOS Jr. scores, particularly in low-to-moderate varus knees.

Conclusions:

Demonstrated the first unsupervised ML application for classifying intraoperative dHKA profiles using a force-controlled device and CAOS system.
This method provides real-time feedback for surgical alignment.
Establishes a foundation for automated alignment classification guidance in personalized TKA.