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Knee Joint

Knee Joint

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...

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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Related Experiment Video

Updated: Jan 17, 2026

The Monoiodoacetate Model of Osteoarthritis Pain in the Mouse

The Monoiodoacetate Model of Osteoarthritis Pain in the Mouse

Published on: May 16, 2016

Localizing Knee Pain via Explainable Bayesian Generative Models and Counterfactual MRI: Data from the Osteoarthritis

Tzu-Yi Chuang¹, Pin-Hsun Lian^1,2, Yu-Chen Kuo¹

¹Institute of Medical Device and Imaging, National Taiwan University College of Medicine, No. 1, Section 1, Ren'ai Rd, Zhongzheng District, Taipei, 100233, Taiwan.

Journal of Imaging Informatics in Medicine

|September 24, 2025

Summary

This summary is machine-generated.

This study introduces an explainable AI framework to pinpoint osteoarthritis pain sources in knee MRIs. The AI accurately identifies pain-driving lesions, offering better insights than traditional methods for personalized treatment.

Keywords:

Counterfactual imaging analysis Explainable artificial intelligence Magnetic resonance imaging Osteoarthritis

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Assessment of Knee Hyperalgesia in Mice using Pressure Application Measurement

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Related Experiment Videos

Last Updated: Jan 17, 2026

The Monoiodoacetate Model of Osteoarthritis Pain in the Mouse

The Monoiodoacetate Model of Osteoarthritis Pain in the Mouse

Published on: May 16, 2016

Spontaneous and Evoked Measures of Pain in Murine Models of Monoarticular Knee Pain

Spontaneous and Evoked Measures of Pain in Murine Models of Monoarticular Knee Pain

Published on: February 22, 2019

Assessment of Knee Hyperalgesia in Mice using Pressure Application Measurement

Assessment of Knee Hyperalgesia in Mice using Pressure Application Measurement

Published on: June 13, 2025

Area of Science:

Radiology
Artificial Intelligence
Medical Imaging

Background:

Osteoarthritis (OA) pain intensity often lacks correlation with MRI-detected structural abnormalities, limiting traditional lesion assessment's clinical utility.
Current methods for analyzing knee MRIs in OA struggle to precisely link specific structural changes to patient-reported pain.

Purpose of the Study:

To develop and validate a novel explainable artificial intelligence (XAI) framework for localizing pain-driving abnormalities in knee MR images.
To improve the clinical relevance of MRI assessments in osteoarthritis by identifying specific lesion contributions to pain.

Main Methods:

Utilized a Bayesian generative network to synthesize asymptomatic knee MR images from symptomatic ones, creating counterfactual scans.
Integrated a black-box pain classifier with counterfactual synthesis, constrained by multimodal segmentation and uncertainty-aware inference.
Applied Shapley Additive Explanations (SHAP) to quantify the contribution of individual lesions to OA pain.

Main Results:

The XAI framework demonstrated high anatomical specificity in identifying pain-relevant features like patellar effusions and bone marrow lesions.
SHAP-derived lesion scores showed a significantly stronger association with pain compared to raw lesion volumes (Odds Ratio 6.75 vs. 3.73 in patellar regions).
The approach achieved superior lesion-level resolution, highlighting the spatial heterogeneity of OA pain mechanisms compared to conventional methods.

Conclusions:

The developed XAI framework provides interpretable, lesion-specific MRI analyses for osteoarthritis.
This method offers a new direction for understanding OA pain mechanisms and guiding personalized treatment strategies.
The findings support the clinical relevance and enhanced interpretability of AI-driven lesion analysis in musculoskeletal disorders.