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Related Experiment Video

Updated: Jun 13, 2026

Movement Retraining using Real-time Feedback of Performance

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Published on: January 17, 2013

Osteo-NeT: An Automated System for Predicting Knee Osteoarthritis from X-ray Images Using Transfer-Learning-Based

Hassan A Alshamrani¹, Mamoon Rashid^2,3, Sultan S Alshamrani⁴

¹Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran 11001, Saudi Arabia.

Healthcare (Basel, Switzerland)

|May 13, 2023

Summary

Early detection of knee osteoarthritis is crucial for managing the condition. This study introduces advanced machine learning models, achieving over 90% accuracy in predicting knee osteoarthritis from X-rays.

Keywords:

CNN ResNeT-50 VGG-16 X-ray automated system osteoarthritis transfer learning

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Area of Science:

Orthopedics
Radiology
Artificial Intelligence

Background:

Knee osteoarthritis is a prevalent global health issue with no current cure.
Early detection is key to managing osteoarthritis progression.
Manual X-ray interpretation for osteoarthritis is subjective and prone to errors.

Purpose of the Study:

To develop a highly accurate automated system for early knee osteoarthritis detection.
To evaluate the performance of transfer learning models for osteoarthritis prediction.

Main Methods:

Utilized transfer learning with sequential Convolutional Neural Networks (CNNs).
Specifically employed Visual Geometry Group 16 (VGG-16) and Residual Neural Network 50 (ResNet-50) models.
Applied models to knee X-ray images for osteoarthritis prediction.

Main Results:

All tested models demonstrated predictive accuracy exceeding 90%.
The pretrained VGG-16 model achieved the highest performance.
VGG-16 reached 99% training accuracy and 92% testing accuracy.

Conclusions:

Transfer learning models, particularly VGG-16, offer a promising approach for accurate early detection of knee osteoarthritis.
The developed method shows potential for real-world clinical application in osteoarthritis diagnosis.