Optimizing knee osteoarthritis severity prediction on MRI images using deep stacking ensemble technique.

Area of Science:

• Orthopedics and Medical Imaging
• Artificial Intelligence in Medicine

Background:

• Knee osteoarthritis (KOA) is a prevalent degenerative joint disease in older adults, characterized by cartilage deterioration and symptoms like pain and stiffness.
• Conventional diagnostic methods, including X-ray, MRI, and CT scans, are time-consuming and can be tedious for medical professionals.

Purpose of the Study:

• To investigate the efficacy of deep learning algorithms for predicting the severity of knee osteoarthritis.
• To develop a faster and more efficient diagnostic approach for KOA compared to traditional methods.

Main Methods:

• Utilized four pre-trained deep learning models: CNN, AlexNet, ResNet34, and ResNet-50.
• Implemented a deep stacked ensemble technique to combine the predictions of the individual models for optimal performance.

Main Results:

• The deep stacked ensemble model achieved a high accuracy of 99.71% in predicting KOA severity.
• Deep learning models demonstrated significant potential in automating and expediting the KOA diagnostic process.

Conclusions:

• Deep learning, particularly ensemble methods, offers a highly accurate and efficient approach for assessing knee osteoarthritis severity.
• This AI-driven methodology can significantly aid clinicians in timely diagnosis and management of KOA.