Identification of Severe Grading in Knee OsteoArthritis from MRI using Ensemble Deep Learning

Jaber Alyami

doi:10.2174/0115734056289963240318040945

Identification of Severe Grading in Knee OsteoArthritis from MRI using Ensemble Deep Learning

Curr Med Imaging. 2024 Mar 27. doi: 10.2174/0115734056289963240318040945. Online ahead of print.

Author

Jaber Alyami^{1

2

3}

Affiliations

¹ Department of Radiologic Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
² Animal House Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
³ Smart Medical Imaging Research Group, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.

PMID: 38584509
DOI: 10.2174/0115734056289963240318040945

Abstract

Background: Accurate finding of Knee Osteoarthritis (KOA) from structural Magnetic Resonance Imaging (MRI) is a difficult task and is greatly subject to user variation. Furthermore, the identification of knee osteoarthritis (KOA) from MRI scans presents a challenge due to the limited information available. A novel methodology using an ensemble Deep Learning algorithm, combining EfficientNet-B3 and ResNext-101 architectures, aims to forecast KOA advancement, bridging the identified gap in clinical trials.

Objectives: The study aims to develop a precise predictive model for knee osteoarthritis using advanced deep-learning architectures and structural MRI scan data. By utilizing an ensemble technique, the model's accuracy in predicting disease development is enhanced, surpassing the limitations of traditional biomarkers.

Methods: The study used the Osteoarthritis Initiative dataset to develop an ensemble Deep Learning model that combined EfficientNet-B3 and ResNext-101 architectures. Techniques like cropping, gamma correction, and in-slice rotation were used to expand the dataset and improve the model's generalization capacity.

Results: The Deep Learning model demonstrated 93% validation accuracy on the OAI dataset, accurately capturing subtle patterns of knee osteoarthritis progression. Augmentation approaches enhanced its resilience.

Conclusion: Our ensemble Deep Learning approach, using ResNext-101 and EfficientNet-B3 architectures, accurately predicts knee osteoarthritis courses using structural MRI data, demonstrating the importance of data augmentation for improved predictive tools.

Keywords: DL models; Health risks; Knee osteoarthritis; MRI; Osteoarthritis Initiative (OAI) dataset..