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A Novel Method to Predict Knee Osteoarthritis Progression on MRI Using Machine Learning Methods by Yaodong Du; Rania Almajalid; Juan Shan; Ming Zhang is a Engineering article available to read on EtoBox.

What is A Novel Method to Predict Knee Osteoarthritis Progression on MRI Using Machine Learning Methods about?

This study explored the hidden biomedical information from knee MR images for osteoarthritis (OA) prediction. We have computed the Cartilage Damage Index (CDI) information from 36 informative locations on tibiofemoral cartilage compartment from 3D MR imaging and used PCA analysis to process the feature set. Four machine learning methods (artificial neural network (ANN), support vector machine (SVM), random forest and naïve Bayes) were employed to predict the progression of OA, which was measured by change of Kellgren and Lawrence (KL) grade, Joint Space Narrowing on Medial compartment (JSM) grade and Joint Space Narrowing on Lateral compartment (JSL) grade. To examine the different effect of medial and lateral informative locations, we have divided the 36-dimensional feature set into 18-dimensional medial feature set and 18-dimensional lateral feature set and run the experiment on four classifiers separately. Experiment results showed that the medial feature set generated better prediction performance than the lateral feature set, while using the total 36-dimensional feature set generated the best. PCA analysis is helpful in feature space reduction and performance improvement. For

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Author

Yaodong Du; Rania Almajalid; Juan Shan; Ming Zhang

Published

2018

Language

EN

Field

Engineering (Physical Sciences)