Intelligent Knee Osteoarthritis Prediction with Optimal Feature Driven Neural Network

Abstract

Knee osteoarthritis (OA) is a common degenerative joint disorder that affects a large portion of the adult
population. Early and accurate prediction of OA progression is essential for enabling timely clinical interventions
and designing personalized treatment strategies. In this study, we present an artificial neural network (ANN)–
based framework for predicting knee OA progression using 3D magnetic resonance imaging (MRI) data. The
process begins with comprehensive preprocessing of MRI scans to ensure consistency and quality, including
resolution standardization, intensity normalization, and alignment of knee joint orientation. Image segmentation
is also employed to isolate the knee region and remove irrelevant anatomical structures, thereby reducing
computational complexity. Following preprocessing, principal component analysis (PCA) is applied to extract a
compact and informative set of features. By identifying principal components that capture the most significant
variations in the dataset, PCA effectively transforms the high-dimensional MRI data into a lower-dimensional
feature space while retaining essential diagnostic information. These reduced PCA features are then used as input
to the ANN classifier, which is trained on labeled MRI samples representing varying levels of OA severity. The
ANN is optimized using an appropriate loss function and iterative weight adjustment to minimize classification
error. Once fully trained, the model can accurately classify new MRI scans into different stages of knee OA
progression, demonstrating its potential as a reliable tool for early diagnosis and clinical decision support