Osteoarthritis (OA) Is One Of The Most Common Degenerative Joint Diseases, Primarily Affecting The Knee Joint And Leading To Pain, Stiffness, And Reduced Mobility. Early And Accurate Prediction Of OA Progression Is Crucial For Timely Intervention, Improved Patient Outcomes, And Reduced Healthcare Costs. Traditional Diagnostic Methods, Such As Clinical Examination And Radiographic Analysis, Are Often Subjective, Time-consuming, And Limited In Their Ability To Detect Subtle Pathological Changes. With Recent Advancements In Artificial Intelligence, Deep Learning (DL) Techniques Have Emerged As Powerful Tools For Automated Medical Image Analysis. This Study Proposes A Deep Learning–based Framework For The Prediction Of Osteoarthritis In Knee Joints Using Radiographic And MRI Data. Convolutional Neural Networks (CNNs) Are Employed To Automatically Extract Discriminative Features From Knee Images, Enabling Precise Detection And Grading Of OA Severity. The System Is Designed To Assist Clinicians By Providing Objective, Reliable, And Rapid Predictions, Thereby Reducing Diagnostic Errors And Supporting Personalized Treatment Planning. The Proposed Approach Demonstrates The Potential Of Deep Learning In Advancing Computer-aided Diagnosis And Improving The Early Prediction And Management Of Knee Osteoarthritis.
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