Knee Osteoarthritis Detection Using Machine Learning on Pre-Processed X-ray Images and Treatment Recommendation

Osteoarthritis (OA) is a degenerative joint disease marked by the gradual breakdown of cartilage and changes in the underlying bone. The condition is most prevalent in older adults and is often exacerbated by joint injuries and mechanical stress. Knee osteoarthritis (KOA) is one of the common disability adjusted life years. There is no cure for the disease, but a combination of lifestyle changes, medications, physical therapies, and sometimes knee transplants can help manage the condition. Despite being time-consuming and highly prone to user variation, manual segmentation, diagnosis, annotation of knee joints, and personalized treatment recommendations remain common procedures for diagnosing osteoarthritis in clinical practice. This research addresses these limitations and reduces diagnostic errors and time and enhance the efficiency and accuracy of knee osteoarthritis diagnosis. The proposed KOA detection model utilizes a deep learning-based approach with Convolutional Neural Networks (CNNs) to identified knee joints in x-rays, Binary classification to determine the presence of disease, a pre trained network(VGG16) and custom CNN used to classify the Kellgren-Lawrence (KL) grades, and random forest model recommending and validating treatment options based on severity. The proposed model has shown promising results by obtaining 98% overall accuracy.