ULTRASOUND-BASED DEEP LEARNING SYSTEM FOR EARLY DETECTION AND RISK PREDICTION OF KNEE OSTEOARTHRITIS

Abstract

Knee Osteoarthritis (OA) is a joint disease that makes movement difficult and reduces quality of life, especially for older people. It is caused by damage to cartilage, narrowing of joint spaces, formation of bone spurs, and stiffness in the joints. Current methods to diagnose OA include X-rays, which can be inaccurate and subjective, and MRI, which is very accurate but expensive and not widely available. To solve these problems, we propose an automated system using machine learning and image processing with ultrasound images. Ultrasound is a low-cost and non-invasive option that helps detect key OA markers like cartilage damage, joint space narrowing, and bone spurs. This system can provide early and accurate diagnosis, and personalized treatment, and reduce the workload of healthcare professionals, ultimately improving patient care and making diagnosis more consistent and efficient.