Prediction of Osteoarthritis in the Knee by KOA Diagnosis Using Artificial Intelligence Based Techniques

Abstract

Knee Osteoarthritis is the most common form of knee arthritis. A healthy knee's articular cartilage, a smooth, slick tissue, allows for simple bending and straightening. The ends of the leg bones that make up your knee are covered, safeguarded, and cushioned by this material. Two c-shaped segments of meniscal cartilage between your bones serve as "shock absorbers" to cushion your knee joint. Osteoarthritis develops gradually. The cartilage becomes rough and ragged as it ages. It hurts to move the bones along this exposed area. The most prevalent signs and symptoms of knee osteoarthritis are pain and stiffness. After being inactive for a while or in the morning, symptoms typically worsen. The earlier you begin treatment, the better, as osteoarthritis gets worse with time. Your doctor may prescribe additional tests after reviewing your symptoms and medical history, such as X-rays, which can reveal bone and cartilage damage as well as the existence of bone spurs, and magnetic resonance imaging (MRI) scans. So, the KOA diagnosis will be based on two modalities: X-rays and MRIs. Depending upon the patient’s opinion about their knee problems, the doctor will decide whether to take X-rays or MRIs for KOA diagnosis. 

To avoid such severe knee issues, researchers used many various techniques for KOA diagnosis in the existing system in order to treat it at an early stage of knee problems. In order to do that, we conducted a survey of research articles based on the KOA diagnosis published in the years 2021 and 2022 for this work. From the conducted survey, the methodologies which are AI-based techniques used for KOA diagnosis are discussed and their performance metrics are compared. The current review paper's goal is to do research on the various KOA diagnosis approaches employed in the years 2021 and 2022. As a result of this study, the methodologies which are AI-based techniques used for KOA diagnosis achieves good efficiency in predicting the osteoarthritis in the knee.