Detection of Knee Osteoarthritis using Artificial Intelligence

Abstract

Knee osteoarthritis (KOA) is a common degenerative joint disease that results in disability due to joint dysfunction and pain. Almost one-fifth of early KOA cases are missed during the routine practice resulting in the progression of the disease. This narrative review aimed to explore and analyze various literatures that proposed Convoluted Neural Network (CNN) model in detecting KOA and its severity based on Kellgren Lawrence grading classification. At first, 221 publications were retrieved using the search term “artificial intelligence” and Knee osteoarthritis”. Only studies that used CNN and radiographic images were included in this study in which only 14 studies fitted our inclusion criteria. Each paper was thoroughly investigated for the input data and CNN model adopted as well as the performance and limitation of that study. Lastly, the conclusion was made and discussed using these results. Object detection and Classification models were among the most popular techniques adopted. Our results showed that object detection models were overall superior regarding the accuracy in the detection of KOA and its severity. The application of CNN for the detection of KOA from radiographic images has shown great promise where each technique has its own advantage. In the foreseeable future, the combination of object detection and classification detection may provide excellent potential as a merit tool to help orthopedists and related physicians for the proper diagnosis and treatment of KOA.