Evaluating the efficacy of deep learning models for knee osteoarthritis prediction based on Kellgren-Lawrence grading system

Abstract

Osteoarthritis of the knee, also known as OA has been determined that osteoarthritis of the knee is the leading cause of activity limitations and the development of disability, particularly in people who are older. The utilisation of artificial intelligence (AI) methodologies grounded in deep learning (DL) has yielded promising outcomes in the realm of radiographic interpretation. The utilisation of deep learning in the healthcare industry has yielded remarkable outcomes and elevated the benchmark for the quality of medical treatment. This study used knee OA as a clinical scenario to compare twelve transfer learning DL models for detecting the grade of KOA from a radiograph, compared their accuracy, and determined the best model for detecting KOA. The models exhibited a range of 30% to 98% in detecting the KOA. It was determined that MobileNet was responsible for the highest level of accuracy, which came in at 98.36%. It has high training and validation accuracy. The maximum loss was observed for EfficientNetB7. DL approaches created by skilled radiologists and orthopaedic specialists could help smaller hospitals learn and make more emergency room. This would be especially helpful in situations when medical personnel may not be available.