MRI detection and grading of knee osteoarthritis – a pilot study using an AI technique with a novel imaging-based scoring system

Abstract

Precise and rapid identification of knee osteoarthritis (OA) is essential for efficient management and therapy planning. Conventional diagnostic techniques frequently depend on subjective interpretation, which have shortcomings, particularly during the first phases of the illness. In this study, magnetic resonance imaging (MRI) was used to create knee datasets as novel techniques for evaluating knee OA. This methodology utilizes artificial intelligence (AI) algorithms to identify and evaluate important indications of knee osteoarthritis, including osteophytes, eburnation, bone marrow lesions (BMLs), and cartilage thickness. We conducted training and evaluation on multiple deep learning models, including ResNet50, DenseNet121, VGG16 and ResNet101 utilizing annotated MRI data. By conducting thorough statistical analysis and validation, we have proven the efficacy of our models in precisely diagnosing and grading knee OA. This research presents a new grading method, verified by experienced radiologists, that uses eburnation as a significant indicator of the severity of knee OA. This study provides a new method for an AI-powered automated system designed to diagnose knee OA. This system will simplify the diagnostic process, minimize mistakes made by humans, and enhance the effectiveness of clinical treatment. Through the integration of AI-ML (machine learning) technologies, our goal is to improve patient outcomes, optimize the utilization of healthcare resources, and enable personalized knee OA therapy.