Advanced Classification of Knee Osteoarthritis Using Artificial Intelligence Technologies
Abstract
Introduction: Knee osteoarthritis is a prevalent and debilitating musculoskeletal condition, particularly in the elderly. Early detection and accurate classification are crucial for improving patient outcomes. Objective: To investigate the application of artificial intelligence (AI) and computer vision for the automated detection and classification of knee osteoarthritis based on the Kellgren-Lawrence (KL) scale. Additionally, to develop and evaluate an automated system capable of accurately classifying the severity of the disease. Materials and Methods: A public dataset of radiographic knee images pre-classified according to the KL scale was used. The images were processed with LandingLens software, using the ConvNext architecture, a convolutional neural network. The model was trained with 995 images and was used to evaluate 240 trial images. Results: The model achieved an overall accuracy of 92.55% in classifying knee osteoarthritis according to the KL scale, with a sensitivity of 93.33%. Per-class accuracy was as follows: 97.87% for grade 0, 79.74% for grade 1, 88.68% for grade 2, 94.04% for grade 3, and 99.42% for grade 4. Conclusions: This study confirms the efficacy of AI and computer vision technologies in the automated detection of knee osteoarthritis. Integrating these technologies into clinical practice can enhance the efficiency and consistency of patient evaluations, ultimately leading to improved clinical outcomes and more personalized care.Downloads
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