ArticleEuropean journal of orthopaedic surgery & traumatology : orthopedie traumatologie2024
Advances in Artificial Intelligence for automated knee osteoarthritis classification using the IKDC system.
Article in European journal of orthopaedic surgery & traumatology : orthopedie traumatologie, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Abstract
introductionKnee osteoarthritis is one of the most prevalent and debilitating musculoskeletal diseases, with a high incidence among the elderly population. Early detection and accurate classification can improve clinical outcomes for affected patients.
objectiveThis study investigates the use of artificial intelligence (AI) and computer vision for automated detection and classification of knee osteoarthritis using the IKDC classification system. The aim was to develop an automated system for this purpose and evaluate its accuracy in classifying disease severity. MATERIALS AND
methodsA public dataset containing radiographic knee images with varying degrees of osteoarthritis, previously classified according to the IKDC scale, was utilized. Images were processed using LandingLens software, an advanced computer vision platform facilitating AI model development and implementation. A machine learning model based on the ConvNext architecture-a convolutional neural network-was trained on 1901 images and evaluated using 380 test images.
resultsThe model demonstrated an overall accuracy of 95.16% in classifying knee osteoarthritis according to the IKDC scale, with a sensitivity of 95.11%. Class-specific accuracies were 92.40% for class A, 93.20% for class B, 98.45% for class C, and 95.69% for class D. These results highlight the model's capability to distinguish between different severity grades of osteoarthritis with high accuracy.
conclusionThis study underscores the efficacy of AI and computer vision in automating knee osteoarthritis detection, providing a precise and reliable tool for physicians in disease diagnosis. Integrating these technologies into clinical practice has the potential to enhance efficiency and consistency in patient evaluation, potentially leading to improved clinical outcomes and more personalized medical care. LEVEL OF EVIDENCE: Level III.
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