Artificial intelligence–assisted radiographic assessment of knee osteoarthritis

Abstract

Knee osteoarthritis (KOA) is one of the most prevalent musculoskeletal disorders and a major cause of pain, reduced mobility, and disability among adults worldwide. Conventional radiography remains the primary imaging modality for diagnosing and grading KOA; however, radiographic interpretation is subject to interobserver variability and may be influenced by the experience of the radiologist. Recent advances in artificial intelligence (AI), particularly deep learning algorithms, have demonstrated promising results in the automated detection and grading of osteoarthritic changes on radiographs. The integration of AI into radiological practice may improve diagnostic consistency, reduce reporting time, and facilitate early disease detection. Therefore, evaluating the effectiveness of AI-assisted radiographic assessment of knee osteoarthritis is of significant clinical and scientific interest.