AI-Based Medical Data Analysis for Differentiating Inflammatory vs Degenerative Joint Diseases in Elderly Patients

Musculoskeletal disorders are among the most prevalent causes of disability in the elderly. Inflammatory joint diseases, such as rheumatoid arthritis, and degenerative joint diseases, such as osteoarthritis, are both common yet challenging to differentiate, particularly in the early stages. Traditional imaging techniques often lack sensitivity and specificity when interpreted solely by human experts, and diagnostic accuracy is further limited by inter-observer variability. Artificial Intelligence (AI), particularly deep learning-based image analysis, has emerged as a powerful tool in medical diagnostics. Convolutional neural networks (CNNs), a class of deep learning models, have been successfully applied to musculoskeletal imaging. For example, a study published in The Lancet Rheumatology (2020) trained a CNN on thousands of hand and wrist radiographs from patients with rheumatoid arthritis. The model was able to automatically detect and grade bone erosions and joint space narrowing-key radiographic features of rheumatoid arthritis-with diagnostic performance comparable to experienced musculoskeletal radiologists. Importantly, AI was able to identify early erosive changes in small joints, reduce the time required for radiographic scoring in clinical trials, and provide consistent results, thereby reducing inter-observer variability. Building on these advances, the current study aims to explore the application of AI in enhancing diagnostic accuracy for differentiating between inflammatory and degenerative joint diseases in elderly patients. By integrating AI-based imaging analysis with clinical and laboratory data, this research will not only support accurate diagnosis but also provide predictive models for disease course, functional decline, and joint damage progression. The ultimate goal is to enable personalized treatment strategies and improve outcomes for elderly patients with musculoskeletal disorders.