👉 Arthritis, particularly osteoarthritis (OA), involves complex mathematical modeling to understand joint degeneration and pain mechanisms. Researchers use biomechanical models that incorporate factors like joint loading, cartilage degradation rates, and synovial fluid dynamics to simulate how stress and wear affect joint health over time. These models often employ differential equations to describe the progression of cartilage thinning and bone remodeling, as well as statistical methods to analyze patient-specific data from imaging and clinical assessments. Machine learning algorithms are also increasingly used to predict disease progression and personalize treatment plans based on individual patient characteristics, such as age, weight, and activity levels. By integrating these mathematical approaches, scientists can better predict arthritis onset, tailor interventions, and develop more effective therapies to manage symptoms and slow disease progression.
