ARTIFICIAL INTELLIGENCE-DRIVEN BIOMECHANICAL MODELING AND PHYSICS-BASED ANALYSIS FOR EARLY PREDICTION OF SPINE AND JOINT DEGENERATION

Abstract

Degeneration of spine and joints constitutes one of the most prevalent causes of chronic pain, disabilities, and deteriorating quality of life around the world. Traditional methods of diagnosis usually diagnose degeneration only after considerable structural damage has happened, making it difficult for early intervention. The recent developments in artificial intelligence (AI), computational biomechanics, and digital health technologies provide promising capabilities in predicting and managing musculoskeletal diseases at an early stage. In this paper, we explore the use of AI-enabled analytics in combination with physics-based models of biomechanics to predict spine and joint degeneration. We describe various computational methods such as finite element analysis, musculoskeletal simulation, multibody dynamics, and patient-specific biomechanical modeling along with machine learning and deep learning methods. We focus particularly on hybrid AI-biomechanics approaches, physics-informed neural networks, multimodal fusion and digital twins using imaging, biomechanical, clinical and sensor data. Clinical applications such as degeneration prediction, surgery planning, monitoring of rehabilitation and personalized treatments are also explored. Additionally, the current challenges in data availability, model validation, interpretability, computational cost and clinical translation are mentioned. Lastly, the future directions include physics-informed foundation models, real-time digital twins, federated learning and autonomous decision-making systems. Collectively, these advances have the potential to transform musculoskeletal healthcare from reactive treatment toward predictive, preventive, and precision medicine.