利用統計形狀建模預測個人化三維膝關節有限元素模型並結合微觀關節軟骨模型進行踩踏力學分析

Abstract

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Osteoarthritis greatly impacts the quality of life for those affected. Diagnosis usually occurs when symptoms become constant and unbearable, leaving only treatment options for alleviation without a complete cure. The sedentary lifestyle prevalent in modern society often leads to prolonged inactivity, resulting in weakened joints and the early onset of osteoarthritis. Conversely, overexertion during exercise can also contribute to injuries and the development of osteoarthritis in young adults. Extensive studies have investigated joint cartilage dynamics during everyday movements, with a focus on the role of collagen content in early detection of osteoarthritis. This project aims to enhance the existing collagen model within cartilage by integrating a water content model, which is a fundamental component of cartilage. By employing finite element analysis (FEA) on the cartilage model, valuable insights into its behavior under mechanical loads can be gained, leading to a deeper understanding of its role in movement. To analyze the movement and the resulting loads on the cartilage, three-dimensional knee joint models have been created from finite element mesh preparation. However, the time-consuming preparation process and significant computational requirements associated with these models pose challenges. To address these challenges and improve efficiency, a statistical shape modeling (SSM) approach has been adopted. This approach establishes a comprehensive database of knee joint models, accessible for subsequent analyses. The successful investigation of the interaction between collagen and water content during mechanical loads has provided valuable insights into model behavior. The SSM process enables easy retrieval of knee joint models from the database, with an approximate root mean squared error (RMSE) of 0.5%. Additionally, the generation of subject-specific models achieves an RMSE of approximately 1.3%. Understanding the interaction between collagen and water content models is crucial for comprehending cartilage functioning under load during movement. Comprehensive joint analyses offer valuable insights into cartilage behavior, facilitating early detection of osteoarthritis. The advantage of the current approach lies in the ease of reproducing these analyses, as the preparation process no longer requires extensive time and computational resources as it did previously.