การศึกษาอิทธิพลของขนาดและทิศทางของแรงต่อการแตกหักที่ปลายกระดูกแขนท่อนล่างด้วยวิธีไฟไนต์เอลิเมนต์

Abstract

Distal forearm fractures are common injuries caused by falls on an outstretched hand (FOOSH), in which loading magnitude and direction affect strain distribution and fracture risk. This study investigated the effects of loading magnitude and direction on distal forearm fractures using Finite Element Analysis (FEA) combined with Machine Learning. A three-dimensional model of the distal forearm was developed from CT scan images, including the radius, ulna, carpal bones, interosseous membrane, articular cartilage, and wrist ligaments. Heterogeneous material properties were assigned based on Hounsfield Unit (HU) values and Young’s modulus relationships. Different FOOSH loading conditions were simulated to evaluate principal strain distribution within the distal forearm. The results showed that increasing loading magnitude increased tensile and compressive strain values, while loading direction and wrist posture significantly influenced strain distribution. Extension combined with radial deviation and ulnar deviation produced higher strain concentrations than other loading conditions. Variations in Young’s modulus also affected fracture risk. In addition, the CatBoost regression model demonstrated acceptable performance in predicting strain distribution, particularly for compressive strain, and generated contour patterns similar to FEA results. These findings demonstrate the potential of Machine Learning to reduce computational time and support biomechanical analysis in the future.