The human forearm, a dynamically coordinated system of bones, muscles, and connective tissues, is central to upper limb function but poses significant challenges for computational modeling due to its anatomical complexity. Existing finite element (FE) models of the forearm often oversimplify soft tissues or lack rigorous validation, limiting their clinical utility. This study aimed to develop and validate a high-fidelity 3D FE model of the forearm musculoskeletal system to replicate in vivo stress distribution under axial loading. The model was constructed using CT and MRI data from a fresh-frozen cadaveric specimen (MorphoSource) and validated with embalmed specimen, combined with anatomical references. Bone, muscle, ligaments, tendons, and cartilage geometries were segmented and optimized using Mimics 19.0, Geomagic Wrap 2017, and SolidWorks 2021. Material properties (isotropic, linear elastic) were assigned based on literature, and mesh convergence was validated. Mechanical validation included mesh convergence testing, geometric comparison with cadaveric measurements and literature data, and axial compression tests (100 N) to simulate physiological loading. A 3D FE model with 672,411 elements and 1,080,964 nodes was successfully built, reconstructing bones, muscles, tendons, ligaments, and cartilage. Mesh convergence testing showed stress differences < 5%. FE model’s muscle belly length matched cadaveric measurements and literature values. Under 100 N axial load, the model predicted peak contact stresses of 4.8 MPa (radioscaphoid) and 2.75 MPa (radiolunate), with a 22.5% contact area, consistent with cadaveric experiments (5.13 MPa, 23.11%) and prior literature (5.24 MPa, 20.1–20.6%). This high-fidelity FE model accurately replicates forearm anatomy and in vivo stress distribution, validated against cadaveric and literature data. It provides a robust platform for exploring forearm biomechanics and guiding clinical applications (e.g., surgical planning, implant design). Limitations include dynamic material properties and single-specimen validation, which warrant future studies with dynamic parameters and multi-specimen data. This framework advances translational biomechanics by bridging computational precision with anatomical realism.
Shang et al. (Thu,) studied this question.