In silico workflow for validation of patient-specific 3D-printed casts in forearm fracture immobilization

This paper aims to establish an in silico workflow for biomechanical assessment of patient-specific 3D-printed forearm casts using finite element analysis, and to apply this workflow to compare a novel computationally designed point-of-care cast with a commercially available state-of-the-art cast.

A finite element model of a human forearm was generated from CT data, incorporating bones, soft tissues, and a simulated distal radius and ulna fracture. Two cast designs were virtually applied: a commercial cast (AA_CAST) and a point-of-care cast (POC_CAST) featuring a hexagonal lattice. The POC_CAST was simulated with three 3D-printing materials: Acrylonitrile Butadiene Styrene, Resin, and Polyamide. Six physiological loading conditions were assessed with the proximal end constrained: flexion, extension, radial and ulnar deviation (400 N load), and pronation and supination (1 Nm moment). Maximum von Mises stress in the casts and at the fracture surfaces, as well as maximum fracture displacement, were evaluated.

The AA_CAST model demonstrated superior fracture stabilization, showing consistently lower maximum von Mises stresses and displacements across all loading conditions. The POC_CAST exhibited its highest internal stresses during extension (36-37 MPa) and the largest fracture displacements during radial deviation (0.28-0.36 mm). In the POC_CAST simulations, printing material influenced fracture displacement but had negligible effect on maximum cast stress. All simulated configurations maintained fracture displacement below 0.4 mm, indicating adequate immobilization performance.

The in silico workflow proved effective for biomechanical evaluation of patient-specific 3D-printed casts and enabled direct comparison between a novel POC design and a commercial standard. While the commercial cast provided superior stabilization in simulation, the POC_CAST also demonstrated mechanically sound performance. These findings support the workflow as a robust tool for preclinical assessment, iterative design, and material selection for orthopedic devices.