Introduction: This study addresses the challenges of anatomical diversity and precision in orthopedic surgery by introducing a novel computational methodology for designing customized osteosynthesis plates. The goal is to improve anatomical fitting and surgical outcomes across different species.
Methods: High-resolution computed tomography (CT) scans were used to generate 3D reconstructions of fractured bones, which were converted into point clouds. The Iterative Closest Point (ICP) algorithm was applied to minimize Euclidean distances between bone and plate models, ensuring optimal alignment. Subsequently, thin-plate spline (TPS) warping was employed to refine the adaptation of plates to complex bone geometries, enhancing biomechanical stability. The methodology was applied to bone scans from camels, dogs, and cats.
Results: The customized plates achieved significantly improved anatomical fitting compared to conventional approaches, with reduced post-process distances and decreased operation times. The improved fitting was strongly correlated with enhanced surgical precision and stability.
Discussion: The proposed workflow demonstrates high potential for improving fracture fixation in both human and veterinary medicine. By integrating CT imaging and computational modeling, this approach enhances efficiency, precision, and clinical outcomes in orthopedic surgery. Future work will refine the methodology and involve extensive clinical trials across species and fracture types.
