Predicting surgical outcomes in spring assisted cranioplasty via finite element analysis and animal experiments

Abstract

Abstract Sagittal craniosynostosis, a congenital cranial suture disorder, is treated with spring-assisted cranioplasty (SAC), but optimal surgical parameters remain unclear. This study explores computational models to predict surgical outcomes of SAC by linking biomechanics with bone regeneration and cranial remodeling. Fifteen 3-week-old Sprague–Dawley (SD) rats underwent SAC with nickel-titanium springs (0–100 g forces). Bone regeneration was tracked via fluorescence labeling, while micro-CT scans measured cephalic index (CI), bone mineral density (BMD), and bone volume fraction (BV/TV). Finite element analysis (FEA) simulated stress and strain distributions. Regression models were established to predict the relationship between mechanical indicators and surgical outcomes. The 50 g group achieved optimal bone regeneration and cranial correction, while > 80 g forces risked bone damage. FEA indicates stress and strain over the bone are influenced by spring force, rat geometry, and bone density. Regression analysis revealed linear strain-CI relationships and quadratic strain-BV/TV and strain-BMD relationship. Moderate spring force (50 g) or strain (1.0%) enhances osteogenesis without structural compromise. Computational models provide a biomechanically grounded framework for SAC optimization, advancing precision treatment for sagittal craniosynostosis. Future studies should validate findings in disease-specific models and assess long-term outcomes.