ScolioSim uses biomechanical modeling and AI to predict how the unfused spine will respond after scoliosis correction surgery.
Predicts compensatory curves above and below instrumented levels using real biomechanical data.
Determine the optimal proximal and distal fusion levels for maximum correction with minimum segments.
Real-time visualization of global spinal balance changes as you adjust your surgical plan.
Machine learning trained on surgical outcomes to estimate post-operative alignment and complication risk.
Automatic curve classification with integrated Lenke typing to guide treatment decisions.
Interactive 3D spine model that updates in real-time as you modify surgical parameters.
Three steps to a biomechanically-informed surgical strategy.
Enter Cobb angles, vertebral measurements, and flexibility indices from standing and bending X-rays.
Select fusion levels, correction targets, and implant configuration. The simulator shows predicted outcomes instantly.
Run multiple strategies side by side. Compare expected correction, balance, and compensation patterns.
Generate a detailed surgical plan report with predicted angles, balance parameters, and risk assessment.
ScolioSim was created by Dr. Diego Ferrández Sempere, a spine neurosurgeon with 20+ years of experience in adolescent scoliosis surgery. Born from the need to better predict surgical outcomes and minimize revision rates.