Virtual-reality based patient-specific simulation of spine surgical procedures: A fast, highly automated and high-fidelity system for surgical education and planning
Raj Kumar Ranabhat, Tayler D Ross, Tony Jiao, Jeremie Larouche, Joel Finkelstein + 1 more
TLDR
A new VR system uses AI to create fast, high-fidelity, patient-specific spine surgery simulations for enhanced surgical education and planning.
Key contributions
- Developed a VR system for patient-specific spinal decompression surgery simulation using AI from CT/MRI.
- Achieves fast 3D anatomical model generation (~2.5 mins/case) with high segmentation accuracy (DSC 0.95 bone).
- Provides high-fidelity simulation of laminectomy, disc resection, and foraminotomy procedures.
- Surgeons reported improved spatial understanding and increased procedural confidence with the system.
Why it matters
This system addresses limited surgical training exposure by offering patient-specific VR simulations. It significantly reduces the time and cost of creating detailed anatomical models, enhancing pre-operative planning and surgical education. This innovation improves surgeon confidence and spatial understanding, making complex spinal procedures safer and more accessible for training.
Original 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.
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