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

Problem
This preprint addresses the gap in surgical training methodologies, particularly the lack of patient-specific simulations in virtual reality (VR) environments. Traditional VR training often relies on standardized scenarios, which do not account for individual anatomical variations. The authors aim to enhance surgical education and planning by leveraging AI-driven computer vision techniques to create tailored simulations based on patient-specific imaging data from computed tomography (CT) and magnetic resonance imaging (MRI).

Method
The core technical contribution involves a two-step process: (1) automatic generation of 3D anatomical models from CT and MRI data through multimodal fusion and segmentation, and (2) the development of a VR simulation platform for spinal decompression procedures, including laminectomy, disc resection, and foraminotomy. The segmentation process utilized advanced algorithms to delineate vertebral bone and soft tissue structures, evaluated using the Dice Similarity Coefficient (DSC). Registration accuracy was assessed via Target Registration Error (TRE). The entire model construction process was optimized for efficiency, achieving approximately 2.5 minutes per case across 15 subjects. The authors did not disclose specific training compute resources used for model training.

Results
The segmentation accuracy yielded a DSC of 0.95 (±0.03) for vertebral bone and 0.895 (±0.02) for soft tissue structures, indicating high fidelity in anatomical representation. The mean TRE for registration accuracy was reported at 1.73 (±0.42) mm. Qualitative feedback from semi-structured interviews with surgeons and trainees highlighted significant improvements in spatial understanding and procedural confidence, alongside a strong perceived educational value of the simulations. The platform demonstrated a substantial reduction in time and costs associated with patient-specific modeling, enhancing pre-operative planning and post-procedural assessments.

Limitations
The authors acknowledge that the study is limited by its small sample size (N = 15) and the preliminary nature of qualitative feedback, which may not fully capture the long-term educational impact of the simulations. Additionally, the study does not address the scalability of the system for broader clinical use or the integration of real-time feedback mechanisms during simulations. The reliance on high-quality imaging data may also limit applicability in settings with less advanced imaging capabilities.

Why it matters
This work has significant implications for the future of surgical education and planning. By providing a framework for creating patient-specific VR simulations, it opens avenues for personalized training that can enhance surgical skills and decision-making. The integration of AI in generating these simulations could lead to more efficient training processes, potentially reducing the learning curve for complex procedures. Furthermore, the ability to visualize and interact with patient-specific anatomy in a risk-free environment may improve surgical outcomes and patient safety in real-world applications.

Authors: Raj Kumar Ranabhat, Tayler D Ross, Tony Jiao, Jeremie Larouche, Joel Finkelstein, Michael Hardisty
Source: arXiv:2604.26781
URL: https://arxiv.org/abs/2604.26781v1