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.

翻译：外科培训包括理论教学、导师指导学习、手术技能实验室以及直接参与手术；然而，日益增加的临床压力限制了手术室（OR）的实践机会。本研究利用虚拟现实（VR）提供安全且沉浸式的培训环境。现有VR培训通常基于标准化场景，未能针对个体临床病例进行定制。本研究通过基于人工智能（AI）的计算机视觉方法，从计算机断层扫描（CT）和磁共振成像（MRI）生成患者特异性模拟，以此解决这一局限。本研究聚焦于虚拟手术室中针对椎管狭窄的患者特异性脊柱减压模拟。研究目标包括：（1）自动构建三维解剖模型；（2）VR模拟脊柱减压手术过程，包括椎板切除、椎间盘切除和椎间孔切开术。模型构建需对CT与MRI进行多模态融合（配准）并分割相关结构。使用Dice相似系数（DSC）评估分割精度，目标配准误差（TRE）评估配准精度。通过半结构化访谈获取外科医生及学员的定性反馈。高效生成了高保真患者特异性三维模型（平均每例约2.5分钟，N=15）。分割精度较高，椎骨DSC为0.95（±0.03），软组织结构DSC为0.895（±0.02）。配准精度显示平均TRE为1.73（±0.42）mm。半结构化访谈表明，该平台提升了空间理解能力、增强了手术操作信心，并具有显著的教育价值。本平台显著降低了患者特异性建模的时间和成本，从而支持术前规划、术后评估及全面的手术模拟。