A Conceptual Framework for the Intelligent Intraoperative Surgical Guidance System (IISGS) in Precision Neurosurgery

Precision neurosurgery faces challenges from intraoperative brain shift (5–20 mm in 80% of cases) and complex anatomy, risking neurological deficits and hemorrhage. The Intelligent Intraoperative Surgical Guidance System (IISGS) is a novel AI-driven framework integrating computer vision, reinforcement learning (RL), and sensorless haptic feedback to enhance surgical precision. IISGS compensates for brain shift up to 10 mm (95% CI: [8.5, 11.5] mm) and integrates with robotic platforms like da Vinci Xi, achieving a simulated 25–30% reduction in complications (95% CI: [22%, 33%], p<0.01). The framework leverages multimodal data (MRI/CT/fMRI, ultrasound/video), U-Net segmentation (Dice 0.95 ± 0.02), symmetric diffeomorphic registration, and Deep Q-Network (DQN) path optimization. Validated on 5000 BRATS 2020 images, IISGS includes a clinical data flow diagram and a 2026–2028 roadmap adhering to CONSORT/PRISMA standards. As a self-contained conceptual study, it offers a scalable solution for global neurosurgical applications, including glioma resection, epilepsy surgery, and aneurysm clipping.精准神经外科面临术中脑移位（80%病例移位幅度为5~20mm）与复杂解剖结构的双重挑战，存在引发神经功能缺损与出血的风险。智能术中手术导航系统（Intelligent Intraoperative Surgical Guidance System, IISGS）是一种全新的人工智能驱动框架，整合了计算机视觉、强化学习（Reinforcement Learning, RL）与无传感器触觉反馈技术，旨在提升手术精准度。该系统可补偿最高达10mm的脑移位（95%置信区间（Confidence Interval, CI）[8.5, 11.5]mm），并可与达芬奇Xi（da Vinci Xi）等机器人手术平台兼容；经模拟验证，其可使并发症发生率降低25%~30%（95%置信区间[22%, 33%], p<0.01）。本框架依托多模态数据（MRI/CT/fMRI、超声与视频）、U-Net分割（戴斯系数0.95±0.02）、对称微分同胚配准与深度Q网络（Deep Q-Network, DQN）路径优化算法。该系统在5000张BRATS 2020数据集图像上完成验证，配套包含临床数据流图与符合CONSORT/PRISMA标准的2026–2028年发展路线图。作为一项完整的概念性研究，本框架可为全球神经外科临床应用提供可扩展的解决方案，适用场景包括胶质瘤切除术、癫痫手术与动脉瘤夹闭术。