Xiaolin Hou, Xiaoling Liao, Ruxiang Xu, Fan Fei, Bo Wu
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Comparative analysis between AI-assisted 3D-color MIF (group A) and manual-3D-monochrome MIF (group B) was conducted, evaluating surgical parameters (operative time, blood loss, resection completeness), clinical outcomes (complications, hospital stay, modified Rankin Scale [mRS] scores), and technical performance metrics (processing time, Dice similarity coefficient [DSC], 95% Hausdorff distance [HD]).</p><p><strong>Results: </strong>The AI-3D-color MIF system achieved superior technical performance with brain segmentation in 1.21 ± 0.13 minutes (vs 4.51 ± 0.15 minutes for manual segmentation), demonstrating exceptional accuracy (DSC 0.978 ± 0.012 vs 0.932 ± 0.029; 95% HD 1.51 ± 0.23 mm vs 3.52 ± 0.35 mm). Clinically, group A demonstrated significant advantages with shorter operative duration, reduced intraoperative blood loss, higher rate of gross-total resection, lower complication incidence, and better postoperative mRS scores (all p < 0.05).</p><p><strong>Conclusions: </strong>The integration of open-source AI tools (FastSurfer/Raidionics) with AR visualization creates an efficient 3D-color MIF workflow that enhances anatomical understanding through color-coded functional mapping and vascular relationship visualization. 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引用次数: 0
摘要
目的:本研究旨在利用开源人工智能(AI)辅助的快速3D彩色多模态图像融合(MIF)和增强现实(AR)技术,开发一种在脑外肿瘤手术中进行术前规划和手术指导的先进方法。方法:在这项针对130名脑外肿瘤患者的前瞻性试验中,作者实施了一种新的工作流程,结合了FastSurfer(基于人工智能的脑分割)、Raidionics-Slicer(深度学习肿瘤分割)和Sina AR投影。将人工智能辅助的3d -彩色MIF (A组)与手动3d -单色MIF (B组)进行对比分析,评估手术参数(手术时间、出血量、切除完整性)、临床结果(并发症、住院时间、改良Rankin量表[mRS]评分)和技术性能指标(处理时间、Dice相似系数[DSC]、95% Hausdorff距离[HD])。结果:AI-3D-color MIF系统在1.21±0.13分钟(人工分割为4.51±0.15分钟)内实现了卓越的脑分割技术性能,显示出卓越的准确率(DSC 0.978±0.012 vs 0.932±0.029;95%高清(1.51±0.23 mm vs 3.52±0.35 mm)。临床方面,A组手术时间短、术中出血量少、总切除率高、并发症发生率低、术后mRS评分好,均具有显著优势(p < 0.05)。结论:开源人工智能工具(FastSurfer/Raidionics)与AR可视化的集成创建了一个高效的3d彩色MIF工作流,通过颜色编码的功能映射和血管关系可视化增强了解剖理解。该系统显著提高了手术精度,同时降低了围手术期风险,代表了资源受限环境下高级神经外科计划的成本效益解决方案。
Open-source AI-assisted rapid 3D color multimodal image fusion and preoperative augmented reality planning of extracerebral tumors.
Objective: This study aimed to develop an advanced method for preoperative planning and surgical guidance using open-source artificial intelligence (AI)-assisted rapid 3D color multimodal image fusion (MIF) and augmented reality (AR) in extracerebral tumor surgical procedures.
Methods: In this prospective trial of 130 patients with extracerebral tumors, the authors implemented a novel workflow combining FastSurfer (AI-based brain parcellation), Raidionics-Slicer (deep learning tumor segmentation), and Sina AR projection. Comparative analysis between AI-assisted 3D-color MIF (group A) and manual-3D-monochrome MIF (group B) was conducted, evaluating surgical parameters (operative time, blood loss, resection completeness), clinical outcomes (complications, hospital stay, modified Rankin Scale [mRS] scores), and technical performance metrics (processing time, Dice similarity coefficient [DSC], 95% Hausdorff distance [HD]).
Results: The AI-3D-color MIF system achieved superior technical performance with brain segmentation in 1.21 ± 0.13 minutes (vs 4.51 ± 0.15 minutes for manual segmentation), demonstrating exceptional accuracy (DSC 0.978 ± 0.012 vs 0.932 ± 0.029; 95% HD 1.51 ± 0.23 mm vs 3.52 ± 0.35 mm). Clinically, group A demonstrated significant advantages with shorter operative duration, reduced intraoperative blood loss, higher rate of gross-total resection, lower complication incidence, and better postoperative mRS scores (all p < 0.05).
Conclusions: The integration of open-source AI tools (FastSurfer/Raidionics) with AR visualization creates an efficient 3D-color MIF workflow that enhances anatomical understanding through color-coded functional mapping and vascular relationship visualization. This system significantly improves surgical precision while reducing perioperative risks, representing a cost-effective solution for advanced neurosurgical planning in resource-constrained settings.