PDF(Pubmed)
Abstract：
OBJECTIVE: Artificial Intelligence (AI) has become increasingly integrated clinically within neurosurgical oncology. This report reviews the cutting-edge technologies impacting tumor treatment and outcomes.
METHODS: A rigorous literature search was performed with the aid of a research librarian to identify key articles referencing AI and related topics (machine learning (ML), computer vision (CV), augmented reality (AR), virtual reality (VR), etc.) for neurosurgical care of brain or spinal tumors.
RESULTS: Treatment of central nervous system (CNS) tumors is being improved through advances across AI-such as AL, CV, and AR/VR. AI aided diagnostic and prognostication tools can influence pre-operative patient experience, while automated tumor segmentation and total resection predictions aid surgical planning. Novel intra-operative tools can rapidly provide histopathologic tumor classification to streamline treatment strategies. Post-operative video analysis, paired with rich surgical simulations, can enhance training feedback and regimens.
CONCLUSIONS: While limited generalizability, bias, and patient data security are current concerns, the advent of federated learning, along with growing data consortiums, provides an avenue for increasingly safe, powerful, and effective AI platforms in the future.
METHODS: A rigorous literature search was performed with the aid of a research librarian to identify key articles referencing AI and related topics (machine learning (ML), computer vision (CV), augmented reality (AR), virtual reality (VR), etc.) for neurosurgical care of brain or spinal tumors.
RESULTS: Treatment of central nervous system (CNS) tumors is being improved through advances across AI-such as AL, CV, and AR/VR. AI aided diagnostic and prognostication tools can influence pre-operative patient experience, while automated tumor segmentation and total resection predictions aid surgical planning. Novel intra-operative tools can rapidly provide histopathologic tumor classification to streamline treatment strategies. Post-operative video analysis, paired with rich surgical simulations, can enhance training feedback and regimens.
CONCLUSIONS: While limited generalizability, bias, and patient data security are current concerns, the advent of federated learning, along with growing data consortiums, provides an avenue for increasingly safe, powerful, and effective AI platforms in the future.
摘要：
目的：人工智能（AI）在神经外科肿瘤学中的临床应用日益广泛。本报告回顾了影响肿瘤治疗和结果的尖端技术。
方法：在研究馆员的帮助下进行了严格的文献检索，以识别引用AI和相关主题（机器学习（ML），计算机视觉(CV)增强现实(AR)虚拟现实(VR)等。)用于脑部或脊柱肿瘤的神经外科治疗。
结果：中枢神经系统（CNS）肿瘤的治疗正在通过AI的进步而得到改善，例如AL，CV,AR/VR人工智能辅助诊断和预测工具可以影响术前患者体验，而自动肿瘤分割和全切除预测有助于手术计划。新颖的术中工具可以快速提供组织病理学肿瘤分类以简化治疗策略。术后视频分析,搭配丰富的手术模拟，可以加强培训反馈和方案。
结论：虽然泛化能力有限，偏见，患者数据安全是当前的关注点，联合学习的出现，随着数据联盟的不断发展，提供了一条越来越安全的途径，强大,以及未来有效的AI平台。
方法：在研究馆员的帮助下进行了严格的文献检索，以识别引用AI和相关主题（机器学习（ML），计算机视觉(CV)增强现实(AR)虚拟现实(VR)等。)用于脑部或脊柱肿瘤的神经外科治疗。
结果：中枢神经系统（CNS）肿瘤的治疗正在通过AI的进步而得到改善，例如AL，CV,AR/VR人工智能辅助诊断和预测工具可以影响术前患者体验，而自动肿瘤分割和全切除预测有助于手术计划。新颖的术中工具可以快速提供组织病理学肿瘤分类以简化治疗策略。术后视频分析,搭配丰富的手术模拟，可以加强培训反馈和方案。
结论：虽然泛化能力有限，偏见，患者数据安全是当前的关注点，联合学习的出现，随着数据联盟的不断发展，提供了一条越来越安全的途径，强大,以及未来有效的AI平台。
