由于先进的成像技术,肾细胞癌(RCC)的患病率正在增加。手术切除是标准的治疗方法,涉及复杂的根治性和部分性肾切除术,需要广泛的培训和计划。此外,人工智能(AI)可以潜在地帮助肾癌领域的训练过程。这篇综述探讨了人工智能(AI)如何为肾癌手术创建一个框架来解决训练困难。按照PRISMA2020标准,我们对PubMed和SCOPUS数据库进行了详尽搜索,没有任何过滤器或限制.纳入标准包括原始的英文文章,重点是AI在肾癌手术训练中的作用。另一方面,所有非原创文章和以英语以外的任何语言发表的文章均被排除.两名独立审稿人评估了这些文章,与第三方解决任何分歧。研究细节,AI工具,方法论,端点,结果由同一作者提取。牛津循证医学中心的证据水平被用来评估这些研究。在468条确定的记录中,选择了14项符合条件的研究。AI在肾癌手术培训中的潜在应用包括分析手术工作流程,注释仪器,识别组织,和三维重建。人工智能能够评估手术技能,包括程序步骤和仪器跟踪的识别。虽然AI和增强现实(AR)增强了训练,在实时跟踪和注册方面仍然存在挑战。利用AI驱动的3D重建被证明有利于术中指导和术前准备。人工智能(AI)显示出通过提供公正的评估来推进手术训练的潜力。个性化反馈,加强学习过程。然而,诸如一致的度量度量、伦理问题,必须解决数据隐私问题。将AI整合到肾癌手术培训中,为培训困难提供了解决方案,并促进了手术教育。然而,为了充分利用它的潜力,更多的研究势在必行。
The prevalence of renal cell carcinoma (RCC) is increasing due to advanced imaging techniques. Surgical resection is the standard treatment, involving complex radical and partial nephrectomy procedures that demand extensive training and planning. Furthermore, artificial intelligence (AI) can potentially aid the training process in the field of kidney cancer. This
review explores how artificial intelligence (AI) can create a framework for kidney cancer surgery to address training difficulties. Following PRISMA 2020 criteria, an exhaustive search of PubMed and SCOPUS databases was conducted without any filters or restrictions. Inclusion criteria encompassed original English articles focusing on AI\'s role in kidney cancer surgical training. On the other hand, all non-original articles and articles published in any language other than English were excluded. Two independent reviewers assessed the articles, with a third party settling any disagreement. Study specifics, AI tools, methodologies, endpoints, and outcomes were extracted by the same authors. The Oxford Center for Evidence-Based Medicine\'s evidence levels were employed to assess the studies. Out of 468 identified records, 14 eligible studies were selected. Potential AI applications in kidney cancer surgical training include analyzing surgical workflow, annotating instruments, identifying tissues, and 3D reconstruction. AI is capable of appraising surgical skills, including the identification of procedural steps and instrument tracking. While AI and augmented reality (AR) enhance training, challenges persist in real-time tracking and registration. The utilization of AI-driven 3D reconstruction proves beneficial for intraoperative guidance and preoperative preparation. Artificial intelligence (AI) shows potential for advancing surgical training by providing unbiased evaluations, personalized feedback, and enhanced learning processes. Yet challenges such as consistent metric measurement, ethical concerns, and data privacy must be addressed. The integration of AI into kidney cancer surgical training offers solutions to training difficulties and a boost to surgical education. However, to fully harness its potential, additional studies are imperative.