Tanoue Shunsuke, Sakakura Yuya, Kono Kenichi
Department of Neurosurgery, Mishuku Hospital, Tokyo, Japan.
Department of Neurosurgery, National Defense Medical College Hospital, Tokorozawa, Saitama, Japan.
J Neuroendovasc Ther. 2025;19(1). doi: 10.5797/jnet.oa.2025-0028. Epub 2025 Jun 21.
Artificial intelligence (AI) holds promise for advancing neuroendovascular therapy through device evaluation, but its application in real-world clinical settings remains limited. We aimed to validate the feasibility of AI-based quantitative device evaluation during actual procedures by assessing the stability of the Rist radial access guide catheter (Medtronic, Dublin, Ireland), a novel device designed for the increasingly adopted transradial approach (TRA), during flow diverter stent (FDS) placement.
We retrospectively analyzed 4 cases of FDS placement using Rist via the TRA. Rist was tracked in recorded fluoroscopic videos using the AI technology of Neuro-Vascular Assist (iMed Technologies, Tokyo, Japan). The movement distance of Rist during FDS placement was calculated as a stability indicator.
All procedures were successfully completed without any complications. Rist was introduced from the radial artery and positioned in the distal internal carotid artery. The maximum movement distances of the Rist during the procedures were 3.36, 6.63, 1.79, and 0.33 mm for each case, respectively, with an average of 3.03 mm. The maximum movement distances per minute were 1.68, 2.34, 1.19, and 0.46 mm/min, respectively, with a mean of 1.42 mm/min. These measurements suggest sufficient stability for the FDS procedures.
This study demonstrates the feasibility of using AI technology to quantitatively analyze Rist stability in TRA procedures. To the best of our knowledge, this is the 1st clinical evaluation of device function in a clinical setting using AI technology. Further studies with more cases are required to validate these findings. This method is promising for real-world device evaluation and development.
人工智能有望通过器械评估推动神经血管内治疗，但在实际临床环境中的应用仍然有限。我们旨在通过评估Rist桡动脉入路导引导管（美敦力公司，都柏林，爱尔兰）在血流导向支架（FDS）置入过程中的稳定性，验证基于人工智能的定量器械评估在实际手术中的可行性，Rist是一种为日益广泛采用的桡动脉入路（TRA）设计的新型器械。
我们回顾性分析了4例通过TRA使用Rist进行FDS置入的病例。使用Neuro-Vascular Assist（日本东京iMed Technologies公司）的人工智能技术在记录的透视视频中跟踪Rist。计算FDS置入过程中Rist的移动距离作为稳定性指标。
所有手术均成功完成，无任何并发症。Rist从桡动脉引入并定位在颈内动脉远端。各病例中Rist在手术过程中的最大移动距离分别为3.36、6.63、1.79和0.33mm，平均为3.03mm。每分钟的最大移动距离分别为1.68、2.34、1.19和0.46mm/min，平均为1.42mm/min。这些测量结果表明FDS手术具有足够的稳定性。
本研究证明了使用人工智能技术定量分析TRA手术中Rist稳定性的可行性。据我们所知，这是首次在临床环境中使用人工智能技术对器械功能进行临床评估。需要更多病例的进一步研究来验证这些发现。这种方法在实际器械评估和开发方面很有前景。
