Purpose: Autonomous navigation of devices in endovascular interventions can decrease operation times, improve decision-making during surgery, and reduce operator radiation exposure while increasing access to treatment. This systematic review explores recent literature to assess the impact, challenges, and opportunities artificial intelligence (AI) has for the autonomous endovascular intervention navigation. Methods: PubMed and IEEEXplore databases were queried. Eligibility criteria included studies investigating the use of AI in enabling the autonomous navigation of catheters/guidewires in endovascular interventions. Following PRISMA, articles were assessed using QUADAS-2. PROSPERO: CRD42023392259. Results: Among 462 studies, fourteen met inclusion criteria. Reinforcement learning (9/14, 64%) and learning from demonstration (7/14, 50%) were used as data-driven models for autonomous navigation. Studies predominantly utilised physical phantoms (10/14, 71%) and in silico (4/14, 29%) models. Experiments within or around the blood vessels of the heart were reported by the majority of studies (10/14, 71%), while simple non-anatomical vessel platforms were used in three studies (3/14, 21%), and the porcine liver venous system in one study. We observed that risk of bias and poor generalisability were present across studies. No procedures were performed on patients in any of the studies reviewed. Studies lacked patient selection criteria, reference standards, and reproducibility, resulting in low clinical evidence levels. Conclusions: AI's potential in autonomous endovascular navigation is promising, but in an experimental proof-of-concept stage, with a technology readiness level of 3. We highlight that reference standards with well-identified performance metrics are crucial to allow for comparisons of data-driven algorithms proposed in the years to come.

翻译：目的：血管内介入手术中器械的自主导航可缩短操作时间、改善术中决策、减少操作者辐射暴露，同时增加治疗可及性。本系统性综述通过梳理近期文献，评估人工智能（AI）对血管内介入自主导航的影响、挑战与机遇。方法：检索PubMed与IEEEXplore数据库。纳入标准为研究AI在血管内介入中实现导管/导丝自主导航的研究。依据PRISMA指南，采用QUADAS-2工具评估文献质量。PROSPERO注册号：CRD42023392259。结果：在462项研究中，14项符合纳入标准。强化学习（9/14，64%）与示范学习（7/14，50%）被用作自主导航的数据驱动模型。研究主要采用物理体模（10/14，71%）与计算机仿真（4/14，29%）模型。多数研究（10/14，71%）报告了在心脏血管内或周围开展的实验，三项研究（3/14，21%）使用简单非解剖血管平台，一项研究使用猪肝静脉系统。我们观察到所有研究均存在偏倚风险与泛化性不足问题。所有纳入研究均未开展患者体内实验。研究缺乏患者选择标准、参考标准及可重复性，导致临床证据等级较低。结论：AI在血管内自主导航领域具有潜力，但目前仍处于实验性概念验证阶段，技术成熟度等级为3级。我们强调，建立包含明确定义性能指标的参考标准对于未来比较不同数据驱动算法至关重要。