Abstract
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.
Abstract (translated)
目的：在介入治疗中，自主导航设备的操作时间可以缩短，手术过程中的决策可以得到改善，同时辐射剂量可以降低，同时提高治疗的可获取性。本系统综述评估了近年来关于人工智能（AI）在自主导航穿刺器/引导线在介入治疗中的影响、挑战和机会的文献，以评估AI在自主导航穿刺器/引导线在介入治疗中的潜在影响。方法：PubMed和IEEE Explore数据库进行查询。符合资格标准的研究包括研究使用AI促进自主导航穿刺器/引导线在介入治疗中的应用。然后使用PRISMA和QUADAS-2对文章进行评估。PROSPERO: CRD42023392259。结果：在462篇论文中，有14篇符合资格标准。强化学习（9/14，64%）和学习演示（7/14，50%）被用作数据驱动模型进行自主导航。研究主要使用物理幻象（10/14，71%）和仿真（4/14，29%）模型。大多数研究（10/14，71%）报道了心脏血管内实验，而三篇研究（3/14，21%）使用了简单非解剖性血管平台，一篇研究（1/14，7）使用了猪肝静脉系统。我们观察到，研究中的偏见和普遍性存在。在所有审查的研究中，没有对患者进行任何操作。研究缺乏患者选择标准、参考标准和可重复性，导致临床证据水平较低。结论：AI在自主导航介入治疗中的潜在影响是积极的，但目前仍处于实验验证阶段，技术成熟度为3。我们强调，具有明确定义的性能指标的参考标准对于允许未来数据驱动算法的比较至关重要。
URL
https://arxiv.org/abs/2405.03305
