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.

人工智能在自主血管内操作导航方面的潜力很大，但目前仍处于实验概念验证阶段，技术准备水平为 3。我们强调指定性能度量的参考标准对于未来几年提出的数据驱动算法的比较至关重要。

内镜手术中的人工智能自主导航：系统综述

Artificial Intelligence in the Autonomous Navigation of Endovascular  Interventions: A Systematic Review

During endovascular interventions, physicians have to perform accurate and
immediate operations based on the available real-time information, such as the
shape and position of guidewires observed on the fluoroscopic images, haptic
information and the patients' physiological signals. For this purpose,
real-time and accurate guidewire segmentation and tracking can enhance the
visualization of guidewires and provide visual feedback for physicians during
the intervention as well as for robot-assisted interventions. Nevertheless,
this task often comes with the challenge of elongated deformable structures
that present themselves with low contrast in the noisy fluoroscopic image
sequences. To address these issues, a two-stage deep learning framework for
real-time guidewire segmentation and tracking is proposed. In the first stage,
a Yolov5s detector is trained, using the original X-ray images as well as
synthetic ones, which is employed to output the bounding boxes of possible
target guidewires. More importantly, a refinement module based on
spatiotemporal constraints is incorporated to robustly localize the guidewire
and remove false detections. In the second stage, a novel and efficient network
is proposed to segment the guidewire in each detected bounding box. The network
contains two major modules, namely a hessian-based enhancement embedding module
and a dual self-attention module. Quantitative and qualitative evaluations on
clinical intra-operative images demonstrate that the proposed approach
significantly outperforms our baselines as well as the current state of the art
and, in comparison, shows higher robustness to low quality images.

提出了一种用于实时导丝分割和跟踪的两阶段深度学习框架，包括一个用于精确定位导丝的 Yolov5s 检测器和一个用于分割导丝的新颖高效网络，通过定量和定性评估证明该方法在临床术中图像上表现出更高的鲁棒性和比当前技术更好的性能。