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

Diabetic retinopathy (DR) and diabetic macular edema are common complications
of diabetes which can lead to vision loss. The grading of DR is a fairly
complex process that requires the detection of fine features such as
microaneurysms, intraretinal hemorrhages, and intraretinal microvascular
abnormalities. Because of this, there can be a fair amount of grader
variability. There are different methods of obtaining the reference standard
and resolving disagreements between graders, and while it is usually accepted
that adjudication until full consensus will yield the best reference standard,
the difference between various methods of resolving disagreements has not been
examined extensively. In this study, we examine the variability in different
methods of grading, definitions of reference standards, and their effects on
building deep learning models for the detection of diabetic eye disease. We
find that a small set of adjudicated DR grades allows substantial improvements
in algorithm performance. The resulting algorithm's performance was on par with
that of individual U.S. board-certified ophthalmologists and retinal
specialists.

此研究探讨了采用不同的评分方法及参考标准对建立深度学习模型来检测糖尿病眼病的影响，发现采用少量的评分结果进行调节可以极大地提高算法的表现，使其性能与个别擅长该领域的专业医生相当。