基于人工智能的血管内导航的血管几何特征表征
Vascular Geometry Characterization for AI-Based Endovascular Navigation
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中文总结 AI 辅助
研究旨在识别与血管内导航难度相关的血管指标并开发自动化流程，通过分割血管树测量指标，用RL算法导航并分析结果，发现血管几何形状影响导航难度，所提流程为相关评估奠定基础。
中文摘要 AI 辅助
机械取栓术（MT）是急性缺血性中风的关键干预措施，但由于神经放射科医生和专业中心短缺，其应用受限。强化学习（RL）有望实现血管内导航自动化并提高可及性，但当前模型缺乏评估导航难度的标准化框架。本研究旨在识别与导航难度相关的血管指标，开发定量血管特征提取的自动化流程。从61例患者的计算机断层血管造影中分割血管树，测量多种血管指标。使用软演员评论家RL算法进行120秒自主导航，通过混合效应线性和逻辑回归分析结果。结果表明，血管几何形状对MT代理导航难度有很大影响。所提出的自动化流程能够客观、定量地表征血管特征，为未来标准化复杂性分级和RL模型评估奠定基础。
英文摘要
Mechanical thrombectomy (MT) is a time-critical intervention for acute ischemic stroke; however, access remains limited due to a shortage of neuroradiologists and specialized centers. Reinforcement learning (RL) offers potential to automate endovascular navigation and improve accessibility, yet current models lack standardized frameworks to assess navigation difficulty for model training and evaluation. This study aims to identify vascular metrics associated with navigation difficulty and to develop an automated pipeline for quantitative vascular feature extraction, enabling future complexity grading. Vascular trees were segmented from computed tomography angiograms from 61 patients, and vascular metrics including aortic arch type, presence of bovine arch, vessel length, tortuosity, take-off angle, number of reverse curves, were measured using a custom pipeline. A Soft Actor-Critic RL algorithm was used for 120 s autonomous navigation. Outcomes were analyzed using both mixed effects linear and logistic regression. On the left side, the presence of a bovine arch and aortic arch type II/III increased navigation time by 30.19 s and 37.92 s, respectively, while greater tortuosity (\b{eta} = 118.20) further prolonged the procedure and reduced success probability. On the right side, type II/III arches extended procedure time by 45.94 s, while each additional reverse curve was associated with 3.96 s longer navigation time and lower probability of success. These findings demonstrate for the first time that MT agent navigation difficulty is strongly influenced by vascular geometry. The proposed automated pipeline enables objective and quantitative characterization of vascular features, providing a foundation for future development of standardized complexity grading and RL model evaluation, without aiming to demonstrate clinically generalizable autonomous navigation.
发表机构
- National Taiwan University Hospital（台湾大学附属医院）
- Chelsea & Westminster Hospital（切尔西和威斯敏斯特医院）
机构由 AI 辅助整理，请以论文原文为准。
