Table 1_Automatic collateral quantification in acute ischemic stroke using U2-net.docx

ObjectivesTo harness the U2-Net deep learning framework for automated quantification of collateral circulation in acute ischemic stroke (AIS) via computed tomography angiography (CTA) images, comparing its performance against traditional visual collateral scores (vCS).
MethodsA cohort of 118 confirmed AIS cases was assembled and stratified into 94 development and 24 test cases. CTA images underwent preprocessing and annotation. The U2-Net was trained to segment collateral vessels, yielding a quantitative collateral score (qCS) based on vessel volume ratios between affected and healthy hemispheres. Performance was assessed via Dice Similarity Coefficient (DSC), Spearman correlation, Intraclass Correlation Coefficient (ICC), and accuracy, with comparisons to vCS (Tan and Menon score) and ground truth.
ResultThe U2-Net demonstrated robust segmentation capabilities, achieving a mean DSC of 0.75 in the test set. The qCS showed a strong correlation with vCS with ρ ranging from 0.78 to 0.92. When compared to the more refined six-class Menon score, the qCS exhibited stronger consistency (development set: ICC = 0.83, test set: ICC = 0.93) than when compared to the four-class Tan score (development set: ICC = 0.76, test set: ICC = 0.79). In terms of classification accuracy, the AI model achieved 0.83 and 0.71 against ground truth and vCS, respectively, for four-class classification. This accuracy escalated to 0.88 and 0.83 for binary classification, emphasizing its proficiency in differentiating collateral status.
ConclusionOur U2-Net AI model offers a reliable, objective tool for quantifying collateral circulation in AIS. The qCS aligns well with vCS and demonstrates the feasibility of automated collateral assessment, which may enhance diagnostic accuracy and therapeutic decision-making.

研究目的：本研究旨在采用U2-Net深度学习框架，通过计算机断层扫描血管造影（computed tomography angiography, CTA）图像对急性缺血性脑卒中（acute ischemic stroke, AIS）的侧支循环进行自动化定量评估，并将其性能与传统视觉侧支评分（visual collateral scores, vCS）进行对比。
研究方法：本研究纳入118例经确诊的急性缺血性脑卒中病例并构建研究队列，将其分层划分为94例开发集与24例测试集。对计算机断层扫描血管造影图像进行预处理与标注。训练U2-Net模型以分割侧支血管，并基于患侧与健侧半球的血管体积比生成定量侧支评分（quantitative collateral score, qCS）。采用戴斯相似系数（Dice Similarity Coefficient, DSC）、斯皮尔曼相关性分析、组内相关系数（Intraclass Correlation Coefficient, ICC）以及分类准确率对模型性能进行评估，并与视觉侧支评分（谭氏评分与梅农评分（Tan and Menon score））及金标准进行对比。
研究结果：U2-Net模型展现出优异的分割性能，在测试集上的平均戴斯相似系数达0.75。定量侧支评分与视觉侧支评分呈现较强相关性，斯皮尔曼相关系数ρ取值介于0.78至0.92之间。相较于四分类谭氏评分，定量侧支评分与更为精细的六分类梅农评分的一致性更优：开发集组内相关系数为0.83，测试集为0.93；而针对四分类谭氏评分的对应组内相关系数，开发集为0.76，测试集为0.79。在分类准确率方面，针对四分类任务，该AI模型相较于金标准与视觉侧支评分的准确率分别为0.83与0.71；针对二分类任务，准确率分别提升至0.88与0.83，凸显其在区分侧支循环状态方面的优异性能。
研究结论：本研究构建的U2-Net人工智能模型可为急性缺血性脑卒中患者的侧支循环定量评估提供可靠、客观的工具。定量侧支评分与视觉侧支评分具有良好的一致性，证实了自动化侧支循环评估的可行性，或可提升诊断准确性并辅助治疗决策制定。