川北医学院学报2026，Vol.41Issue(6)：683-688,6.DOI:10.3969/j.issn.1005-3697.2026.06.008
人工智能在头颈部CTA血管狭窄评估中的临床应用价值
Clinical value of AI in the assessment of vascular stenosis on head and neck CTA
摘要
Abstract
Objective:To investigate the value of artificial intelligence(AI)in grading vascular stenosis on head and neck CT angiography(CTA)and to compare its diagnostic performance with that of frontline and second-line radiologists in a real-world clinical workflow.Methods:A total of 212 patients who underwent both head and neck CTA and digital subtraction angiography(DSA)were retrospectively enrolled.Using 3,816 vascular segments as the unit of analysis,DSA was used as the reference standard.The diagnostic performance of frontline radiologists,second-line radiologists,and the AI system was compared for three binary endpoints:≥50%stenosis,≥70%stenosis,and complete occlusion.McNemar tests,receiver operating characteristic(ROC)curve analyses and DeLong tests were performed.Further analyses compared AI with frontline radiologists of different seniority levels and evaluated its performance in the anterior and posterior circulation.Results:Second-line radiologists achieved the best overall diagnostic performance across all three endpoints.The overall performance of the AI system was comparable to that of frontline radiologists but remained inferior to that of second-line radiologists(P<0.001).For the three endpoints,the AUCs of frontline radiologists,second-line radiologists,and the AI system were 0.841,0.910,and 0.838 for≥50%stenosis,0.893,0.950,and 0.877 for≥70%stenosis,and 0.931,0.964,and 0.914 for complete occlusion,respectively.No significant differences in AUC were observed between AI and frontline radiologists(P>0.05),whereas AI differed significantly from second-line radiologists for all endpoints(P<0.001).After stratification,no significant AUC differences were found between AI and either residents or attending radiologists(P>0.05).However,for the≥50%and≥70%stenosis endpoints,AI showed better paired classification performance than residents.AI performed relatively stable in the anterior circulation,while its sensitivity decreased in the posterior circulation.Conclusion:AI demonstrates adjunctive value in grading vascular stenosis on head and neck CTA.Its overall performance is comparable to that of frontline radiologists but remains inferior to that of second-line radiologists.At the current stage,AI is better suited as an assistive tool for frontline in-terpretation and as a prompt before second-line review.关键词
头颈部CTA/人工智能/数字减影血管造影/血管狭窄/诊断效能Key words
Head and neck CT angiography/Artificial intelligence/Digital subtraction angiography/Vascular stenosis/Diagnostic performance分类
医药卫生引用本文复制引用
邱林,石涛,邓雪梅,苏思宇,林鹏,林永,蔡春仙,刘爱民..人工智能在头颈部CTA血管狭窄评估中的临床应用价值[J].川北医学院学报,2026,41(6):683-688,6.基金项目
四川省卫生健康委员会科技项目(25CXTD43) （25CXTD43）
四川省医学会青年创新项目(Q2024013) （Q2024013）
四川省内江市基础研究与应用基础研究(2024NJJCYJEYY018) （2024NJJCYJEYY018）
