End-to-End Autonomous Quantification of Brain Aneurysm and Parent Artery Morphology at CT Angiography.

IF 20.1 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Samuel D Pettersson, Jean Filo, Paulina Skrzypkowska, Thomas B Fodor, Kamil Siedlecki, Peter Liaw, Ciprian N Ionita, Pratham B Bhatt, Jason Luo, Wolfgang Marik, Piotr Zieliński, Elad I Levy, Adnan H Siddiqui, Carlos E Baccin, Yu-Ming Chang, Christopher S Ogilvy
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引用次数: 0

Abstract

Purpose To develop and validate an end-to-end autonomous platform for the quantification and visualization of brain aneurysm and parent artery morphology at CT angiography (CTA). Materials and Methods: A total of 2980 CTA scans performed between 2004 and 2025 containing 2585 aneurysms from 2980 patients obtained from five international high-volume stroke centers were included. The model was trained using expert hand-annotated vascular segmentations spanning the cervical internal carotid and vertebral arteries through the A4, M3, and P3 segments. Internal prospective and multicenter external testing assessed aneurysm detection performance and compared morphology measurements with expert-derived values obtained with digital subtraction angiography-verified CTA. Statistical analysis used paired t tests or Wilcoxon signed rank tests, with sensitivity, specificity, and 95% CIs reported. A public web-based platform was developed to allow further validation. Results Internal and multicenter external testing yielded a patient-level sensitivity of 87.9% (290 of 330; 95% CI: 83.6, 91.3) and specificity of 86.6% (395 of 456; 95% CI: 83.3, 89.4). Physician-performed morphology extraction required 24.3 minutes ± 6.6 per scan, whereas the model completed the task autonomously in 90 seconds ± 12 (P < .001). At the aggregate level, no statistically significant differences were observed for aneurysm volume, neck diameter, parent artery diameter, flow angle, aspect ratio, size ratio, height-width ratio, undulating index, ellipticity index, or nonsphericity index. Dome height (mean difference, 0.1 ± 1.1; P = .03) and surface area (mean difference, -5.3 mm2 ± 29.5; P = .04) differed but had minimal effect sizes. Conclusion The developed autonomous system for rupture-related morphology metric acquisition substantially reduced physician workload. Keywords: Intracranial Aneurysm, CT Angiography, Artificial Intelligence, Morphology, Rupture Risk Supplemental material is available for this article. © RSNA, 2026 See also commentary by Maiter and Kular in this issue.

CT血管造影对脑动脉瘤和母动脉形态的端到端自主量化。
目的开发和验证一个端到端自主平台,用于CT血管造影(CTA)上脑动脉瘤和母动脉形态的量化和可视化。材料和方法入选了2004年至2025年间进行的2,980次CTA扫描,其中包含来自五个国际大容量卒中中心的2980例患者的2,585个动脉瘤。该模型使用专家手工标注的血管段进行训练,这些血管段横跨颈内颈动脉和椎动脉,通过A4、M3和P3节段。内部前瞻性和多中心外部测试评估了动脉瘤检测性能,并将形态学测量结果与经数字减影血管造影(DSA)验证的CTA获得的专家衍生值进行了比较。统计分析采用配对t检验或Wilcoxon符号秩检验,报告了敏感性、特异性和95%置信区间。开发了一个基于网络的公共平台,以便进一步验证。结果内部和多中心外部检测的患者水平敏感性为87.9% (330 / 290)(95% CI: 83.8, 91.3),特异性为86.6% (456 / 395)(95% CI: 83.3, 89.4)。医生执行的形态学提取每次扫描需要24.3±6.6分钟,而模型在90±12秒内自主完成任务(P < 0.001)。在总体水平上,动脉瘤体积、颈径、载动脉直径、血流角、长径比、大小比、高宽比、波动指数、椭圆度指数、非球性指数差异均无统计学意义。穹顶高度(MD): 0.1±1.1,P = 0.03)和表面积(MD: -5.3 mm2±29.5 mm2, P = 0.04)存在差异,但效应值最小。结论研制的破裂相关形态学测量自主采集系统大大减少了医生的工作量。©RSNA, 2026年。
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来源期刊
CiteScore
16.20
自引率
1.00%
发文量
0
期刊介绍: Radiology: Artificial Intelligence is a bi-monthly publication that focuses on the emerging applications of machine learning and artificial intelligence in the field of imaging across various disciplines. This journal is available online and accepts multiple manuscript types, including Original Research, Technical Developments, Data Resources, Review articles, Editorials, Letters to the Editor and Replies, Special Reports, and AI in Brief.
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