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

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.