Related Experiment Video
Updated: Aug 14, 2026
Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
Published on: January 15, 2022
Multicenter Validation of an AI-Based CTA Tool for Anterior Circulation Large Vessel Occlusion Detection
Judith Cendrero1, Leonardo Tanzi1, María Hernández-Pérez1
1From the Stroke Research Group (J.C., M.R.), Vall d'Hebron Research Institute (VHIR), Barcelona, Spain; Department of Neurology (M.H.-P.), Germans Trias i Pujol University Hospital (Can Ruti), Badalona, Spain; Department of Neurology (X.U., L. O.), Hospital Clínic de Barcelona, Barcelona, Spain; Methinks AI S.L. (L.T., J.O.S., V.S.), Barcelona, Spain; Department of Radiology (A.R., D.L.), West Virginia University, Morgantown, WV, United States; Department of Neurology (T.J.), Cooper University Hospital, Cooper Medical School of Rowan University, Camden, NJ, United States; Department of Neurology (M.R.), Vall d'Hebron University Hospital, Barcelona, Spain and Department of Neurology (S.O.-G.), University of Iowa Hospitals and Clinics, Iowa City, IA, United States.
Background:
Rapid identification of anterior circulation large vessel occlusions (LVOs) is critical for timely mechanical thrombectomy in acute ischemic stroke. Computed tomography angiography (CTA) interpretation can be challenging, particularly in settings without continuous subspecialty expertise. Artificial intelligence (AI) based decision support tools may improve workflow efficiency and diagnostic consistency. The purpose of this study is to evaluate the diagnostic performance, processing time, and generalizability of the Methinks CTA-LVO software for automated detection of anterior circulation LVOs.
Methods:
This retrospective multicenter study included consecutive CTA scans from four external institutions in the United States and Europe. After quality assessment, 379 patients were analyzed (142 LVO, 237 non-LVO). Ground truth was established by review with adjudication by independent expert neuroradiologists. Primary endpoints were sensitivity and specificity for LVO detection. Secondary analyses included performance by occlusion subtype (ICA, M1, M2), time-to-notification, false positive/negative characterization, and institution-level generalization.
Results:
The algorithm achieved a sensitivity of 95.8% (95% CI: 91.0-98.4%) and specificity of 88.6% (95% CI: 84.0-92.4%), with an AUC of 97.6%. Sensitivity by subtype was 98.5% for M1, 89.7% for M2, and 97.1% for ICA occlusions. The mean time-to-notification was 3.30 minutes. Error analysis showed that several apparent false positives and negatives reflected ground-truth ambiguity, severe stenosis, or occlusions outside the study definition. Performance remained robust across institutions and CT vendors.
Conclusion:
The Methinks CTA-LVO software demonstrated high accuracy, rapid notification, and good generalizability for anterior circulation LVO detection, supporting its use as a triage tool to assist timely stroke care, with final interpretation remaining under expert supervision.
More Related Videos
04:01Point of Care Transcranial Color-Coded Duplex Ultrasound of the Middle Cerebral Artery
Published on: August 9, 2024
09:36A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis
Published on: August 12, 2025