Performance of An Automated Algorithm in Large and Medium Vessel Occlusion Detection: A Real-World Experience
Recommended Citation
Sriwastwa A, Aziz YN, Weiss K, Buse R, Zhang B, Demel SL, Ali A, Voleti S, Wang LL, and Vagal AS. Performance of An Automated Algorithm in Large and Medium Vessel Occlusion Detection: A Real-World Experience. AJNR Am J Neuroradiol 2024.
Document Type
Article
Publication Date
9-26-2024
Publication Title
AJNR. American journal of neuroradiology
Abstract
BACKGROUND AND PURPOSE: Fast, accurate detection of large (LVO) and medium vessel occlusion (MeVO) is critical for triage and management of acute ischemic stroke. Multiple AIbased software products are commercially available. However, their strengths and limitations for detection of vessel occlusion in the context of expanding indications for mechanical thrombectomy are not entirely understood. We aimed to investigate the performance of a fully automated commercial detection algorithm to identify large and medium vessel occlusions in Code Stroke patients.
MATERIALS AND METHODS: We utilized a single-center, institutional, retrospective registry of all consecutive code stroke patients with CTA and automated processing using Viz.ai presenting at a comprehensive stroke center between March 2020 and February 2023. LVO was categorized as anterior LVO (aLVO), defined as occlusion of the intracranial internal cerebral artery or M1-middle cerebral artery (MCA), and posterior LVO (pLVO), defined as occlusion of the basilar artery or V4-vertebral artery. MeVO was defined as occlusion of the M2-MCA, A1/A2-anterior cerebral artery, or P1/P2-posterior cerebral artery. Reports from 12 board-certified radiologists were considered the gold standard. We analyzed the performance of the automated algorithm using STARD guidelines. Our primary outcome was accuracy of anterior LVO (aLVO) by the software. Secondary outcomes were accuracy of the software to detect three additional categories: all LVO (aLVO and pLVO), aLVO with M2-MCA, and aLVO with MeVO.
RESULTS: Of 3,590 code stroke patients, 3,576 were technically sufficient for analysis by the automated software (median age 67 years; 51% female; 68% White), of which 616 (17.2%) had vessel occlusions. The respective sensitivity and specificity for all four pre-specified categories were: aLVO: 91% (87-94%), 93% (92-94%); all LVO: 73% (68-77%), 92% (91-93%); aLVO with M2-MCA:74% (70-78%), 93% (92-94%); aLVO with all MeVO: 65% (61-69%), and 93% (92-94%).
CONCLUSIONS: The automated algorithm demonstrated high accuracy in identifying anterior LVO with lower performance for pLVO and MeVO. It is crucial for acute stroke teams to be aware of the discordance between automated algorithm results and true rates of LVO and MeVO for timely diagnosis and triage.
PubMed ID
39326885
ePublication
ePub ahead of print
