Objectives This study aims to explore the ability to identify high-grade intracranial arterial stenosis (ICAS) by an artificial intelligence (AI) designed to detect large vessel occlusions (LVO) and the clinical relevance of these 'false positive' findings. Design We are presenting a retrospective cohort study.
Setting The study was conducted at a supraregional stroke centre of an urban tertiary care provider. Participants Consecutive stroke cases treated between January 2023 and December 2023 of patients >18 years of both sexes and any ethnicity were eligible for inclusion.
934 patients (52.7% male) with a mean age of 71.7±13.6 years (25 - 101 years) were included. Interventions CT angiographies were analysed by a deep learning algorithm for LVO detection of the anterior circulation.
AI results were compared with radiology reports and secondary focused evaluation. Primary and secondary outcome measures Diagnostic accuracies for ICAS detection by the AI were calculated.
Results Primary reports identified 30 ICAS and nine additional ICAS were detected during secondary evaluation (incidence 4.2%).
BMJ Open published a clinical update in Research Highlights on 11 Jun 2026.
The item focuses on Clinical relevance of intracranial stenosis as false-positive findings of a deep learning algorithm trained to detect large vessel occlusions: a retrospective cohort study of a supraregional stroke centre.
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