Methinks AI software for identifying large vessel occlusion in non-contrast head CT: A pilot retrospective study in American population.

Abstract

BackgroundNon-contrast computed tomography (NCCT) is the first image for stroke assessment, but its sensitivity for detecting large vessel occlusion (LVO) is limited. Artificial intelligence (AI) algorithms may contribute to a faster LVO diagnosis using only NCCT. This study evaluates the performance and the potential diagnostic time saving of Methinks LVO AI algorithm in a U.S. multi-facility stroke network.MethodsThis retrospective pilot study reviewed NCCT and computed tomography angiography (CTA) images between 2015 and 2023. The Methinks AI algorithm, designed to detect LVOs in the internal carotid artery and middle cerebral artery, was tested for sensitivity, specificity, and predictive values. A neuroradiologist reviewed cases to establish a gold standard. To evaluate potential time saving in workflow, time gaps between NCCT and CTA were analyzed and stratified into four groups in true positive cases: Group 1 (<10 min), Group 2 (10-30 min), Group 3 (30-60 min), and Group 4 (>60 min).ResultsFrom a total of 1155 stroke codes, 608 NCCT exams were analyzed. Methinks LVO demonstrated 75% sensitivity and 83% specificity, identifying 146 out of 194 confirmed LVO cases correctly. The PPV of the algorithm was 72%. The NPV was 83% (considering 'other occlusion', 'stenosis' and 'posteriors' as negatives), and 73% considered the same conditions as positives. Among the true positive cases, we found 112 patients Group 1, 32 patients in Group 2, 15 patients in Group 3, 3 patients in Group 4.ConclusionThe Methinks AI algorithm shows promise for improving LVO detection from NCCT, especially in resource limited settings. However, its sensitivity remains lower than CTA-based systems, suggesting the need for further refinement.