TY - JOUR
TI - Hyperdense Artery Sign in Patients With Acute Ischemic Stroke–Automated Detection With Artificial Intelligence-Driven Software
AU - Weyland, C.S.
AU - Papanagiotou, P.
AU - Schmitt, N.
AU - Joly, O.
AU - Bellot, P.
AU - Mokli, Y.
AU - Ringleb, P.A.
AU - Kastrup, A.
AU - Möhlenbruch, M.A.
AU - Bendszus, M.
AU - Nagel, S.
AU - Herweh, C.
JO - Frontiers in Neurology
PY - 2022
VL - 13
TODO - null
SP - null
PB - Frontiers Media S.A
SN - null
TODO - 10.3389/fneur.2022.807145
TODO - acute ischemic stroke;  adult;  aged;  algorithm;  Article;  artificial intelligence;  automation;  computed tomographic angiography;  computer assisted tomography;  controlled study;  diagnostic test accuracy study;  disease severity;  double blind procedure;  female;  human;  hyperdense artery sign;  intermethod comparison;  major clinical study;  male;  neuroimaging;  neurologic disease assessment;  neuroradiologist;  secondary analysis;  sensitivity and specificity;  software;  standard;  thrombus;  very elderly
TODO - Background: Hyperdense artery sign (HAS) on non-contrast CT (NCCT) can indicate a large vessel occlusion (LVO) in patients with acute ischemic stroke. HAS detection belongs to routine reporting in patients with acute stroke and can help to identify patients in whom LVO is not initially suspected. We sought to evaluate automated HAS detection by commercial software and compared its performance to that of trained physicians against a reference standard. Methods: Non-contrast CT scans from 154 patients with and without LVO proven by CT angiography (CTA) were independently rated for HAS by two blinded neuroradiologists and an AI-driven algorithm (Brainomix®). Sensitivity and specificity were analyzed for the clinicians and the software. As a secondary analysis, the clot length was automatically calculated by the software and compared with the length manually outlined on CTA images as the reference standard. Results: Among 154 patients, 84 (54.5%) had CTA-proven LVO. HAS on the correct side was detected with a sensitivity and specificity of 0.77 (CI:0.66–0.85) and 0.87 (0.77–0.94), 0.8 (0.69–0.88) and 0.97 (0.89–0.99), and 0.93 (0.84–0.97) and 0.71 (0.59–0.81) by the software and readers 1 and 2, respectively. The automated estimation of the thrombus length was in moderate agreement with the CTA-based reference standard [intraclass correlation coefficient (ICC) 0.73]. Conclusion: Automated detection of HAS and estimation of thrombus length on NCCT by the tested software is feasible with a sensitivity and specificity comparable to that of trained neuroradiologists. Copyright © 2022 Weyland, Papanagiotou, Schmitt, Joly, Bellot, Mokli, Ringleb, Kastrup, Möhlenbruch, Bendszus, Nagel and Herweh.
ER -