RT Journal Article
SR Electronic
T1 AI software detection of large vessel occlusion stroke on CT angiography: a real-world prospective diagnostic test accuracy study
JF Journal of NeuroInterventional Surgery
JO J NeuroIntervent Surg
FD BMJ Publishing Group Ltd.
SP 52
OP 56
DO 10.1136/neurintsurg-2021-018391
VO 15
IS 1
A1 Stavros Matsoukas
A1 Jacob Morey
A1 Gregory Lock
A1 Deeksha Chada
A1 Tomoyoshi Shigematsu
A1 Naoum Fares Marayati
A1 Bradley N Delman
A1 Amish Doshi
A1 Shahram Majidi
A1 Reade De Leacy
A1 Christopher Paul Kellner
A1 Johanna T Fifi
YR 2023
UL http://jnis.bmj.com/content/15/1/52.abstract
AB Background Artificial intelligence (AI) software is increasingly applied in stroke diagnostics. However, the actual performance of AI tools for identifying large vessel occlusion (LVO) stroke in real time in a real-world setting has not been fully studied.Objective To determine the accuracy of AI software in a real-world, three-tiered multihospital stroke network.Methods All consecutive head and neck CT angiography (CTA) scans performed during stroke codes and run through an AI software engine (Viz LVO) between May 2019 and October 2020 were prospectively collected. CTA readings by radiologists served as the clinical reference standard test and Viz LVO output served as the index test. Accuracy metrics were calculated.Results Of a total of 1822 CTAs performed, 190 occlusions were identified; 142 of which were internal carotid artery terminus (ICA-T), middle cerebral artery M1, or M2 locations. Accuracy metrics were analyzed for two different groups: ICA-T and M1 ±M2. For the ICA-T/M1 versus the ICA-T/M1/M2 group, sensitivity was 93.8% vs 74.6%, specificity was 91.1% vs 91.1%, negative predictive value was 99.7% vs 97.6%, accuracy was 91.2% vs 89.8%, and area under the curve was 0.95 vs 0.86, respectively. Detection rates for ICA-T, M1, and M2 occlusions were 100%, 93%, and 49%, respectively. As expected, the algorithm offered better detection rates for proximal occlusions than for mid/distal M2 occlusions (58% vs 28%, p=0.03).Conclusions These accuracy metrics support Viz LVO as a useful adjunct tool in stroke diagnostics. Fast and accurate diagnosis with high negative predictive value mitigates missing potentially salvageable patients.Data are available upon reasonable request. N/A.