External validation of e-ASPECTS software for interpreting brain CT in stroke

Mair, G. et al. (2022) External validation of e-ASPECTS software for interpreting brain CT in stroke. Annals of Neurology, 92(6), pp. 943-957. (doi: 10.1002/ana.26495) (PMID:36053916)

Abstract

Objective: To test e-ASPECTS software in patients with stroke. Marketed as a decision-support tool, e-ASPECTS may detect features of ischemia or hemorrhage on computed tomography (CT) imaging and quantify ischemic extent using ASPECTS (Alberta Stroke Program Early CT Score). Methods: Using CT from nine stroke studies, we compared software with masked experts. As per indications for software use, we assessed e-ASPECTS results for patients with/without middle cerebral artery (MCA) ischemia but no other cause of stroke. In an analysis outside the intended use of software, we enriched our dataset with non-MCA ischemia, hemorrhage, and mimics to simulate a representative ‘front door’ hospital population. With final diagnosis as the reference standard, we tested the diagnostic accuracy of e-ASPECTS for identifying stroke features (ischemia, hyperattenuated arteries, hemorrhage) in the representative population. Results: We included 4100 patients (51% female, median age 78 years, NIHSS 10, onset to scan 2·5 hours). Final diagnosis was ischemia (78%), hemorrhage (14%), or mimic (8%). From 3035 CTs with expert-rated ASPECTS, most (2084/3035, 69%) e-ASPECTS results were within one point of experts. In the representative population, the diagnostic accuracy of e-ASPECTS was 71% (95% confidence interval, 70-72%) for detecting ischemic features, 85% (83-86%) for hemorrhage. Software identified more false positive ischemia (12% vs 2%) and hemorrhage (14% vs <1%) than experts. Interpretation: On independent testing, e-ASPECTS provided moderate agreement with experts and overcalled stroke features. Therefore, future prospective trials testing impacts of AI software on patient care and outcome are required before widespread implementation of stroke decision-support software.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Muir, Professor Keith
• Authors: Mair, G., White, P., Bath, P. M., Muir, K. W., Salman, R. A.-S., Martin, C., Dye, D., Chappell, F. M., Vacek, A., von Kummer, R., Macleod, M., Sprigg, N., and Wardlaw, J. M.
• College/School: College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
• Journal Name: Annals of Neurology
• Publisher: Wiley
• ISSN: 0364-5134
• ISSN (Online): 1531-8249
• Published Online: 31 August 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in Annals of Neurology 92(6): 943-957
• Publisher Policy: Reproduced under a Creative Commons License