ArticleAnnals of neurology2022
External Validation of e-ASPECTS Software for Interpreting Brain CT in Stroke.
Article in Annals of neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of commercial artificial intelligence tools for the detection and volume quantification in intracerebral hemorrhage.European radiology · 2026Pooled it
- Artificial Intelligence in Ischemic Stroke Lesion Segmentation: A Narrative Review of Deep Learning Methods, Clinical Utility, and Future Directions.Journal of imaging informatics in medicine · 2026Review
- Artificial Intelligence That Changes Clinical Neurology Practice: Translating Algorithms Into Actionable Care.Journal of clinical neurology (Seoul, Korea) · 2026Review
- Artificial Intelligence and Emerging Digital Technologies Across the Stroke Continuum: From Risk Prediction to Real-Time Monitoring and Rapid Response.Medicina (Kaunas, Lithuania) · 2026Review
- Clinical evaluation of an automated Alberta Stroke Program Early Computed Tomography Score (ASPECTS)-scoring system.Quantitative imaging in medicine and surgery · 2026Article
- Transforming Stroke Diagnosis with Artificial Intelligence: A Scoping Review of Brainomix e-Stroke, Aidoc, RapidAI, and Viz.ai.Medicina (Kaunas, Lithuania) · 2026Article
- Artificial intelligence in acute and critical care: current challenges and strategic solutions.Frontiers in public health · 2026Review
- Human Readers versus AI-Based Systems in ASPECTS Scoring for Acute Ischemic Stroke: A Systematic Review and Meta-Analysis with Region-Specific Guidance.ASIDE internal medicine · 2025Article
- Development of a deep learning method to identify acute ischaemic stroke lesions on brain CT.Stroke and vascular neurology · 2025Article
- AI-Based Automated Quantification of Arterial Stenosis in Head and Neck CT Angiography: A Comparison with Manual Measurements from Digital Subtraction Angiography and CT Angiography.Clinical neuroradiology · 2025Article
- Artificial Intelligence in Vascular Neurology: Applications, Challenges, and a Review of AI Tools for Stroke Imaging, Clinical Decision Making, and Outcome Prediction Models.Current neurology and neuroscience reports · 2025Review
- Assessing the eligibility of Brainomix e-ASPECTS for acute stroke imaging.Frontiers in neuroinformatics · 2025Article
- Deep learning biomarker of chronometric and biological ischemic stroke lesion age from unenhanced CT.NPJ digital medicine · 2024Article
- Real-world evaluation of Brainomix e-Stroke software.Stroke and vascular neurology · 2024Article
- A Comparative Study of AI-Based Automated and Manual Postprocessing of Head and Neck CT Angiography: An Independent External Validation with Multi-Vendor and Multi-Center Data.Neuroradiology · 2024Article
- Performance of Automated ASPECTS Software and Value as a Computer-Aided Detection Tool.AJNR. American journal of neuroradiology · 2023Article
- Accuracy of artificial intelligence software for CT angiography in stroke.Annals of clinical and translational neurology · 2023Article
- What's the Future of Vascular Neurology?Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2023Review
- Impact of e-ASPECTS software on the performance of physicians compared to a consensus ground truth: a multi-reader, multi-case study.Frontiers in neurology · 2023Article
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Authors and funding
13 authors.
Funding
Abstract
objectiveThe purpose of this study was 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 Alberta Stroke Program Early CT Score (ASPECTS).
methodsUsing CT from 9 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 the 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, and hemorrhage) in the representative population.
resultsWe included 4,100 patients (51% women, median age = 78 years, National Institutes of Health Stroke Scale [NIHSS] = 10, onset to scan = 2.5 hours). Final diagnosis was ischemia (78%), hemorrhage (14%), or mimic (8%). From 3,035 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 [CI] = 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.
interpretationOn independent testing, e-ASPECTS provided moderate agreement with experts and overcalled stroke features. Therefore, future prospective trials testing impacts of artificial intelligence (AI) software on patient care and outcome are required before widespread implementation of stroke decision-support software. ANN NEUROL 2022;92:943-957.
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