ArticleNeuroradiology2020
Automated ASPECT scoring in acute ischemic stroke: comparison of three software tools.
Article in Neuroradiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it, 60 citations in OpenAlex.
- Software with artificial intelligence-derived algorithms for analysing CT brain scans in people with a suspected acute stroke: a systematic review and cost-effectiveness analysis.Health technology assessment (Winchester, England) · 2024Pooled it
- Clinical evaluation of a deep-learning model for automatic scoring of the Alberta stroke program early CT score on non-contrast CT.Journal of neurointerventional surgery · 2023Trial
- Refined AI-ASPECTS with modified atlas and lesion-load thresholds: advancing acute ischemic stroke imaging and prognostic prediction.BMC medicine · 2026Article
- Spectral CTA-based quantitative differentiation of infarcted and non-infarcted ASPECTS regions in acute stroke.European radiology experimental · 2026Article
- Transforming Stroke Diagnosis with Artificial Intelligence: A Scoping Review of Brainomix e-Stroke, Aidoc, RapidAI, and Viz.ai.Medicina (Kaunas, Lithuania) · 2026Article
- Current State of the Clinical Applications of Artificial Intelligence in Stroke: A Literature Review.Brain sciences · 2026Review
- Performance of an artificial intelligence tool for multi-step acute stroke imaging: A multicenter diagnostic study.European journal of radiology open · 2025Article
- 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
- Review
- Deep Learning-Based ASPECTS Algorithm Enhances Reader Performance and Reduces Interpretation Time.AJNR. American journal of neuroradiology · 2025Article
- Stroke Diagnosis and Prediction Tool Using ChatGLM: Development and Validation Study.Journal of medical Internet research · 2025Article
- Deep learning-based automatic ASPECTS calculation can improve diagnosis efficiency in patients with acute ischemic stroke: a multicenter study.European radiology · 2025Article
- Automated CT angiography collateral scoring in anterior large vessel occlusion stroke: A multireader study.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2025Article
- Assessing the eligibility of Brainomix e-ASPECTS for acute stroke imaging.Frontiers in neuroinformatics · 2025Article
- Current Stroke Solutions Using Artificial Intelligence: A Review of the Literature.Brain sciences · 2024Review
- Real-world evaluation of Brainomix e-Stroke software.Stroke and vascular neurology · 2024Article
- Automated ASPECTS calculation may equal the performance of experienced clinicians: a machine learning study based on a large cohort.European radiology · 2024Article
- Application of Artificial Intelligence in Acute Ischemic Stroke: A Scoping Review.Neurointervention · 2024Review
- Automated detection of early signs of irreversible ischemic change on CTA source images in patients with large vessel occlusion.PloS one · 2024Article
- Artificial intelligence applied in acute ischemic stroke: from child to elderly.La Radiologia medica · 2024Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeVarious software applications offer support in the diagnosis of acute ischemic stroke (AIS), yet it remains unclear whether the performance of these tools is comparable to each other. Our study aimed to evaluate three fully automated software applications for Alberta Stroke Program Early CT (ASPECT) scoring (Syngo.via Frontier ASPECT Score Prototype V2, Brainomix e-ASPECTS® and RAPID ASPECTS) in AIS patients.
methodsRetrospectively, 131 patients with large vessel occlusion (LVO) of the middle cerebral artery or the internal carotid artery, who underwent endovascular therapy (EVT), were included. Pre-interventional non-enhanced CT (NECT) datasets were assessed in random order using the automated ASPECT software and by three experienced neuroradiologists in consensus. Interclass correlation coefficient (ICC), Bland-Altman, and receiver operating characteristics (ROC) were applied for statistical analysis.
resultsMedian ASPECTS of the expert consensus reading was 8 (7-10). Highest correlation was between the expert read and Brainomix (r = 0.871 (0.818, 0.909), p < 0.001). Correlation between expert read and Frontier V2 (r = 0.801 (0.719, 0.859), p < 0.001) and between expert read and RAPID (r = 0.777 (0.568, 0.871), p < 0.001) was high, respectively. There was a high correlation among the software tools (Frontier V2 and Brainomix: r = 0.830 (0.760, 0.880), p < 0.001; Frontier V2 and RAPID: r = 0.847 (0.693, 0.913), p < 0.001; Brainomix and RAPID: r = 0.835 (0.512, 0.923), p < 0.001). An ROC curve analysis revealed comparable accuracy between the applications and expert consensus reading (Brainomix: AUC = 0.759 (0.670-0.848), p < 0.001; Frontier V2: AUC = 0.752 (0.660-0.843), p < 0.001; RAPID: AUC = 0.734 (0.634-0.831), p < 0.001).
conclusionOverall, there is a convincing yet developable grade of agreement between current ASPECT software evaluation tools and expert evaluation with regard to ASPECT assessment in AIS.
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