ArticleAJNR. American journal of neuroradiology2019
Automated ASPECTS on Noncontrast CT Scans in Patients with Acute Ischemic Stroke Using Machine Learning.
Article in AJNR. American journal of neuroradiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 4 of them syntheses 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.
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Who cites it
52 citing papers in PubMed, 4 syntheses or guidelines pooled it, 123 citations in OpenAlex.
- Machine learning models of segmentation in acute ischemic stroke: a systematic review and meta-analysis.Biomedical engineering online · 2026Pooled it
- To Bridge or Not to Bridge? A Meta-Analysis of Intravenous Thrombolysis Before Thrombectomy in Large Ischemic Core Strokes.Brain and behavior · 2025Pooled it
- A 25-Year Retrospective of the Use of AI for Diagnosing Acute Stroke: Systematic Review.Journal of medical Internet research · 2024Pooled it
- 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
- Radiomics analysis for predicting malignant cerebral edema in patients undergoing endovascular treatment for acute ischemic strokeDiagnostic and interventional radiology (Ankara, Turkey) · 2023Trial
- Predictive values of early head computed tomography for survival outcome after cardiac arrest in childhood: a pilot study.Scientific reports · 2021Trial
- Refined AI-ASPECTS with modified atlas and lesion-load thresholds: advancing acute ischemic stroke imaging and prognostic prediction.BMC medicine · 2026Article
- Robust slice-level stroke classification in non-contrast head CT via structural consistency regularization and counterfactual suppression.Frontiers in bioengineering and biotechnology · 2026Article
- Evaluation of ChatGPT-5 for Automated ASPECTS Assessment on Non-Contrast CT in Acute Ischemic Stroke.Diagnostics (Basel, Switzerland) · 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
- The Role of Artificial Intelligence in Stroke Imaging in Emergency Settings: A Systematic Review.Cureus · 2025Review
- Fusion Learning from Non-contrast CT Scans for the Detection of Hemorrhagic Transformation in Stroke Patients.Journal of imaging informatics in medicine · 2025Article
- Predicting Ischemic Stroke Patients to Transfer for Endovascular Thrombectomy Using Machine Learning: A Case Study.Healthcare (Basel, Switzerland) · 2025Article
- Core-Penumbra Hyperacute Ischemic Stroke Dataset.Scientific data · 2025Article
- Automated ASPECTS Segmentation and Scoring Tool: a Method Tailored for a Colombian Telestroke Network.Journal of imaging informatics in medicine · 2025Article
- Non-Contrast Computed Tomography-Based Triage and Notification for Large Vessel Occlusion Stroke: A Before and After Study Utilizing Artificial Intelligence on Treatment Times and Outcomes.Journal of clinical medicine · 2025Article
- Deep learning-based automatic ASPECTS calculation can improve diagnosis efficiency in patients with acute ischemic stroke: a multicenter study.European radiology · 2025Article
- Taxonomy of Acute Stroke: Imaging, Processing, and Treatment.Diagnostics (Basel, Switzerland) · 2024Review
- Application of radiomics in ischemic stroke.The Journal of international medical research · 2024Review
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 4 countries.
Funding
Abstract
background and purposeAlberta Stroke Program Early CT Score (ASPECTS) was devised as a systematic method to assess the extent of early ischemic change on noncontrast CT (NCCT) in patients with acute ischemic stroke (AIS). Our aim was to automate ASPECTS to objectively score NCCT of AIS patients. MATERIALS AND
methodsWe collected NCCT images with a 5-mm thickness of 257 patients with acute ischemic stroke (<8 hours from onset to scans) followed by a diffusion-weighted imaging acquisition within 1 hour. Expert ASPECTS readings on DWI were used as ground truth. Texture features were extracted from each ASPECTS region of the 157 training patient images to train a random forest classifier. The unseen 100 testing patient images were used to evaluate the performance of the trained classifier. Statistical analyses on the total ASPECTS and region-level ASPECTS were conducted.
resultsFor the total ASPECTS of the unseen 100 patients, the intraclass correlation coefficient between the automated ASPECTS method and DWI ASPECTS scores of expert readings was 0.76 (95% confidence interval, 0.67-0.83) and the mean ASPECTS difference in the Bland-Altman plot was 0.3 (limits of agreement, -3.3, 2.6). Individual ASPECTS region-level analysis showed that our method yielded κ = 0.60, sensitivity of 66.2%, specificity of 91.8%, and area under curve of 0.79 for 100 × 10 ASPECTS regions. Additionally, when ASPECTS was dichotomized (>4 and ≤4), κ = 0.78, sensitivity of 97.8%, specificity of 80%, and area under the curve of 0.89 were generated between the proposed method and expert readings on DWI.
conclusionsThe proposed automated ASPECTS scoring approach shows reasonable ability to determine ASPECTS on NCCT images in patients presenting with acute ischemic stroke.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.