ArticleBiomedicines2023
Automated Collateral Scoring on CT Angiography of Patients with Acute Ischemic Stroke Using Hybrid CNN and Transformer Network.
Article in Biomedicines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Comparison of AI-assisted quantitative collateral score and visual collateral score in stroke thrombectomy triage.Neuroradiology · 2026Article
- Automated Collateral Classification on CT Angiography in Acute Ischemic Stroke: Performance Trends Across Hyperparameter Combinations.Bioengineering (Basel, Switzerland) · 2026Article
- Automated collateral assessment restricted to the hypoperfused area for distal vessel occlusions in ischemic stroke.European radiology · 2025Article
- Automatic collateral quantification in acute ischemic stroke using UFrontiers in neurology · 2025Article
- Radiomics-Based Prediction of Collateral Status from CT Angiography of Patients Following a Large Vessel Occlusion Stroke.Diagnostics (Basel, Switzerland) · 2024Article
- Inter-Rater Reliability of Collateral Status Assessment Based on CT Angiography: A Retrospective Study of Middle Cerebral Artery Ischaemic Stroke.Journal of clinical medicine · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Collateral scoring plays an important role in diagnosis and treatment decisions of acute ischemic stroke (AIS). Most existing automated methods rely on vessel prominence and amount after vessel segmentation. The purpose of this study was to design a vessel-segmentation free method for automating collateral scoring on CT angiography (CTA). We first processed the original CTA via maximum intensity projection (MIP) and middle cerebral artery (MCA) region segmentation. The obtained MIP images were fed into our proposed hybrid CNN and Transformer model (MPViT) to automatically determine the collateral scores. We collected 154 CTA scans of patients with AIS for evaluation using five-folder cross validation. Results show that the proposed MPViT achieved an intraclass correlation coefficient of 0.767 (95% CI: 0.68-0.83) and a Kappa of 0.6184 (95% CI: 0.4954-0.7414) for three-point collateral score classification. For dichotomized classification (good vs. non-good and poor vs. non-poor), it also achieved great performance.
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Registered trials
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