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Related Experiment Video

Updated: Jul 26, 2025

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Automatic Alberta Stroke Program Early Computed Tomographic Scoring in patients with acute ischemic stroke using

Yan Wu1, Rong Sun1, Yuanzhong Xie2

  • 1School of Health Science and Engineering, University of Shanghai for Science and Technology, No. 516 Jun-Gong Road, Shanghai, 200093, China.

Medical & Biological Engineering & Computing
|June 22, 2023
PubMed
Summary
This summary is machine-generated.

Deep learning accurately scores Diffusion-Weighted Imaging Alberta Stroke Program Early Computed Tomographic Scoring (DWI-ASPECTS) for acute stroke, aiding rapid treatment decisions. The novel CBAM-VGG model enhances accuracy for small brain regions.

Keywords:
Acute ischemic strokeAlberta Stroke Program Early Computed Tomographic ScoringConvolutional neural networksDeep learningDiffusion weighted imaging

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence in Medicine
  • Stroke Imaging

Background:

  • Alberta Stroke Program Early Computed Tomographic Scoring (ASPECTS) is crucial for evaluating early ischemic changes in acute ischemic stroke.
  • Accurate ASPECTS scoring is vital for timely clinical decisions, particularly for endovascular thrombectomy.
  • Manual ASPECTS evaluation can be time-consuming and subject to inter-observer variability.

Purpose of the Study:

  • To develop and validate a deep learning-based automatic evaluation strategy for Diffusion-Weighted Imaging ASPECTS (DWI-ASPECTS).
  • To improve the accuracy of DWI-ASPECTS scoring, especially in small-volume regions.
  • To provide a reliable tool for clinicians to aid urgent decision-making in acute ischemic stroke.

Main Methods:

  • Extraction of ten ASPECTS regions from DWI series for independent classification network training.
  • Validation of classical convolutional neural networks (VGG-16, ResNet-50).
  • Development of a novel CBAM-VGG model to enhance scoring accuracy in four small-volume ASPECTS regions (caudate nucleus, lenticular nucleus, internal capsule, insular lobe).

Main Results:

  • Achieved an average F1-score of 0.929 for six cortical regions (M1-M6) and 0.840 for four small ASPECTS regions.
  • Obtained an average accuracy of 94.75% for cortical regions and 84.99% for small ASPECTS regions.
  • The CBAM-VGG model demonstrated significant improvement in estimating the accuracy of the four smaller-volume ASPECTS regions.

Conclusions:

  • Deep learning methods enhance the efficiency and robustness of automatic DWI-ASPECTS scoring.
  • The proposed automatic scoring strategy, particularly CBAM-VGG, offers a valuable reference for clinical decision-making in acute ischemic stroke.
  • Automated DWI-ASPECTS scoring can support faster and more consistent treatment planning for endovascular thrombectomy.