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Automated Collateral Classification on CT Angiography in Acute Ischemic Stroke: Performance Trends Across

Chi-Ming Ku1, Tzong-Rong Ger2

  • 1Department of Biomedical Engineering, Chung Yuan Christian University, Taoyuan 320314, Taiwan.

Bioengineering (Basel, Switzerland)
|January 28, 2026
PubMed
Summary
This summary is machine-generated.

Automated collateral classification using convolutional neural networks (CNNs) on CT angiography (CTA) images improves accuracy for acute ischemic stroke (AIS) patients. This AI approach offers tailored models for sensitivity, specificity, or balanced decision-making in clinical triage.

Keywords:
CT angiographycollateral classificationcollateral of acute ischemic strokehyperparameter analysisvessel segmentation

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Area of Science:

  • Neurology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Collateral status is crucial for acute ischemic stroke (AIS) treatment decisions.
  • Current visual collateral grading from CT angiography (CTA) is subjective and inconsistent.

Purpose of the Study:

  • To develop and evaluate a convolutional neural network (CNN) for automated collateral classification from CTA images.
  • To analyze performance trends across various CNN architectures for tailored clinical application.

Main Methods:

  • Extracted binarized vascular morphological features from CTA images of 157 AIS patients.
  • Developed and systematically analyzed 192 CNN architectures.
  • Stratified patients into good (Menon score 3-5) and poor (Menon score 0-2) collateral groups.

Main Results:

  • Achieved AUCs ranging from 0.753 to 0.773.
  • Identified three model tendencies: sensitivity-oriented (87.18% sensitivity, 65.00% specificity), balanced (72.65% sensitivity, 77.50% specificity), and specificity-oriented (63.25% sensitivity, 85.00% specificity).
  • Key determinants of performance directionality include kernel size, filters in the first layer, and number of convolutional layers.

Conclusions:

  • Automated collateral classification from CTA using CNNs is feasible and overcomes subjectivity.
  • Systematic analysis provides a framework for developing models optimized for specific clinical needs (sensitivity, specificity, or balance).
  • Findings support potential integration into AI systems for endovascular thrombectomy patient triage.