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Updated: Dec 30, 2025

Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging
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Automatic Collateral Scoring From 3D CTA Images.

Jiahang Su, Lennard Wolff, Adriaan C G M van Es

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    Summary
    This summary is machine-generated.

    This study developed an automated method for collateral scoring in ischemic stroke patients, improving treatment decisions. The AI model achieved high accuracy, comparable to experienced radiologists, reducing subjective interpretation.

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    Area of Science:

    • Neurology
    • Medical Imaging
    • Artificial Intelligence

    Background:

    • Collateral scoring is crucial for endovascular treatment (EVT) decisions in ischemic stroke.
    • Current visual-based grading systems suffer from subjectivity and interobserver variability.
    • There is a need for objective and reproducible collateral assessment methods.

    Purpose of the Study:

    • To develop an automated collateral scoring method for ischemic stroke patients.
    • To create a reproducible and objective alternative to subjective visual assessment.
    • To improve decision-making for endovascular treatment.

    Main Methods:

    • Developed an AI-based method inspired by human collateral scoring.
    • Utilized atlas-based registration for anatomical region definition.
    • Employed deep convolutional neural networks for vessel structure extraction.
    • Defined high-level features based on vessel length and volume ratios.
    • Used multi-class classification models for collateral score prediction.

    Main Results:

    • The automated method achieved an accuracy of 0.8 for a four-grade collateral score.
    • Dichotomized collateral score accuracy reached 0.9.
    • Model performance demonstrated error rates comparable to interobserver variation.
    • Results were on par with experienced radiologists (10-30 years of experience).

    Conclusions:

    • The developed automated collateral scoring method is accurate and reproducible.
    • This AI-driven approach can aid in objective decision-making for endovascular treatment.
    • The method shows potential to reduce subjectivity and improve consistency in clinical practice.