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Related Concept Videos

Arteries of the Head and Neck01:26

Arteries of the Head and Neck

The human body's intricate network of arteries ensures that every organ system receives the necessary oxygen and nutrients for optimal function. The arterial network in the head and neck region is particularly complex, providing vital blood flow to the brain, eyes, and other critical structures. Prominent arteries in this region include the internal carotid arteries and the vertebral arteries.
The internal carotid arteries supply blood to the anterior portion of the cerebrum. They enter the...

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High-Risk Carotid Lesion Segmentation: Advancing Stroke Risk Detection With Deep Learning.

Dylan Fischer1, Claire Webster2, Fabien Lareyre3

  • 1Department of Vascular Surgery, Bordeaux University Hospital, Bordeaux, France.

Journal of Endovascular Therapy : an Official Journal of the International Society of Endovascular Specialists
|December 1, 2025
PubMed
Summary

Artificial intelligence (AI) software accurately segments carotid lesions on CT angiography, differentiating plaque composition. This AI tool aids in identifying high-risk asymptomatic plaques, improving surgical decision-making for carotid artery disease.

Keywords:
artificial intelligencecarotid artery stenosisdeep learningsegmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Cardiovascular Disease Research

Background:

  • Carotid artery disease management is debated for asymptomatic lesions.
  • Plaque characteristics may predict events better than stenosis alone.
  • AI feasibility for carotid lesion segmentation is investigated.

Purpose of the Study:

  • To assess AI software (PRAEVAorta2) for segmenting carotid lesions on CT angiography.
  • To compare plaque composition (thrombus, calcium) between symptomatic and asymptomatic patients.
  • To evaluate AI segmentation accuracy against manual segmentation.

Main Methods:

  • Two segmentation methods: manual and AI-based (PRAEVAorta2).
  • Analysis of thrombus, calcium, and lumen volume in 156 patients.
  • Evaluation of AI performance using DSC, sensitivity, specificity, and reliability metrics.

Main Results:

  • AI segmentation showed strong agreement with manual segmentation for lumen, calcification, and plaque.
  • Symptomatic lesions had higher thrombus volume and total lesion volume.
  • Asymptomatic lesions showed higher calcification-to-total volume ratios.
  • AI demonstrated high intra- and inter-observer reliability.

Conclusions:

  • PRAEVAorta2 effectively automates carotid lesion analysis on CT angiography.
  • AI aids in identifying high-risk asymptomatic plaques, supporting surgical decisions.
  • Plaque composition differs significantly between symptomatic and asymptomatic carotid lesions.