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DefinitionRenal angiography, also known as renal arteriography, is an imaging technique used to obtain a comprehensive view of blood flow and the vascular structure of blood vessels in the kidneys and surrounding areas.PurposeRenal angiography detects blood vessel abnormalities in the kidneys, such as aneurysms, stenosis, thrombosis, vascular tumors, and renal artery stenosis. It evaluates kidney function and guides interventional treatments like angioplasty or stent placement.Pre-Procedure...
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Related Experiment Video

Updated: Nov 5, 2025

Author Spotlight: Integrated Photoacoustic, Ultrasound, and Angiographic Tomography (PAUSAT) for NonInvasive Whole-Brain Imaging of Ischemic Stroke
06:45

Author Spotlight: Integrated Photoacoustic, Ultrasound, and Angiographic Tomography (PAUSAT) for NonInvasive Whole-Brain Imaging of Ischemic Stroke

Published on: June 2, 2023

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Leveraging artificial intelligence in ischemic stroke imaging.

Omid Shafaat1, Joshua D Bernstock2, Amir Shafaat3

  • 1The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287, USA.

Journal of Neuroradiology = Journal De Neuroradiologie
|May 13, 2021
PubMed
Summary

Artificial intelligence (AI) enhances clinical medicine, particularly for ischemic stroke diagnosis and management. This review details AI applications in stroke imaging, prognosis, and treatment selection for clinicians.

Keywords:
Artificial intelligenceBrain ischemiaMachine learningNeural networkStroke

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Area of Science:

  • Clinical Medicine
  • Medical Imaging
  • Neurology

Background:

  • Artificial intelligence (AI) is transforming clinical medicine.
  • Prompt diagnosis and imaging are crucial for reducing ischemic stroke morbidity and mortality.
  • Clinicians need to understand AI's capabilities and limitations for optimal patient care.

Purpose of the Study:

  • To review AI applications in the clinical management of ischemic stroke.
  • To explain basic AI and machine learning concepts to clinicians.
  • To summarize AI's role in stroke imaging, prognosis prediction, and treatment selection.

Main Methods:

  • Review of current literature on AI in ischemic stroke management.
  • Classification of AI applications into specific clinical areas.
  • Explanations of AI concepts tailored for a non-technical clinical audience.

Main Results:

  • AI is extensively applied in ischemic stroke evaluation.
  • Key AI applications include automated brain infarction diagnosis, ASPECT score calculation, and infarction segmentation.
  • AI also aids in prognosis prediction and patient selection for stroke treatments.

Conclusions:

  • AI offers significant potential to improve ischemic stroke diagnosis and management.
  • Understanding AI tools is essential for clinicians to leverage its benefits effectively.
  • This review provides a clinician-focused overview of AI in stroke care.