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Related Concept Videos

Brain Imaging01:14

Brain Imaging

536
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
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Related Experiment Video

Updated: Dec 7, 2025

Author Spotlight: Integrated Photoacoustic, Ultrasound, and Angiographic Tomography (PAUSAT) for NonInvasive Whole-Brain Imaging of Ischemic Stroke
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Artificial intelligence in stroke imaging: Current and future perspectives.

Vivek S Yedavalli1, Elizabeth Tong2, Dann Martin3

  • 1Stanford University, Department of Radiology, Division of Neuroradiology and Neurointervention, 300 Pasteur Drive, Room S047, Stanford, CA 94305, United States of America; Johns Hopkins Hospital, Department of Radiological Sciences, 600 N. Wolfe St. B 112-D, Baltimore, MD 21287, United States of America.

Clinical Imaging
|September 27, 2020
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) methods, particularly supervised machine learning, are advancing stroke diagnosis and management. These AI techniques enhance neuroimaging analysis for acute stroke patients, improving clinical workflows and outcomes.

Keywords:
Image optimization and analysisPerfusion imagingStrokeSupervised artificial intelligence

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Area of Science:

  • Computer Science
  • Medical Imaging
  • Radiology

Background:

  • Artificial intelligence (AI) mimics cognitive processes and is rapidly advancing.
  • Supervised machine learning (ML) analyzes high-dimensional data using labeled datasets.
  • ML is gaining traction in medicine, especially in radiology and neuroradiology, due to large data volumes.

Purpose of the Study:

  • To provide an overview of recent AI advancements in acute stroke.
  • To focus on supervised machine learning and deep learning applications.
  • To discuss AI's role in stroke diagnosis, management, and neuroimaging.

Main Methods:

  • Review of artificial intelligence techniques, focusing on supervised machine learning and deep learning.
  • Analysis of AI applications in stroke workflow, image acquisition/reconstruction, and interpretation.
  • Discussion of potential challenges and future directions for AI in stroke care.

Main Results:

  • AI, especially supervised ML, shows promise in analyzing neuroimaging data for stroke.
  • These methods can aid in image-based diagnosis and clinical management of acute stroke.
  • Recent advances cover workflow optimization, enhanced image acquisition, and improved interpretation.

Conclusions:

  • AI, particularly supervised ML, offers significant potential for improving acute stroke diagnosis and management.
  • Further research and development are needed to overcome potential pitfalls and fully realize AI's future applications in neuroradiology.
  • AI integration can enhance efficiency and accuracy in stroke care pathways.