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Brain Imaging

Brain Imaging

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: Nov 28, 2025

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

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

Published on: June 2, 2023

Artificial Intelligence and Acute Stroke Imaging.

J E Soun¹, D S Chow^2,3, M Nagamine⁴

¹From the Departments of Radiological Sciences (J.E.S., D.S.C., P.D.C.).

AJNR. American Journal of Neuroradiology

|November 27, 2020

Summary

This summary is machine-generated.

Artificial intelligence (AI) enhances acute stroke imaging analysis for faster diagnosis and treatment. AI tools, particularly convolutional neural networks, improve detection, classification, and prognostication in stroke care.

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Area of Science:

Neurology
Medical Imaging
Artificial Intelligence

Background:

Acute stroke diagnosis requires rapid intervention to minimize patient disability and mortality.
Artificial intelligence (AI) offers advanced capabilities for analyzing complex medical images.
AI applications in stroke imaging are rapidly evolving, impacting clinical workflows.

Purpose of the Study:

To review AI methodologies and platforms used in stroke imaging.
To summarize current AI applications in acute stroke triage, surveillance, and prediction.
To highlight the potential of AI in improving stroke care outcomes.

Main Methods:

Review of current literature on AI in acute stroke imaging.
Description of AI techniques, including convolutional neural networks.
Identification of public and commercial AI platforms for stroke imaging.

Main Results:

AI demonstrates significant potential in infarct/hemorrhage detection, segmentation, and classification.
AI aids in large vessel occlusion detection and Alberta Stroke Program Early CT Score grading.
AI tools show promise for efficient and accurate image-based stroke assessment and prediction.

Conclusions:

AI technologies are transforming acute stroke imaging analysis.
AI facilitates earlier and more accurate stroke diagnosis and risk stratification.
Further integration of AI is expected to enhance stroke treatment paradigms and patient outcomes.