Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Computed Tomography01:10

Computed Tomography

6.3K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
6.3K
Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

68
Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
68

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Neuromelanin-sensitive MRI for identifying dopamine transporter imaging abnormality risk in idiopathic rapid eye movement sleep behavior disorder.

Brain imaging and behavior·2026
Same author

Simulation-Guided Optimization of NH<sub>3</sub>/H<sub>2</sub> Cocombustion over a CuO Catalyst: Achieving High-Efficiency and near-Zero NO<sub><i>x</i></sub> Emissions.

Environmental science & technology·2026
Same author

Seizure following epidural blood patch in spontaneous intracranial hypotension complicated by subdural hematoma: Illustrated case and literature review.

Surgical neurology international·2026
Same author

Intensive Versus Conventional Blood Pressure Lowering After Successful Endovascular Thrombectomy: OPTIMAL-BP 1-Year Outcomes.

Stroke·2026
Same author

Omega-3 Fatty Acids Attenuate Renal Myostatin Expression and Mitochondrial Alterations Under Uremic Conditions.

International journal of molecular sciences·2026
Same author

Refining endovascular thrombectomy for large vessel occlusion in active cancer: predictors of death despite successful recanalization.

Journal of neurointerventional surgery·2026

Related Experiment Video

Updated: Sep 16, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K

Using a Deep Learning-Based Decision Support System to Predict Emergent Large Vessel Occlusion Using Non-Contrast

Seong-Joon Lee1, Dohyun Kim2, Dae Han Choi3

  • 1Department of Neurology, Ajou University School of Medicine, 164 World Cup-ro, Yeongtong-gu, Suwon-si 16499, Gyeonggi-do, Republic of Korea.

Journal of Clinical Medicine
|July 12, 2025
PubMed
Summary

An artificial intelligence (AI) system significantly improved the detection of emergent large vessel occlusion (ELVO) in brain CT scans. This AI tool enhances clinician accuracy, aiding in faster stroke treatment decisions.

Keywords:
artificial intelligenceneurologyreperfusionstrokethrombectomy

More Related Videos

Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging
09:32

Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging

Published on: December 9, 2021

3.1K
Optimized Management of Endovascular Treatment for Acute Ischemic Stroke
09:21

Optimized Management of Endovascular Treatment for Acute Ischemic Stroke

Published on: January 18, 2018

12.1K

Related Experiment Videos

Last Updated: Sep 16, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K
Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging
09:32

Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging

Published on: December 9, 2021

3.1K
Optimized Management of Endovascular Treatment for Acute Ischemic Stroke
09:21

Optimized Management of Endovascular Treatment for Acute Ischemic Stroke

Published on: January 18, 2018

12.1K

Area of Science:

  • Neurology
  • Radiology
  • Medical Imaging

Background:

  • Retrospective, multi-reader, blinded trial evaluating an AI-based clinical decision support system.
  • Focus on improving clinician detection of emergent large vessel occlusion (ELVO) using brain non-contrast computed tomography (NCCT).

Purpose of the Study:

  • To assess the performance of an AI system in detecting ELVO compared to unassisted clinician readings.
  • To evaluate the impact of AI assistance on diagnostic accuracy metrics like sensitivity, specificity, and AUROC.

Main Methods:

  • 477 patients enrolled; 112 with anterior circulation ELVO, 365 controls.
  • Clinicians performed unassisted and AI-assisted readings of NCCT images after a 2-week washout period.
  • Primary endpoints: sensitivity and specificity; Secondary endpoints: AUROC and individual-level accuracy.

Main Results:

  • AI-assisted readings significantly improved sensitivity (92.0% vs 75.9%) and specificity (92.6% vs 83.0%) compared to unassisted readings (p < 0.01).
  • AI assistance also increased accuracy (92.5% vs 81.3%) and AUROC (0.95 vs 0.87) (p < 0.01).
  • The AI system demonstrated high standalone performance with 88.4% sensitivity and 91.2% specificity.

Conclusions:

  • AI-based clinical decision support systems enhance the detection of ELVO on NCCT scans.
  • AI can facilitate acute stroke reperfusion therapy by improving patient triage, especially in centers without thrombectomy capabilities.