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

You might also read

Related Articles

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

Sort by
Same author

Routine admission biomarkers for identifying co-infection and stratifying severity in pediatric macrolide-resistant <i>Mycoplasma pneumoniae</i> pneumonia: a retrospective cohort study.

Translational pediatrics·2026
Same author

Radiomics using contrast-enhanced T1-weighted imaging and clinical features for predicting response to EGFR-TKIs in EGFR-mutated non-small cell lung cancer patients with brain metastases.

BMC medical imaging·2026
Same author

Two-stage robust 3D CTA-2D DSA alignment via vascular-aware rigid and pyramid-based hierarchical non-rigid registration.

Medical image analysis·2026
Same author

Chlorine-induced severe ARDS in an adolescent rescued with VV-ECMO: a case report with 6-month functional follow-up.

Frontiers in pediatrics·2026
Same author

Magnetic resonance imaging characteristics of brain metastases from lung cancer.

Quantitative imaging in medicine and surgery·2026
Same author

Disturbed flow induced targeting of nanomedicine to endothelial cells for effective atherosclerosis therapy.

Cardiovascular research·2026

Related Experiment Video

Updated: Jun 10, 2025

Assessing Cortical Cerebral Microinfarcts on High Resolution MR Images
08:39

Assessing Cortical Cerebral Microinfarcts on High Resolution MR Images

Published on: November 20, 2015

13.4K

Enhancing Radiologists' Performance in Detecting Cerebral Aneurysms Using a Deep Learning Model: A Multicenter Study.

Liyong Zhuo1, Yu Zhang1, Zijun Song2

  • 1Department of Radiology, Affiliated Hospital of Hebei University, Baoding, PR China (L.Z., Y.Z., L.X., F.Z., H.M., J.W., X.Y.).

Academic Radiology
|October 15, 2024
PubMed
Summary

A deep learning (DL) model improved cerebral aneurysm detection by radiologists, significantly boosting diagnostic accuracy and reducing interpretation times. This AI tool enhances workflow efficiency and diagnostic performance, particularly for junior radiologists.

Keywords:
Computer-aided diagnosisDeep learningIntracranial aneurysmTomographyX-ray computed

More Related Videos

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

2.3K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.5K

Related Experiment Videos

Last Updated: Jun 10, 2025

Assessing Cortical Cerebral Microinfarcts on High Resolution MR Images
08:39

Assessing Cortical Cerebral Microinfarcts on High Resolution MR Images

Published on: November 20, 2015

13.4K
Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

2.3K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.5K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Neurology

Background:

  • Cerebral aneurysms pose significant diagnostic challenges.
  • Accurate and timely detection is crucial for patient outcomes.
  • Existing diagnostic methods can be time-consuming and require expert interpretation.

Purpose of the Study:

  • To develop and evaluate a deep learning (DL) model for detecting and diagnosing cerebral aneurysms.
  • To assess the impact of DL assistance on radiologist performance and workflow efficiency.
  • To compare diagnostic accuracy with and without DL model support.

Main Methods:

  • A DL model was trained on data from 3829 patients across 11 centers and tested on 484 patients from three institutions.
  • Image interpretations were performed by junior and senior radiologists, the DL model alone, and a combination of radiologists with DL assistance.
  • Diagnostic performance metrics (AUC, sensitivity, specificity) and time spent on interpretation and post-processing were analyzed.

Main Results:

  • Combining DL model assistance with radiologists reduced image interpretation time by 37.2% and post-processing time by 90.8%.
  • The DL model significantly improved the area under the curve (AUC) for both junior (0.842 to 0.881) and senior radiologists (0.853 to 0.895).
  • Sensitivity and specificity for cerebral aneurysm detection were notably enhanced with DL model assistance, especially for junior radiologists.

Conclusions:

  • The deep learning model significantly enhances radiologists' diagnostic performance in detecting cerebral aneurysms.
  • DL assistance leads to a more efficient workflow by reducing interpretation and post-processing times.
  • The study highlights the potential of AI to augment clinical decision-making in neuroimaging.