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

Impact of middle meningeal artery embolization timing on chronic subdural hematoma resolution and functional outcomes.

Journal of neuroradiology = Journal de neuroradiologie·2026
Same author

Temporal Immune and Metabolic Shifts Drive the Anti-Tumor Efficacy of Resiquimod-Loaded Nanoparticles in Peritoneal Carcinomatosis.

Advanced healthcare materials·2026
Same author

MAD-MT Score: A Tool to Optimize Patient Selection for Mechanical Thrombectomy in Distal Vessel Occlusions.

Stroke·2026
Same author

Real-World Safety Profile of Middle Meningeal Artery Embolization for Chronic Subdural Hematoma: a Multinational Multicenter Study.

Clinical neuroradiology·2026
Same author

Neuro Optical Coherence Tomography Guided Mechanical Thrombectomy.

Stroke·2026
Same author

Temporal relationship between hematoma resolution and functional recovery after middle meningeal artery embolization for chronic subdural hematoma.

Journal of neurosurgery·2026

Related Experiment Video

Updated: Jul 30, 2025

Three-Dimensional Printing of a Complex Aortic Anomaly
03:40

Three-Dimensional Printing of a Complex Aortic Anomaly

Published on: November 1, 2018

6.8K

Deep learning-based cerebral aneurysm segmentation and morphological analysis with three-dimensional rotational

Hidehisa Nishi1,2, Nicole M Cancelliere3,2, Ariana Rustici2

  • 1Department of Surgery, Division of Neurosurgery, St Michael's Hospital, Toronto, Ontario, Canada venturahighway83@gmail.com.

Journal of Neurointerventional Surgery
|May 16, 2023
PubMed
Summary

An automated model accurately assesses cerebral aneurysm morphology from angiography, improving on subjective manual evaluations. This AI tool aids in endovascular treatment planning for aneurysms.

Keywords:
aneurysmangiographyintervention

More Related Videos

A Volumetric Method for Quantification of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage
08:12

A Volumetric Method for Quantification of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage

Published on: July 28, 2018

8.1K
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

2.5K

Related Experiment Videos

Last Updated: Jul 30, 2025

Three-Dimensional Printing of a Complex Aortic Anomaly
03:40

Three-Dimensional Printing of a Complex Aortic Anomaly

Published on: November 1, 2018

6.8K
A Volumetric Method for Quantification of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage
08:12

A Volumetric Method for Quantification of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage

Published on: July 28, 2018

8.1K
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

2.5K

Area of Science:

  • Neurosurgery
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Cerebral aneurysm morphological assessment via angiography is crucial for endovascular treatment planning.
  • Manual evaluation by human raters shows moderate interrater and intrarater reliability.
  • Objective and reliable methods are needed to enhance treatment strategy and device selection.

Purpose of the Study:

  • To develop and validate an automated model for the morphological analysis of cerebral aneurysms using cerebral angiography data.
  • To assess the accuracy and reliability of the automated model in calculating key aneurysm parameters.

Main Methods:

  • Collected data from 889 cerebral angiograms (2017-2021).
  • Developed an automatic morphological analysis model on a derivation cohort (388 scans, 437 aneurysms).
  • Validated the model on a separate cohort (96 scans, 124 aneurysms), calculating volume, maximum size, neck size, height, and aspect ratio.

Main Results:

  • The model achieved high segmentation accuracy (Dice similarity index: 0.87, median 0.93).
  • All calculated morphological parameters significantly correlated with the reference standard (P<0.0001).
  • Small mean differences were observed for maximum aneurysm size (0.5±0.7 mm) and neck size (0.8±1.7 mm) compared to the reference standard.

Conclusions:

  • The developed automatic aneurysm analysis model demonstrates high accuracy in evaluating cerebral aneurysm morphology from angiography.
  • This AI-driven approach offers a reliable alternative to manual assessment, potentially improving endovascular treatment planning.