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Related Concept Videos

Aneurysm I: Introduction01:30

Aneurysm I: Introduction

An aortic aneurysm is a localized outpouching or dilation at a weak point in the artery wall. It may involve different parts of the aorta, such as the abdominal aorta, aortic arch, or thoracic aorta.Etiological factorsSeveral disorders are associated with aortic aneurysms.Congenital causes, such as primary connective tissue disorders like Marfan syndrome, impact the integrity and strength of connective tissues, notably affecting the aorta. Marfan syndrome is a genetic disorder that specifically...

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A Method to Study the Correlation Between Local Collagen Structure and Mechanical Properties of Atherosclerotic Plaque Fibrous Tissue
13:45

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Published on: November 11, 2022

Morphological parameters contributing to aneurysm rupture: Identifying the rogue from the mugshot using different

Ehsan Kharati Koopaei1, Nasima Akhter2,3,4, Bartlomiej Roj2,5

  • 1Department of Anthropology, Durham University, Durham, UK.

British Journal of Neurosurgery
|June 30, 2026
PubMed
Summary

Aneurysm rupture risk can be predicted using morphological features, with accuracy improving when combined with patient history. Machine learning models effectively identify rupture-prone aneurysms, highlighting key predictive factors.

Keywords:
Aneurysm rupturemorphologypredictionsub-arachnoid haemorrhage

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The Helsinki Rat Microsurgical Sidewall Aneurysm Model
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A Method to Study the Correlation Between Local Collagen Structure and Mechanical Properties of Atherosclerotic Plaque Fibrous Tissue
13:45

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Published on: November 11, 2022

The Helsinki Rat Microsurgical Sidewall Aneurysm Model
13:53

The Helsinki Rat Microsurgical Sidewall Aneurysm Model

Published on: October 12, 2014

Area of Science:

  • Neurosurgery
  • Medical Imaging
  • Biostatistics

Background:

  • Current aneurysm rupture risk assessment primarily relies on size (>7mm), often overlooking crucial morphological details.
  • This limitation necessitates exploring advanced methods to identify subtle morphological indicators of aneurysm rupture.

Purpose of the Study:

  • To identify specific morphological features associated with intracranial aneurysm rupture.
  • To compare the predictive performance of machine learning models for aneurysm rupture detection.

Main Methods:

  • Utilized data from 429 aneurysms, including demographic and morphological information.
  • Employed generalized estimating equations and machine learning techniques (random forest, neural network, support vector machine) to analyze rupture risk.
  • Assessed the relative importance of morphological, demographic, and historical factors in predicting rupture.

Main Results:

  • Morphological characteristics, specifically parent vessel and aneurysm dome diameter, showed significant associations with rupture risk.
  • Support vector machine models performed best when using morphology alone; random forest excelled when incorporating demographic and historical data.
  • Key predictors for aneurysm rupture included age, aneurysm dome diameter, shape irregularity, smoking history, and bottleneck factor.

Conclusions:

  • Morphological characteristics alone can predict aneurysm rupture risk with moderate certainty.
  • Integrating additional patient data significantly enhances predictive accuracy, underscoring the need for comprehensive models.
  • Further large-scale studies are warranted to validate these findings and refine predictive capabilities.