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Aneurysm III: Interprofessional Care

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Aneurysm management involves either conservative medical therapy or surgical intervention, depending on the size and symptoms of the aneurysm. Conservative management is generally reserved for smaller, asymptomatic aneurysms, while larger or symptomatic aneurysms often necessitate surgical repair.Conservative Medical TherapyFor small, asymptomatic aneurysms, particularly abdominal aortic aneurysms (AAA) less than 5.5 centimeters in diameter, conservative medical therapy is recommended. This...

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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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Updated: Jan 18, 2026

Minimally Invasive Thumb-sized Pterional Craniotomy for Surgical Clip Ligation of Unruptured Anterior Circulation Aneurysms

Minimally Invasive Thumb-sized Pterional Craniotomy for Surgical Clip Ligation of Unruptured Anterior Circulation Aneurysms

Published on: August 11, 2015

Machine Learning-Based Rupture Risk Prediction for Intracranial Aneurysms: A Systematic Review and Meta-Analysis.

S Farzad Maroufi¹, Maria José Pachón-Londoño^1,2, Maged Ghoche^1,2

¹Neurosurgery Simulation and Innovation Laboratory, Mayo Clinic, Phoenix , Arizona , USA.

|May 30, 2025

Summary

This summary is machine-generated.

Machine learning (ML) models show promise in predicting intracranial aneurysm rupture risk, offering higher specificity than the PHASES score. Incorporating hemodynamic data may further improve ML model accuracy for clinical integration.

Keywords:

Aneurysm Machine learning Ruptured

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Last Updated: Jan 18, 2026

Minimally Invasive Thumb-sized Pterional Craniotomy for Surgical Clip Ligation of Unruptured Anterior Circulation Aneurysms

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Endovascular Perforation Model for Subarachnoid Hemorrhage Combined with Magnetic Resonance Imaging MRI

Endovascular Perforation Model for Subarachnoid Hemorrhage Combined with Magnetic Resonance Imaging MRI

Published on: December 16, 2021

Area of Science:

Neuroscience and Medical Imaging
Artificial Intelligence in Healthcare
Clinical Risk Prediction

Background:

Aneurysm risk prediction is currently imprecise, leading to potential patient overtreatment or undertreatment.
Machine learning (ML) offers a potential solution to enhance precision in aneurysm risk assessment.

Purpose of the Study:

To systematically review and assess ML applications for predicting intracranial aneurysm rupture risk.
To compare the performance of ML models against the established PHASES score.

Main Methods:

A systematic review of PubMed, Scopus, and Web of Science databases.
Inclusion of studies utilizing ML tools for intracranial aneurysm rupture risk prediction.
Meta-analysis of ML algorithms and comparison with the PHASES score.

Main Results:

Analysis of 36 studies with 22,462 patients, employing 124 ML models across 25 algorithms.
ML models demonstrated comparable sensitivity and significantly higher specificity (0.763 vs. 0.507) versus the PHASES score.
ML models achieved a higher area under the receiver operating characteristic curve (AUC) (0.84 vs. 0.64) compared to the PHASES score. Deep learning showed a slightly better performance profile.

Conclusions:

ML techniques hold potential to improve intracranial aneurysm rupture prediction accuracy over traditional methods.
Integrating hemodynamic parameters into ML models may further enhance predictive accuracy.
Prospective studies are necessary to validate ML models for clinical implementation.