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Advancing Intracranial Aneurysm Detection: A Comprehensive Systematic Review and Meta-analysis of Deep Learning

Niloufar Delfan¹, Fatemeh Abbasi², Negar Emamzadeh³

¹School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; Neuraitex Research Center, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.

Journal of Clinical Neuroscience : Official Journal of the Neurosurgical Society of Australasia

|April 30, 2025

Summary

Deep learning (DL) models significantly improve the detection and prediction of cerebral aneurysms, outperforming human diagnostics. DL assistance enhances clinician accuracy and agreement, offering a transformative potential for patient safety.

Keywords:

Computer aided diagnosis Deep learning Detection Intracranial aneurysms

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Area of Science:

Medical Imaging Analysis
Artificial Intelligence in Healthcare
Neurosurgery and Neurology

Background:

Cerebral aneurysms present a critical risk, especially when ruptured, necessitating early detection.
Traditional diagnostic methods often lack sensitivity and consistency.
Deep learning (DL) offers a promising avenue for enhancing diagnostic performance.

Purpose of the Study:

To systematically review and meta-analyze the performance of DL models in detecting and predicting intracranial aneurysms.
To compare DL model performance against clinician-based evaluations.
To assess the impact of DL assistance on clinician sensitivity, specificity, and interrater agreement.

Main Methods:

Systematic review and meta-analysis of studies evaluating DL models for aneurysm detection.
Inclusion of imaging modalities: CT angiography (CTA), digital subtraction angiography (DSA), and MR angiography (TOF-MRA).
Analysis of lesion-wise sensitivity, specificity, and clinician performance with and without DL assistance; subgroup analyses by aneurysm size/location; interrater agreement measurement using Fleiss' κ.

Main Results:

DL systems achieved superior lesion-wise sensitivity (90%) and specificity (94%) compared to human diagnostics.
DL assistance significantly improved clinician sensitivity (82% to 88% lesion-wise) and specificity (93% to 95% lesion-wise).
DL achieved 100% sensitivity for aneurysms >10mm and improved interrater agreement (Fleiss' κ from 0.668 to 0.862).

Conclusions:

DL models show transformative potential in cerebral aneurysm management through enhanced accuracy and reduced missed diagnoses.
DL systems can effectively support clinical decision-making in neurovascular diagnostics.
Further validation in diverse settings and workflow integration are crucial for widespread DL adoption.