Artificial intelligence in the diagnosis and outcome prediction of intracranial aneurysms: a meta-analysis and review.
7 affiliations
- 1Student Research Committee, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
- 2Functional Neurosurgery Research Center, Research Institute of Functional Neurosurgery, Shohada Tajrish Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
- 3Research Center of Artificial Intelligence in Health, Shohada-e Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
- 4Critical Care Quality Improvement Research Center, Shohada-e Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
- 5Research Center of Artificial Intelligence in Health, Shohada-e Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran. [email protected].
- 6Clinical Research Development Unit of Shohada-e Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran. [email protected].
- 7Musculoskeletal Injuries Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran. [email protected].
Abstract
Background
Intracranial aneurysms (IAs) affect up to 5% of the general population and pose a significant risk of subarachnoid hemorrhage if undetected or misclassified. Recent advances in artificial intelligence (AI), particularly deep learning, have shown promise in enhancing aneurysm detection and outcome prediction. We conducted a systematic review and meta-analysis to evaluate the diagnostic performance of AI in IA detection and reviewed its applications in rupture risk and outcome prediction.Methods
Following PRISMA guidelines, we screened major databases for studies reporting AI-based IA detection and/or outcome prediction. Eligible studies included comparisons of AI versus clinicians or AI-augmented interpretations. Pooled sensitivity, specificity, and interrater agreement (Fleiss' κ) were calculated. Outcome prediction studies were reviewed narratively.Results
Twenty studies (n = 20) met the inclusion criteria for meta-analysis. Pooled AI sensitivity and specificity were 0.96 and 0.95 respectively. However, substantial heterogeneity across studies was observed. These estimates should therefore be interpreted as exploratory summaries of reported study performance rather than definitive measures of real-world diagnostic accuracy. Subgroup analysis revealed robust AI performance for aneurysms > 3 mm (sensitivity: 0.98), while performance declined for < 3 mm lesions (sensitivity: 0.75). AI assistance improved clinician sensitivity to 0.92 and increased interrater agreement from κ = 0.67 to 0.86. Review of outcome prediction models showed promising AUCs (up to 0.96) for predicting rupture risk, aneurysm growth, and complications like vasospasm.Conclusions
AI shows promising performance in intracranial aneurysm detection and outcome prediction, particularly when used as a decision-support tool alongside clinicians. While limitations remain-especially in detecting very small aneurysms and generalizability across centers-AI represents a valuable adjunct in neurovascular diagnostics. Future directions include improved training datasets, multicenter validation, and seamless clinical integration to realize the full potential of AI-assisted neuroimaging and risk stratification.Clinical trial number
Not applicable.
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BibTeX
@article{naghizadeh2026artificial,
title = {{Artificial intelligence in the diagnosis and outcome prediction of intracranial aneurysms: a meta-analysis and review.}},
author = {Shahin Naghizadeh and Seyedpouzhia Shojaei and Fariborz Faeghi and Mohammadreza Aghamohammadi and Mona Gorji},
journal = {BMC medical imaging},
year = {2026},
month = jul,
doi = {10.1186/s12880-026-02495-w},
url = {https://europepmc.org/article/MED/42972273},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Artificial intelligence in the diagnosis and outcome prediction of intracranial aneurysms: a meta-analysis and review.
AU - Shahin Naghizadeh
AU - Seyedpouzhia Shojaei
AU - Fariborz Faeghi
AU - Mohammadreza Aghamohammadi
AU - Mona Gorji
PY - 2026
DA - 2026/07/31
JO - BMC medical imaging
DO - 10.1186/s12880-026-02495-w
UR - https://europepmc.org/article/MED/42972273
AB - Background Intracranial aneurysms (IAs) affect up to 5% of the general population and pose a significant risk of subarachnoid hemorrhage if undetected or misclassified. Recent advances in artificial intelligence (AI), particularly deep learning, have shown promise in enhancing aneurysm detection and outcome prediction. We conducted a systematic review and meta-analysis to evaluate the diagnostic performance of AI in IA detection and reviewed its applications in rupture risk and outcome prediction.Methods Following PRISMA guidelines, we screened major databases for studies reporting AI-based IA detection and/or outcome prediction. Eligible studies included comparisons of AI versus clinicians or AI-augmented interpretations. Pooled sensitivity, specificity, and interrater agreement (Fleiss' κ) were calculated. Outcome prediction studies were reviewed narratively.Results Twenty studies (n = 20) met the inclusion criteria for meta-analysis. Pooled AI sensitivity and specificity were 0.96 and 0.95 respectively. However, substantial heterogeneity across studies was observed. These estimates should therefore be interpreted as exploratory summaries of reported study performance rather than definitive measures of real-world diagnostic accuracy. Subgroup analysis revealed robust AI performance for aneurysms > 3 mm (sensitivity: 0.98), while performance declined for < 3 mm lesions (sensitivity: 0.75). AI assistance improved clinician sensitivity to 0.92 and increased interrater agreement from κ = 0.67 to 0.86. Review of outcome prediction models showed promising AUCs (up to 0.96) for predicting rupture risk, aneurysm growth, and complications like vasospasm.Conclusions AI shows promising performance in intracranial aneurysm detection and outcome prediction, particularly when used as a decision-support tool alongside clinicians. While limitations remain-especially in detecting very small aneurysms and generalizability across centers-AI represents a valuable adjunct in neurovascular diagnostics. Future directions include improved training datasets, multicenter validation, and seamless clinical integration to realize the full potential of AI-assisted neuroimaging and risk stratification.Clinical trial number Not applicable.
SN - 1471-2342
ER -