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Background <p>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.</p> Methods <p>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.</p> Results <p>Twenty studies (<i>n</i> = 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 &gt; 3&#xa0;mm (sensitivity: 0.98), while performance declined for &lt; 3&#xa0;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.</p> Conclusions <p>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.</p> Clinical trial number <p>Not applicable.</p>

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Artificial intelligence in the diagnosis and outcome prediction of intracranial aneurysms: a meta-analysis and review

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月31日

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  • Shahin Naghizadeh,
  • Seyedpouzhia Shojaei,
  • Fariborz Faeghi,
  • Mohammadreza Aghamohammadi,
  • Mona Gorji

摘要

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.