A survey of AI-based detection techniques for intracranial aneurysms

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creativework.keywordsartificial intelligence, intracranial aneurysm detection, medical imaging
dc.contributor.authorDacanay, Kathrina T.
dc.contributor.authorIngosan, Jeffrey S.
dc.date.accessioned2026-07-29T00:10:57Z
dc.date.available2026-07-29T00:10:57Z
dc.date.issued2026
dc.descriptionAbstract only
dc.description.abstractEarly detection of intracranial aneurysms (IA) is essential for preventing subarachnoid hemorrhage, a condition associated with high morbidity and mortality. Recent advances in artificial intelligence (AI), particularly deep learning, have shown promising results in assisting radiologists across different angiographic modalities. This survey synthesizes findings from 23 peer-reviewed studies spanning CTA, TOF-MRA, DSA, 3D-RA, and multiphase CTA. Across modalities, CTA-based models achieved some of the highest reported patient-level sensitivities, often reaching ${95-100 \%}$, while multiphase CTA further improved sensitivity by $4-6 \%$ compared to single-phase scanning. TOF-MRA studies reported sensitivities ranging from $90-96 \%$, with segmentation models attaining Dice scores of $0.80-0.83$ and AUC values up to 0.96. DSA and 3D-RA systems demonstrated strong performance for larger aneurysms, achieving sensitivities of $91-97 \%$, though performance dropped for aneurysms $\leq 3$ mm. Across all modalities, 3D CNNs and U-Net variants were the most frequently used architectures, with two-stage detection-segmentation pipelines consistently reducing false positives. Despite these advancements, challenges remain, particularly false-positive rates in CTA, reduced sensitivity for very small aneurysms, and limited cross-institutional generalization. This review consolidates current trends, highlights persistent gaps, and outlines future directions for developing clinically robust AI systems for IA detection.
dc.identifier.citationDacanay, K. T., & Ingosan, J. S. (2026). A survey of AI-based detection techniques for intracranial aneurysms. 2026 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications, 1-7. 10.1109/ACDSA67686.2026.11467811
dc.identifier.doi10.1109/ACDSA67686.2026.11467811
dc.identifier.urihttps://lakasa.dmmmsu.edu.ph/handle/123456789/2277
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers
dc.relation.urihttps://www.semanticscholar.org/paper/A-Survey-of-AI-Based-Detection-Techniques-for-Dacanay-Ingosan/3c3572c1837144935b3640485dc2d7668bb0fe53
dc.relation.urihttps://ieeexplore.ieee.org/document/11467811
dc.sdgSDG 3
dc.sdgSDG 9
dc.subjectIntracranial aneurysms
dc.subjectArtificial intelligence
dc.subjectDeep learning
dc.subjectMachine learning
dc.subjectMedical image analysis
dc.subjectComputer-aided diagnosis
dc.subjectMedical imaging
dc.subjectCerebrovascular diseases
dc.subjectDiagnostic imaging
dc.subjectSurvey of AI techniques
dc.subjectFiltering
dc.subjectFilters
dc.subjectCircuits and systems
dc.subjectLocation awareness
dc.subjectProtocols
dc.subjectProduct development
dc.subjectMobile communication
dc.subjectCommunication systems
dc.subjectHTTP
dc.subjectInternet
dc.subjectFalse Positive
dc.subjectFalse Positive Rate
dc.subjectArtificial Intelligence Systems
dc.subjectDice Score
dc.subjectModel Performance
dc.subjectVolumetric
dc.subjectImaging Modalities
dc.subjectTemporal Information
dc.subject.ddcCerebrovascular diseases--Diagnostic imaging
dc.subject.ddcArtificial intelligence
dc.subject.lcshIntracranial aneurysm--Diagnosis
dc.subject.lcshCerebral aneurysm--Diagnosis
dc.subject.lcshDiagnostic imaging
dc.subject.lcshArtificial intelligence--Medical applications
dc.subject.lcshDeep learning (Artificial intelligence)
dc.subject.lcshMachine learning
dc.subject.lcshComputer-aided diagnosis
dc.subject.lcshMedical image processing
dc.titleA survey of AI-based detection techniques for intracranial aneurysms
dc.typeArticle
oaire.citation.endPage7
oaire.citation.startPage1
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