Trial reportYonsei medical journal2021
A Deep Learning Model with High Standalone Performance for Diagnosis of Unruptured Intracranial Aneurysm.
Trial report in Yonsei medical journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 5 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Shifting the paradigm in intracranial aneurysm detection with deep learning: A diagnostic accuracy meta-analysis and meta-regression.Neurosurgical review · 2026Pooled it
- Systematic review of artificial intelligence methods for detection and segmentation of unruptured intracranial aneurysms using medical imaging.Medical & biological engineering & computing · 2025Pooled it
- Classification, detection, and segmentation performance of image-based AI in intracranial aneurysm: a systematic review.BMC medical imaging · 2024Pooled it
- Application of convolutional network models in detection of intracranial aneurysms: A systematic review and meta-analysis.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2023Pooled it
- Systematic Review of Radiomics and Artificial Intelligence in Intracranial Aneurysm Management.Journal of neuroimaging : official journal of the American Society of NeuroimagingPooled it
- Clinical Comparison of Two Versions of a Commercial Artificial Intelligence System for Identifying Candidate Locations of Cerebral Aneurysms: Reduction and Characterization of False-Positive Findings.Journal of clinical medicine · 2026Article
- A Landmark-Guided Dual-Stream Synergistic Framework for Automated Intracranial Aneurysm Detection in Magnetic Resonance Angiography.Journal of imaging informatics in medicine · 2026Article
- A Mask R-CNN-Based Approach for Brain Aneurysm Detection and Segmentation from TOF-MRA Data.Brain sciences · 2025Article
- Methodological Challenges in Deep Learning-Based Detection of Intracranial Aneurysms: A Scoping Review.Neurointervention · 2025Review
- Integrated Deep Learning Model for the Detection, Segmentation, and Morphologic Analysis of Intracranial Aneurysms Using CT Angiography.Radiology. Artificial intelligence · 2025Article
- Automatic diagnosis and measurement of intracranial aneurysms using deep learning in MRA raw images.Frontiers in neurology · 2025Article
- Research progress in medical imaging for intracranial aneurysms: technological advances in diagnosis, management, and clinical integration.Frontiers in neurology · 2025Review
- Reproducibility and across-site transferability of an improved deep learning approach for aneurysm detection and segmentation in time-of-flight MR-angiograms.Scientific reports · 2024Article
- The automatic evaluation of steno-occlusive changes in time-of-flight magnetic resonance angiography of moyamoya patients using a 3D coordinate attention residual network.Quantitative imaging in medicine and surgery · 2023Article
- The value of peripheral blood PLR, Lp-PLA2, MHR, SII, and HCY in assessing the rupture risk of small and medium-sized intracranial aneurysms.Frontiers in neurologyArticle
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Authors and funding
13 authors.
Funding
Abstract
purposeThis study aimed to investigate whether a deep learning model for automated detection of unruptured intracranial aneurysms on time-of-flight (TOF) magnetic resonance angiography (MRA) can achieve a target diagnostic performance comparable to that of human radiologists for approval from the Korean Ministry of Food and Drug Safety as an artificial intelligence-applied software. MATERIALS AND
methodsIn this single-center, retrospective, confirmatory clinical trial, the diagnostic performance of the model was evaluated in a predetermined test set. After sample size estimation, the test set consisted of 135 aneurysm-containing examinations with 168 intracranial aneurysms and 197 aneurysm-free examinations. The target sensitivity and specificity were set as 87% and 92%, respectively. The patient-wise sensitivity and specificity of the model were analyzed. Moreover, the lesion-wise sensitivity and false-positive detection rate per case were also investigated.
resultsThe sensitivity and specificity of the model were 91.11% [95% confidence interval (CI): 84.99, 95.32] and 93.91% (95% CI: 89.60, 96.81), respectively, which met the target performance values. The lesion-wise sensitivity was 92.26%. The overall false-positive detection rate per case was 0.123. Of the 168 aneurysms, 13 aneurysms from 12 examinations were missed by the model.
conclusionThe present deep learning model for automated detection of unruptured intracranial aneurysms on TOF MRA achieved the target diagnostic performance comparable to that of human radiologists. With high standalone performance, this model may be useful for accurate and efficient diagnosis of intracranial aneurysm.
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