ArticleClinical neuroradiology2020
Performance of a Deep-Learning Neural Network to Detect Intracranial Aneurysms from 3D TOF-MRA Compared to Human Readers.
Article in Clinical neuroradiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 4 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 4 syntheses or guidelines pooled it, 61 citations in OpenAlex.
- 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
- 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
- Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis.Journal of neurointerventional surgery · 2023Pooled it
- A Deep Learning Model with High Standalone Performance for Diagnosis of Unruptured Intracranial Aneurysm.Yonsei medical journal · 2021Trial
- 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
- Intracranial Aneurysm Segmentation with a Dual-Path Fusion Network.Bioengineering (Basel, Switzerland) · 2025Article
- Automatic diagnosis and measurement of intracranial aneurysms using deep learning in MRA raw images.Frontiers in neurology · 2025Article
- Intracranial aneurysm detection: an object detection perspective.International journal of computer assisted radiology and surgery · 2024Review
- Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery.World neurosurgery: X · 2024Review
- Machine learning based outcome prediction of microsurgically treated unruptured intracranial aneurysms.Scientific reports · 2023Article
- Effectiveness of tuning an artificial intelligence algorithm for cerebral aneurysm diagnosis: a study of 10,000 consecutive cases.Scientific reports · 2023Article
- Automated detection of intracranial aneurysms using skeleton-based 3D patches, semantic segmentation, and auxiliary classification for overcoming data imbalance in brain TOF-MRA.Scientific reports · 2023Article
- Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future.Diagnostics (Basel, Switzerland) · 2023Review
- Automated unruptured cerebral aneurysms detection in TOF MR angiography images using dual-channel SE-3D UNet: a multi-center research.European radiology · 2023Article
- 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
- Deep Learning for Detection of Intracranial Aneurysms from Computed Tomography Angiography Images.Journal of digital imaging · 2023Article
- Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease.Frontiers in human neuroscience · 2023Review
- Review
- Role of Artificial Intelligence in Unruptured Intracranial Aneurysm: An Overview.Frontiers in neurology · 2022Review
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo study the clinical potential of a deep learning neural network (convolutional neural networks [CNN]) as a supportive tool for detection of intracranial aneurysms from 3D time-of-flight magnetic resonance angiography (TOF-MRA) by comparing the diagnostic performance to that of human readers.
methodsIn this retrospective study a pipeline for detection of intracranial aneurysms from clinical TOF-MRA was established based on the framework DeepMedic. Datasets of 85 consecutive patients served as ground truth and were used to train and evaluate the model. The ground truth without annotation was presented to two blinded human readers with different levels of experience in diagnostic neuroradiology (reader 1: 2 years, reader 2: 12 years). Diagnostic performance of human readers and the CNN was studied and compared using the χ
resultsGround truth consisted of 115 aneurysms with a mean diameter of 7 mm (range: 2-37 mm). Aneurysms were categorized as small (S; <3 mm; N = 13), medium (M; 3-7 mm; N = 57), and large (L; >7 mm; N = 45) based on the diameter. No statistically significant differences in terms of overall sensitivity (OS) were observed between the CNN and both of the human readers (reader 1 vs. CNN, P = 0.141; reader 2 vs. CNN, P = 0.231). The OS of both human readers was improved by combination of each readers' individual detections with the detections of the CNN (reader 1: 98% vs. 95%, P = 0.280; reader 2: 97% vs. 94%, P = 0.333).
conclusionA CNN is able to detect intracranial aneurysms from clinical TOF-MRA data with a sensitivity comparable to that of expert radiologists and may have the potential to improve detection rates of incidental findings in a clinical setting.
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