ArticleNature communications2020
A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers, 3 of them syntheses that pooled it.
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Who cites it
74 citing papers in PubMed, 3 syntheses or guidelines pooled 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
- Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis.Journal of neurointerventional surgery · 2023Pooled it
- DNA methylation-estimated phenotypes, telomere length, aging and risk of intracranial aneurysms: Evidence from genetic studies.IBRO neuroscience reports · 2026Article
- HSF-Net: a hybrid-scale fusion network for intracranial aneurysm segmentation in CTA images.Medical & biological engineering & computing · 2026Article
- Artificial Intelligence for Cerebral Aneurysm Management: Integrating Imaging, Hemodynamics, and Clinical Decision Support.Journal of imaging informatics in medicine · 2026Review
- GN-Net: A Geometric and Neighborhood-Aware Network for Predicting Intracranial Aneurysm Rupture Risk and Assisting Clinical Decision-Making.Interdisciplinary sciences, computational life sciences · 2026Article
- Exploring the causal relationship and pathogenesis of intracranial aneurysms with osteoporosis based on Mendelian randomization and bioinformatics study.Journal of bone and mineral metabolism · 2026Article
- Deep Learning-Guided Retinal Vascular Morphometric Quantification in Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy Mouse Models.Ophthalmology science · 2026Article
- Artificial intelligence-based biomarkers for the diagnosis and treatment of neurological conditions: a narrative review.Molecular brain · 2026Review
- PlaNet-S: an Automatic Semantic Segmentation Model for Placenta Using U-Net and SegNeXt.Journal of imaging informatics in medicine · 2026Article
- Anatomically-guided masked autoencoder pre-training for aneurysm detection.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision · 2026Article
- Artificial intelligence in neurovascular surgery: advancing diagnosis, treatment, and outcomes.Frontiers in surgery · 2026Review
- ecPICK: A deep learning-enabled spatial diagnostic platform for direct ecDNA identification and clinical prognosis across pan-cancer histopathology.Theranostics · 2026Article
- Article
- Assessing the Accuracy of Artificial Intelligence in Detecting Intracranial Aneurysms in a Clinical Setting Relative to Neuroradiologists.AJNR. American journal of neuroradiology · 2025Article
- A Joint Geometric Topological Analysis Network (JGTA-Net) for Detecting and Segmenting Intracranial Aneurysms.IEEE transactions on bio-medical engineering · 2025Article
- Retrospective detection of missed intra-cranial aneurysms on computed tomography angiography using a commercial deep learning algorithm.Neuroradiology · 2025Article
- A Mask R-CNN-Based Approach for Brain Aneurysm Detection and Segmentation from TOF-MRA Data.Brain sciences · 2025Article
- Deep learning algorithm for predicting rapid progression of abdominal aortic aneurysm by integrating CT images and clinical features.Scientific reports · 2025Article
14 more citing papers are in PubMed but not listed here.
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Authors and funding
24 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intracranial aneurysm is a common life-threatening disease. Computed tomography angiography is recommended as the standard diagnosis tool; yet, interpretation can be time-consuming and challenging. We present a specific deep-learning-based model trained on 1,177 digital subtraction angiography verified bone-removal computed tomography angiography cases. The model has good tolerance to image quality and is tested with different manufacturers. Simulated real-world studies are conducted in consecutive internal and external cohorts, in which it achieves an improved patient-level sensitivity and lesion-level sensitivity compared to that of radiologists and expert neurosurgeons. A specific cohort of suspected acute ischemic stroke is employed and it is found that 99.0% predicted-negative cases can be trusted with high confidence, leading to a potential reduction in human workload. A prospective study is warranted to determine whether the algorithm could improve patients' care in comparison to clinicians' assessment.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.