SynthesisBMC medical imaging2024
Classification, detection, and segmentation performance of image-based AI in intracranial aneurysm: a systematic review.
Synthesis in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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
13 citing papers in PubMed.
- 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
- Artificial Intelligence for Cerebral Aneurysm Management: Integrating Imaging, Hemodynamics, and Clinical Decision Support.Journal of imaging informatics in medicine · 2026Review
- Multimodal Deep Learning and Knowledge-Enhanced Intelligent Decision Support System for Pipeline Embolization Device Size Selection in Intracranial Aneurysm Treatment.CNS neuroscience & therapeutics · 2026Article
- Artificial Intelligence Across the Radiology Workflow: A Nine-Stage Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence in neurovascular surgery: advancing diagnosis, treatment, and outcomes.Frontiers in surgery · 2026Review
- Review of Aneurysms Detection Methods Focusing on Selected YOLO-Based Models.Journal of clinical medicine · 2025Review
- Prediction of Distal Dural Ring Location in Internal Carotid Paraclinoid Aneurysms Using the Tuberculum Sellae-Anterior Clinoid Process Line.Journal of clinical medicine · 2025Article
- Software-based simulation for pipeline vantage flow diverter preprocedural assessment: Method and validation study.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2025Article
- Review
- Intracranial aneurysm segmentation on digital subtraction angiography: a retrospective and multi-center study.Frontiers in neurology · 2025Article
- Evaluation of the clinical application value of artificial intelligence in diagnosing head and neck aneurysms.BMC medical imaging · 2024Article
- Global burden of subarachnoid hemorrhage among adolescents and young adults aged 15-39 years: A trend analysis study from 1990 to 2021.PloS one · 2024Article
- From severity scoring to predictive analytics: the emerging role of AI in neurosurgery.Frontiers in neurologyReview
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundThe detection and management of intracranial aneurysms (IAs) are vital to prevent life-threatening complications like subarachnoid hemorrhage (SAH). Artificial Intelligence (AI) can analyze medical images, like CTA or MRA, spotting nuances possibly overlooked by humans. Early detection facilitates timely interventions and improved outcomes. Moreover, AI algorithms offer quantitative data on aneurysm attributes, aiding in long-term monitoring and assessing rupture risks.
methodsWe screened four databases (PubMed, Web of Science, IEEE and Scopus) for studies using artificial intelligence algorithms to identify IA. Based on algorithmic methodologies, we categorized them into classification, segmentation, detection and combined, and then their merits and shortcomings are compared. Subsequently, we elucidate potential challenges that contemporary algorithms might encounter within real-world clinical diagnostic contexts. Then we outline prospective research trajectories and underscore key concerns in this evolving field.
resultsForty-seven studies of IA recognition based on AI were included based on search and screening criteria. The retrospective results represent that current studies can identify IA in different modal images and predict their risk of rupture and blockage. In clinical diagnosis, AI can effectively improve the diagnostic accuracy of IA and reduce missed detection and false positives.
conclusionsThe AI algorithm can detect unobtrusive IA more accurately in communicating arteries and cavernous sinus arteries to avoid further expansion. In addition, analyzing aneurysm rupture and blockage before and after surgery can help doctors plan treatment and reduce the uncertainties in the treatment process.
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Registered trials
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.