Artificial Intelligence for Automated Detection of Large Vessel Occlusion: State of the Art, Clinical Applications and Future Perspectives
Review Summary
A clinically focused narrative review of artificial intelligence-assisted large vessel occlusion detection, diagnostic performance, workflow integration, commercial platforms and future development.
Large vessel occlusion is a major cause of morbidity and mortality in acute ischemic stroke and a common indication for mechanical thrombectomy. Rapid identification is critical because the benefit of reperfusion treatment is highly time dependent. Artificial intelligence-based imaging systems can support automated LVO identification, image prioritisation and rapid stroke-team notification.
To critically review current evidence on AI-assisted LVO detection, including diagnostic accuracy, clinical and workflow implications, commercially available platforms, barriers to implementation and future perspectives.
This narrative review examined literature published from January 2015 to June 2026 on AI-based LVO identification using computed tomography angiography and multimodal stroke imaging. Evidence relating to diagnostic performance, external validation, workflow impact, commercial platforms, implementation and emerging technologies was reviewed.
AI systems demonstrated high diagnostic accuracy for proximal anterior-circulation LVO, with reported sensitivities of 85–97% and specificities above 90%. Implementation has been associated with improved image triage, specialist notification, thrombectomy activation and interhospital coordination. Performance is less consistent for distal and posterior-circulation occlusions.
AI-assisted LVO detection has the potential to improve the speed and coordination of acute stroke care, particularly for proximal anterior-circulation occlusions. AI should augment rather than replace clinician interpretation, and its long-term value requires prospective multicentre validation, standardised comparisons and evaluation of patient-centred outcomes.
Review at a Glance
The review used a structured literature search followed by duplicate removal, title and abstract screening and full-text assessment.
PubMed/MEDLINE, Scopus, Web of Science, Embase and Google Scholar.
English-language literature published between January 2015 and June 2026.
Approximately 25 records were identified during the structured literature search.
Thirteen studies were included in the final narrative review after screening.
Why Rapid LVO Detection Matters
Large vessel occlusion accounts for a disproportionate share of severe stroke disability, mortality and healthcare burden.
LVO Detection → Thrombectomy Eligibility → Transfer → Reperfusion
Mechanical thrombectomy has transformed outcomes for eligible patients with LVO, but treatment benefit declines with delay. Fast recognition on CTA and rapid communication across stroke networks are therefore critical components of modern acute stroke care.
20–40% of AIS
The review notes that LVO comprises approximately 20–40% of acute ischemic strokes while accounting for a disproportionate burden of severe disability and death.
CTA
Computed tomography angiography enables immediate visualisation of cerebral vessels and identification of arterial occlusion.
Mechanical Thrombectomy
Endovascular thrombectomy is standard treatment for eligible anterior-circulation LVO and is increasingly supported for selected additional patient groups.
Time to Reperfusion
Delays in interpretation, specialist consultation, transfer and team activation can prolong time to reperfusion.
How Automated LVO Detection Works
Modern AI systems combine automated image processing, vascular analysis and deep-learning techniques to identify potential occlusions on CTA.
Image Pre-processing
CTA images may undergo normalisation, registration, artifact correction and other preprocessing before vascular analysis.
Vessel Segmentation
Algorithms identify and segment cerebral vasculature so that vessel continuity, enhancement and branching patterns can be analysed.
CNN & Deep Learning
Three-dimensional convolutional neural networks are among the most extensively studied architectures for automated LVO detection.
Vision Transformers
Attention-based and hybrid CNN-transformer models may capture longer-range vascular relationships, although their clinical utility requires further validation.
Strongest Performance in Proximal Anterior-Circulation LVO
Published evidence demonstrates strong diagnostic performance for proximal occlusions, particularly intracranial ICA and M1 disease.
Reported range for proximal anterior-circulation large vessel occlusion.
Specificities above 90% have been reported across systematic reviews and validation studies.
Area-under-the-curve values above 0.90 have been reported in published evaluations.
Performance is less consistent for M2–M3 occlusions, tandem lesions and posterior-circulation disease. Accuracy is also affected by image quality, contrast timing, patient characteristics, vascular anatomy, scanner type, LVO definitions and validation methods.
Beyond Diagnostic Accuracy
The potential clinical value of AI lies not only in detecting LVO but also in accelerating time-critical stroke pathways.
Automated Image Triage
Suspected LVO examinations can be prioritised for rapid clinical review.
Specialist Notification
Automated alerts can support simultaneous notification of neurologists, radiologists, neurointerventionalists and stroke coordinators.
Thrombectomy Activation
Earlier identification may support faster activation of endovascular treatment pathways.
Interhospital Coordination
AI-enabled communication may facilitate earlier transfer decisions within hub-and-spoke stroke networks.
Observational studies report reductions in selected process times, but evidence that AI independently improves functional outcome or mortality remains limited. Prospective evaluation of patient-centred outcomes is therefore necessary.
AI-Assisted LVO Detection in Clinical Practice
The review discusses several commercial platforms that combine image analysis with stroke-workflow functionality. No definitive ranking is supported because evaluation methods and study populations differ substantially.
CTA Detection & Team Communication
Provides CTA-based LVO detection, automated alerts, mobile communication and workflow tools designed to support rapid stroke-team collaboration.
Multimodal Stroke Imaging
Combines LVO detection with CT perfusion and additional stroke-imaging analysis that can support reperfusion decision-making.
ASPECTS · LVO · Collaterals
Supports automated assessment including ASPECTS grading, LVO detection and collateral evaluation.
Automated Triage
Provides automated prioritisation of suspected urgent abnormalities for rapid physician review.
Published evaluations differ in LVO definitions, vascular territories, imaging protocols, populations, reference standards and outcome measures. Platform selection should therefore consider local workflow, integration, validation, cost, support and clinical requirements rather than headline accuracy alone.
What Must Be Addressed Before Wider Adoption?
Diagnostic performance is only one component of safe, equitable and clinically useful AI deployment.
False Positives & Negatives
False negatives may delay thrombectomy, while false positives can lead to unnecessary review, transfers and alert fatigue.
External Validity
Models developed in high-volume academic centres may perform differently across scanners, protocols, populations and healthcare systems.
Algorithmic Fairness
Underrepresentation of particular demographic groups or rare stroke presentations can introduce algorithmic bias.
Model Transparency
Saliency maps, Grad-CAM and attention visualisation may improve interpretability but do not fully explain deep learning decision processes.
Regulatory & Medico-Legal Issues
Responsibility for erroneous or missed AI outputs, software updates and post-deployment performance monitoring requires clear institutional oversight.
PACS · RIS · EHR Integration
Clinical benefit depends heavily on reliable integration with imaging, electronic health records and communication systems.
Privacy & Cybersecurity
Cloud-based and mobile stroke networks require secure data sharing, authentication and compliance with data-protection requirements.
Automation Bias
Over-reliance on AI may create automation bias, while frequent false alerts can reduce clinician trust.
LMIC Implementation
Licensing costs, network connectivity, IT infrastructure, technical support and access to advanced imaging may restrict deployment in resource-limited settings.
From Image Detection to Integrated Decision Support
Next-generation AI systems may evolve beyond isolated LVO detection toward broader multimodal stroke decision support.
Foundation Models
General imaging representations may support multiple tasks such as vascular segmentation, LVO detection, ASPECTS, collateral assessment and outcome prediction.
Self-Supervised Learning
May reduce dependence on large manually annotated datasets and improve transferability between centres.
Multimodal AI
Future systems may integrate CTA, perfusion imaging, clinical data, NIHSS scores, laboratory findings and electronic health records.
Federated Learning
Could allow multiple institutions to train models collaboratively without directly sharing patient-level data.
Generative AI
Potential applications include automated reporting, clinical documentation and interdisciplinary communication, subject to rigorous validation.
Prehospital Triage
Integration with mobile tools, clinical assessment, wearables and portable imaging may support earlier identification and direct transfer to thrombectomy-capable centres.
Human–AI Collaboration, Not Machine-Only Decision-Making
Current systems demonstrate high diagnostic accuracy for proximal anterior-circulation occlusions and may improve image prioritisation, specialist notification, interhospital communication and activation of thrombectomy pathways. However, performance is less reliable for distal and posterior-circulation disease, and important concerns remain around generalisability, bias, explainability, interoperability, regulation, cost and equitable access. Future research should prioritise prospective multicentre validation, independent comparisons, post-deployment surveillance and patient-centred outcomes.
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Review Article / Narrative Review
A clinically focused narrative review of AI-assisted large vessel occlusion detection in acute ischemic stroke.
Five Scholarly Databases
PubMed/MEDLINE, Scopus, Web of Science, Embase and Google Scholar were searched for English-language literature published between January 2015 and June 2026.
~25 Records → 13 Included
Approximately 25 records were retrieved. Following duplicate removal and title, abstract and full-text screening, 13 studies were included in the final narrative review.
Qualitative Narrative Synthesis
Heterogeneity in AI models, populations, LVO definitions, imaging protocols, validation methods and outcome measures precluded meaningful quantitative synthesis.
Language & Readability Support
The manuscript states that AI language tools were used only for language, organisation and readability improvements. Literature selection, interpretation, critical analysis and conclusions remained the author’s responsibility, and AI was not used to generate, manipulate or fabricate research data, results or references.
Ishaan Bakshi
Conceptualisation, literature review, analysis, manuscript drafting, review and editing.
Conflict of Interest & Funding
Conflict of Interest:
No conflict of interest.
Funding:
No funding received.
Editorial Timeline
CC BY 4.0
Published under the Creative Commons Attribution 4.0
International licence.
DOI: 10.64573/torgj2607005
Volume 2 · Issue 3 · September 2026
The Operating Room Global Journal (TORGJ).
ISSN 3105-3262.
Review Article.
DOI: 10.64573/torgj2607005.
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Read or download the complete published review or access its persistent DOI record.