ArticleStroke2022
Machine Learning Automated Detection of Large Vessel Occlusion From Mobile Stroke Unit Computed Tomography Angiography.
Article in Stroke, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 34 citations in OpenAlex.
- Workflow improvements from automated large vessel occlusion detection algorithms are dependent on care team engagement.Journal of neurointerventional surgery · 2026Trial
- Anatomy- and Time-Based Post-Processing with CT Perfusion-Derived Time-Vessel Maps to Reduce Incorrect Occlusion Detections in Late-Phase CT Angiography.Diagnostics (Basel, Switzerland) · 2026Article
- The Future of Artificial Intelligence in Emergency Medical Services by 2030: An International Consensus Report.Journal of the American College of Emergency Physicians open · 2026Article
- Redefining Stroke Care with Artificial Intelligence: Recent Advances.Current neurology and neuroscience reports · 2026Review
- Artificial intelligence in neurology practice: promise, perils, and a roadmap for responsible integration.Journal of neurology · 2026Review
- Current State of the Clinical Applications of Artificial Intelligence in Stroke: A Literature Review.Brain sciences · 2026Review
- Seawater drowning induced acute lung injury: new insights from novel mouse models and micro-CT imaging.Open medicine (Warsaw, Poland) · 2026Article
- Computational modeling of immersed non-spherical bodies in viscous flows to study embolus-hemodynamics interactions in large-vessel occlusion stroke.Engineering with computers · 2026Article
- Can CTA-Based Machine Learning Identify Patients for Whom Successful Endovascular Stroke Therapy Is Insufficient?AJNR. American journal of neuroradiology · 2025Article
- Generalizable self-supervised learning for brain CTA in acute stroke.Computers in biology and medicine · 2025Article
- Enhancing Stroke Recognition: A Comparative Analysis of Balance and Eyes-Face, Arms, Speech, Time (BE-FAST) and Face, Arms, Speech, Time (FAST) in Identifying Posterior Circulation Strokes.Journal of clinical medicine · 2024Article
- Use of the SONAS Ultrasound Device for the Assessment of Cerebral Perfusion in Acute Ischemic Stroke.Stroke (Hoboken, N.J.) · 2024Article
- Prehospital identification of acute ischaemic stroke with large vessel occlusion: a retrospective study from western Norway.Emergency medicine journal : EMJ · 2024Article
- A self-supervised learning approach for registration agnostic imaging models with 3D brain CTA.iScience · 2024Article
- Application of Artificial Intelligence in Acute Ischemic Stroke: A Scoping Review.Neurointervention · 2024Review
- Automated identification of thrombectomy amenable vessel occlusion on computed tomography angiography using deep learning.Frontiers in neurology · 2024Article
- Deep learning-assisted diagnosis of large vessel occlusion in acute ischemic stroke based on four-dimensional computed tomography angiography.Frontiers in neuroscience · 2024Article
- Artificial intelligence and machine learning in prehospital emergency care: A scoping review.iScience · 2023Article
- Prehospital stroke management and mobile stroke units.Current opinion in neurology · 2023Review
- SELF-SUPERVISED LEARNING WITH RADIOLOGY REPORTS, A COMPARATIVE ANALYSIS OF STRATEGIES FOR LARGE VESSEL OCCLUSION AND BRAIN CTA IMAGES.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
Corrections and comments
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Authors and funding
17 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundPrehospital automated large vessel occlusion (LVO) detection in Mobile Stroke Units (MSUs) could accelerate identification and treatment of patients with LVO acute ischemic stroke. Here, we evaluate the performance of a machine learning (ML) model on CT angiograms (CTAs) obtained from 2 MSUs to detect LVO.
methodsPatients evaluated on MSUs in Houston and Los Angeles with out-of-hospital CTAs were identified. Anterior circulation LVO was defined as an occlusion of the intracranial internal carotid artery, middle cerebral artery (M1 or M2), or anterior cerebral artery vessels and determined by an expert human reader. A ML model to detect LVO was trained and tested on independent data sets consisting of in-hospital CTAs and then tested on MSU CTA images. Model performance was determined using area under the receiver-operator curve statistics.
resultsAmong 68 patients with out-of-hospital MSU CTAs, 40% had an LVO. The most common occlusion location was the middle cerebral artery M1 segment (59%), followed by the internal carotid artery (30%), and middle cerebral artery M2 (11%). Median time from last known well to CTA imaging was 88.0 (interquartile range, 59.5-196.0) minutes. After training on 870 in-hospital CTAs, the ML model performed well in identifying LVO in a separate in-hospital data set of 441 images with area under receiver-operator curve of 0.84 (95% CI, 0.80-0.87). ML algorithm analysis time was under 1 minute. The performance of the ML model on the MSU CTA images was comparable with area under receiver-operator curve 0.80 (95% CI, 0.71-0.89). There was no significant difference in performance between the Houston and Los Angeles MSU CTA cohorts.
conclusionsIn this study of patients evaluated on MSUs in 2 cities, a ML algorithm was able to accurately and rapidly detect LVO using prehospital CTA acquisitions.
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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.