ReviewBiomedicines2023
Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review.
Review in Biomedicines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Systematic Review of Artificial Intelligence Applications in Clinical Trials for Central Nervous System Injuries.Current neuropharmacology · 2026Pooled it
- Cerebral Infarction: Epidemiology, Classification, Mechanisms, Diagnosis, and Management.MedComm · 2026Review
- Artificial intelligence for early diagnosis in emergency department.Journal of anesthesia, analgesia and critical care · 2026Review
- Epidemiological Profile and Vascular Risk Factors of Stroke Patients in a Moroccan Provincial Hospital: A Retrospective Study.Neurology research international · 2026Article
- Development of a deep learning model for intussusception using point-of-care ultrasound.Frontiers in radiology · 2026Article
- Genetic Associations of Clonal Hematopoiesis With Cardioembolic Stroke: Insights From Genome-Wide Mendelian Randomization, Bulk RNA, Single-Cell RNA Sequencing.CNS neuroscience & therapeutics · 2025Article
- The association between vitamin D and restless legs syndrome following acute ischemic stroke.Scientific reports · 2025Article
- Predictive value of the product term BRI × carotid plaque thickness for stroke and transient ischemic attack: a prospective cohort study.Frontiers in neurology · 2025Article
- Automatic etiological classification of stroke thrombus digital photographs using a deep learning model.Frontiers in neurology · 2025Article
- Clinical applications of artificial intelligence and machine learning in neurocardiology: a comprehensive review.Frontiers in cardiovascular medicine · 2025Review
- Multi-classification Deep Learning Approach for Diagnosing Stroke Type and Severity Using Multimodal Magnetic Resonance Images.Journal of medical signals and sensors · 2025Article
- The most efficient machine learning algorithms in stroke prediction: A systematic review.Health science reports · 2024Review
- Review
- Predictive etiological classification of acute ischemic stroke through interpretable machine learning algorithms: a multicenter, prospective cohort study.BMC medical research methodology · 2024Article
- StrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records.NPJ digital medicine · 2024Article
- Molecular Pathways of Vulnerable Carotid Plaques at Risk of Ischemic Stroke: A Narrative Review.International journal of molecular sciences · 2024Review
- The montage method improves the classification of suspected acute ischemic stroke using the convolution neural network and brain MRI.Radiological physics and technology · 2024Article
- Artificial Intelligence as A Complementary Tool for Clincal Decision-Making in Stroke and Epilepsy.Brain sciences · 2024Review
- Optimization of Radiology Diagnostic Services for Patients with Stroke in Multidisciplinary Hospitals.Materia socio-medica · 2024Review
- Artificial intelligence in ischemic stroke images: current applications and future directions.Frontiers in neurology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The correct recognition of the etiology of ischemic stroke (IS) allows tempestive interventions in therapy with the aim of treating the cause and preventing a new cerebral ischemic event. Nevertheless, the identification of the cause is often challenging and is based on clinical features and data obtained by imaging techniques and other diagnostic exams. TOAST classification system describes the different etiologies of ischemic stroke and includes five subtypes: LAAS (large-artery atherosclerosis), CEI (cardio embolism), SVD (small vessel disease), ODE (stroke of other determined etiology), and UDE (stroke of undetermined etiology). AI models, providing computational methodologies for quantitative and objective evaluations, seem to increase the sensitivity of main IS causes, such as tomographic diagnosis of carotid stenosis, electrocardiographic recognition of atrial fibrillation, and identification of small vessel disease in magnetic resonance images. The aim of this review is to provide overall knowledge about the most effective AI models used in the differential diagnosis of ischemic stroke etiology according to the TOAST classification. According to our results, AI has proven to be a useful tool for identifying predictive factors capable of subtyping acute stroke patients in large heterogeneous populations and, in particular, clarifying the etiology of UDE IS especially detecting cardioembolic sources.
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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.