ReviewFrontiers in neurology2018
Machine Learning in Acute Ischemic Stroke Neuroimaging.
Review in Frontiers in neurology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 2 of them syntheses 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
43 citing papers in PubMed, 2 syntheses or guidelines pooled it, 123 citations in OpenAlex.
- Patterns of Infarction on MRI in Patients With Acute Ischemic Stroke and Cardio-Embolism: A Systematic Review and Meta-Analysis.Frontiers in neurology · 2020Pooled it
- A systematic review of machine learning models for predicting outcomes of stroke with structured data.PloS one · 2020Pooled it
- Collaborative artificial intelligence for the diagnosis and management of acute ischemic stroke.Annals of medicine · 2026Review
- Quantitative generation of diffusion-weighted imaging from non-contrast CT in acute ischemic stroke: a multi-scanner deep-learning model with external validation.Quantitative imaging in medicine and surgery · 2026Article
- Functional connectivity and graph theory of impaired central visual pathways in acute ischemic stroke based on functional magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2025Article
- Review
- Automated ASPECTS Segmentation and Scoring Tool: a Method Tailored for a Colombian Telestroke Network.Journal of imaging informatics in medicine · 2025Article
- State-of-the-art for automated machine learning predicts outcomes in poor-grade aneurysmal subarachnoid hemorrhage using routinely measured laboratory & radiological parameters: coagulation parameters and liver function as key prognosticators.Neurosurgical review · 2025Article
- From data to decisions: AI and functional connectivity for diagnosis, prognosis, and recovery prediction in stroke.GeroScience · 2025Article
- A Retrospective Machine Learning Analysis to Predict 3-Month Nonunion of Unstable Distal Clavicle Fracture Patients Treated with Open Reduction and Internal Fixation.Therapeutics and clinical risk management · 2025Article
- Voxel level dense prediction of acute stroke territory in DWI using deep learning segmentation models and image enhancement strategies.Japanese journal of radiology · 2024Article
- Association of Retinal Biomarkers With the Subtypes of Ischemic Stroke and an Automated Classification Model.Investigative ophthalmology & visual science · 2024Article
- Prediction of cerebral hemorrhagic transformation after thrombectomy using a deep learning of dual-energy CT.European radiology · 2024Article
- Developing a Warning Model of Potentially Inappropriate Medications in Older Chinese Outpatients in Tertiary Hospitals: A Machine-Learning Study.Journal of clinical medicine · 2023Article
- MRI advances in the imaging diagnosis of tuberculous meningitis: opportunities and innovations.Frontiers in microbiology · 2023Review
- End to end stroke triage using cerebrovascular morphology and machine learning.Frontiers in neurology · 2023Article
- Using Neural Networks Algorithm in Ischemic Stroke Diagnosis: A Systematic Review.Journal of multidisciplinary healthcare · 2023Review
- Predicting adverse drug events in older inpatients: a machine learning study.International journal of clinical pharmacy · 2022Article
- New strategy for clinical etiologic diagnosis of acute ischemic stroke and blood biomarker discovery based on machine learning.RSC advances · 2022Article
- Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review.BioMed research international · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine Learning (ML) through pattern recognition algorithms is currently becoming an essential aid for the diagnosis, treatment, and prediction of complications and patient outcomes in a number of neurological diseases. The evaluation and treatment of Acute Ischemic Stroke (AIS) have experienced a significant advancement over the past few years, increasingly requiring the use of neuroimaging for decision-making. In this review, we offer an insight into the recent developments and applications of ML in neuroimaging focusing on acute ischemic stroke.
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What OpenQuestion holds
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.