ReviewFrontiers in neurology2021
Machine Learning in Action: Stroke Diagnosis and Outcome Prediction.
Review in Frontiers in neurology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 8 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
70 citing papers in PubMed, 8 syntheses or guidelines pooled it.
- Diagnostic Accuracy of Machine Learning Models in Predicting Functional Outcome of Thrombectomy for Acute Posterior Circulation Artery Occlusion: a Systematic Review and Meta-Analysis.Clinical neuroradiology · 2026Pooled it
- Guidelines for Neuroprognostication in Critically ill Adults with Acute Ischemic Stroke.Neurocritical care · 2026Guideline
- The performance of machine learning for predicting the recurrent stroke: a systematic review and meta-analysis on 24,350 patients.Acta neurologica Belgica · 2025Pooled it
- Exploring the potential of machine learning and magnetic resonance imaging in early stroke diagnosis: a bibliometric analysis (2004-2023).Frontiers in neurology · 2025Pooled it
- Application of artificial intelligence in X-ray imaging analysis for knee arthroplasty: A systematic review.PloS one · 2025Pooled it
- The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Machine learning in predicting outcomes for stroke patients following rehabilitation treatment: A systematic review.PloS one · 2023Pooled it
- Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta-analysis.Frontiers in neurology · 2023Pooled it
- Identification of the Dynamic Expression Patterns of Immune Genes in the Peripheral Blood of Acute Ischemic Stroke Patients.Biochemical genetics · 2026Article
- Artificial Intelligence and Emerging Digital Technologies Across the Stroke Continuum: From Risk Prediction to Real-Time Monitoring and Rapid Response.Medicina (Kaunas, Lithuania) · 2026Review
- Do predictors of motor recovery differ between robotic and conventional post-stroke rehabilitation?Journal of neuroengineering and rehabilitation · 2026Article
- Lesion-specific EEG signatures in stroke: a multi-scale framework integrating oscillations, connectivity, and asymmetry for machine learning decoding.Journal of neuroengineering and rehabilitation · 2026Article
- Challenges and potential of using digital biomarkers in healthcare and clinical trials.Communications medicine · 2026Review
- Predicting COVID-19 patient recovery or mortality using deep neural decision tree and forest.BMC research notes · 2025Article
- Systematic data management for effective AI-driven decision support systems in robotic rehabilitation.Scientific reports · 2025Article
- Predicting Long-Term Prognosis of Poststroke Dysphagia with Machine Learning.Journal of clinical medicine · 2025Article
- Heart Rate Variability and Autonomic Dysfunction After Stroke: Prognostic Markers for Recovery.Biomedicines · 2025Review
- Machine Learning Predictive Models for Survival in Patients with Brain Stroke.Health promotion perspectives · 2025Article
- Automatic prediction of stroke treatment outcomes: latest advances and perspectives.Biomedical engineering letters · 2025Review
- A retrospective evaluation of the potential of ChatGPT in the accurate diagnosis of acute stroke.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
10 more citing papers are in PubMed but not listed here.
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The application of machine learning has rapidly evolved in medicine over the past decade. In stroke, commercially available machine learning algorithms have already been incorporated into clinical application for rapid diagnosis. The creation and advancement of deep learning techniques have greatly improved clinical utilization of machine learning tools and new algorithms continue to emerge with improved accuracy in stroke diagnosis and outcome prediction. Although imaging-based feature recognition and segmentation have significantly facilitated rapid stroke diagnosis and triaging, stroke prognostication is dependent on a multitude of patient specific as well as clinical factors and hence accurate outcome prediction remains challenging. Despite its vital role in stroke diagnosis and prognostication, it is important to recognize that machine learning output is only as good as the input data and the appropriateness of algorithm applied to any specific data set. Additionally, many studies on machine learning tend to be limited by small sample size and hence concerted efforts to collate data could improve evaluation of future machine learning tools in stroke. In the present state, machine learning technology serves as a helpful and efficient tool for rapid clinical decision making while oversight from clinical experts is still required to address specific aspects not accounted for in an automated algorithm. This article provides an overview of machine learning technology and a tabulated review of pertinent machine learning studies related to stroke diagnosis and outcome prediction.
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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.