ReviewFrontiers in neuroscience2023
The predictive performance of artificial intelligence on the outcome of stroke: a systematic review and meta-analysis.
Review in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.
What it found
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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
15 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Predictive Value of Machine Learning for Poststroke Mortality Risk: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Accuracy of Machine Learning in Predicting Post-Stroke Depression: A Systematic Review and Meta-Analysis.Brain and behavior · 2025Pooled it
- Systematic Review of Machine Learning Applied to the Secondary Prevention of Ischemic Stroke.Journal of medical systems · 2024Pooled it
- Understanding Responsible Development in AI-Based Clinical Prediction Models for Mortality: Protocol for a Scoping Review.JMIR research protocols · 2026Article
- Radiomics features and clinical factors for predicting restenosis following endovascular therapy in patients with peripheral artery disease.BMC medical informatics and decision making · 2026Article
- Circulating IncRNAs and Their Association With Ischemic Stroke Severity and Prognosis: A Systematic Review and Meta-Analysis.Health science reports · 2026Review
- Exploring Primary and Interaction Effects of Minor Physical Anomalies: Development and Validation of Prediction Models Using Explainable Machine Learning Algorithms for Early-Onset Schizophrenia.Schizophrenia bulletin · 2026Article
- Navigating the new frontier: technical, humanistic, and ethical skills in AI-augmented stroke care for vascular neurologists: a scoping review.Einstein (Sao Paulo, Brazil) · 2026Article
- Application of Artificial Intelligence in the Prediction and Management of Stroke Rehabilitation.Cyborg and bionic systems (Washington, D.C.) · 2026Review
- Development and validation of an interpretable machine learning model integrating baseline multimodal CT perfusion and clinical data for predicting 9-month functional outcomes in acute ischemic stroke.Frontiers in neurology · 2026Article
- Predicting Spinal Cord Injury Prognosis Using Machine Learning: Systematic Review and Meta-Analysis.JMIR AI · 2025Review
- AI and Neurology.Neurological research and practice · 2025Review
- Acute ischemic stroke prediction and predictive factors analysis using hematological indicators in elderly hypertensives post-transient ischemic attack.Scientific reports · 2024Article
- Machine learning-based nomogram: integrating MRI radiomics and clinical indicators for prognostic assessment in acute ischemic stroke.Frontiers in neurology · 2024Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: This study aimed to assess the accuracy of artificial intelligence (AI) models in predicting the prognosis of stroke. Methods: We searched PubMed, Embase, and Web of Science databases to identify studies using AI for acute stroke prognosis prediction from the database inception to February 2023. Selected studies were designed cohorts and had complete data. We used the Quality Assessment of Diagnostic Accuracy Studies tool to assess the qualities and bias of included studies and used a random-effects model to summarize and analyze the data. We used the area under curve (AUC) as an indicator of the predictive accuracy of AI models. Results: We retrieved a total of 1,241 publications and finally included seven studies. There was a low risk of bias and no significant heterogeneity in the final seven studies. The total pooled AUC under the fixed-effects model was 0.872 with a 95% CI of (0.862-0.881). The DL subgroup showed its AUC of 0.888 (95%CI 0.872-0.904). The LR subgroup showed its AUC 0.852 (95%CI 0.835-0.869). The RF subgroup showed its AUC 0.863 (95%CI 0.845-0.882). The SVM subgroup showed its AUC 0.905 (95%CI 0.857-0.952). The Xgboost subgroup showed its AUC 0.905 (95%CI 0.805-1.000). Conclusion: The accuracy of AI models in predicting the outcomes of ischemic stroke is good from our study. It could be an assisting tool for physicians in judging the outcomes of stroke patients. With the update of AI algorithms and the use of big data, further AI predictive models will perform better.
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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.