ReviewFrontiers in genetics2021
Predicting Ischemic Stroke Outcome Using Deep Learning Approaches.
Review in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 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
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A 25-Year Retrospective of the Use of AI for Diagnosing Acute Stroke: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Predictive Performance of Artificial Intelligence Models for Stroke Risk Stratification: A Systematic Review and Meta-Analysis.Inquiry : a journal of medical care organization, provision and financingPooled it
- Automated CTA-Derived Collateral Grading and Morphologic Metrics for Enhanced Prediction of Poststroke Outcomes.AJNR. American journal of neuroradiology · 2026Article
- Prediction of recurrent ischemic stroke using machine learning from real-world data.BMC medical research methodology · 2026Article
- Cerebrovascular morphology: Insights into normal variations, aging effects and disease implications.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2025Review
- Comprehensive analysis of stroke epidemiology in Vietnam: Insights from GBD 1990-2019 and RES-Q 2017-2023.Global epidemiology · 2025Article
- Establishment and validation of a ResNet-based radiomics model for predicting prognosis in cervical spinal cord injury patients.Scientific reports · 2025Article
- Improving stroke risk prediction by integrating XGBoost, optimized principal component analysis, and explainable artificial intelligence.BMC medical informatics and decision making · 2025Article
- Heat syndrome types prediction of traditional Chinese medicine in acute ischemic stroke through deep learning: a pilot study.Frontiers in pharmacology · 2025Article
- OptiSelect and EnShap: Integrating machine learning and game theory for ischemic stroke prediction.PloS one · 2025Article
- Intelligent Stroke Disease Prediction Model Using Deep Learning Approaches.Stroke research and treatment · 2024Article
- Ischemic stroke outcome prediction with diversity features from whole brain tissue using deep learning network.Frontiers in neurology · 2024Article
- From Admission to Discharge: Predicting National Institutes of Health Stroke Scale Progression in Stroke Patients Using Biomarkers and Explainable Machine Learning.Journal of personalized medicine · 2023Article
- Artificial Intelligence-Based Speech Analysis System for Medical Support.International neurourology journal · 2023Article
- Evaluation of Blood Biomarkers and Parameters for the Prediction of Stroke Survivors' Functional Outcome upon Discharge Utilizing Explainable Machine Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Connectomics underlying motor functional outcomes in the acute period following stroke.Frontiers in aging neuroscience · 2023Article
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
Predicting functional outcomes after an Ischemic Stroke (IS) is highly valuable for patients and desirable for physicians. This facilitates physicians to set reasonable goals for patients and cooperate with patients and relatives effectively, and furthermore to reach common after-stroke care decisions for recovery and make exercise plans to facilitate rehabilitation. The objective of this research is to apply three current Deep Learning (DL) approaches for 6-month IS outcome predictions, using the openly accessible International Stroke Trial (IST) dataset. Furthermore, another objective of this research is to compare these DL approaches with machine learning (ML) for performing in clinical prediction. After comparing various ML methods (Deep Forest, Random Forest, Support Vector Machine, etc.) with current DL frameworks (CNN, LSTM, Resnet), the results show that DL doesn't outperform ML significantly. DL methods and reporting used for analyzing structured medical data should be developed and improved.
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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.