ArticleIEEE transactions on medical imaging2019
A Machine Learning Approach for Classifying Ischemic Stroke Onset Time From Imaging.
Article in IEEE transactions on medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The best diagnostic approach for classifying ischemic stroke onset time: A systematic review and meta-analysis.Neuroradiology · 2025Pooled it
- The Value of Applying Machine Learning in Predicting the Time of Symptom Onset in Stroke Patients: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2023Pooled it
- Mixture-of-Skip-Connection Deep Learning Model to Classify Stroke Severity from Diffusion Weighted Imaging Based on NIHSS.Journal of imaging informatics in medicine · 2026Article
- A review of machine learning applications in heart health.Biomedical engineering online · 2025Review
- Healthcare professionals' knowledge, attitudes, and practices towards predictive diagnosis of early neurological deterioration.Scientific reports · 2025Article
- SDS-Net: A Synchronized Dual-Stage Network for Predicting Patients Within 4.5-h Thrombolytic Treatment Window Using MRI.Journal of imaging informatics in medicine · 2025Article
- Diffusion-/perfusion-weighted imaging fusion to automatically identify stroke within 4.5 h.European radiology · 2024Article
- Posterior circulation ischemic stroke: radiomics-based machine learning approach to identify onset time from magnetic resonance imaging.Neuroradiology · 2024Article
- The new era of artificial intelligence in neuroradiology: current research and promising tools.Arquivos de neuro-psiquiatria · 2024Article
- Taxonomy of Acute Stroke: Imaging, Processing, and Treatment.Diagnostics (Basel, Switzerland) · 2024Review
- Automated Assessment of the DWI-FLAIR Mismatch in Patients with Acute Ischemic Stroke: Added Value to Routine Clinical Practice.AJNR. American journal of neuroradiology · 2024Article
- Emerging frontiers of artificial intelligence and machine learning in ischemic stroke: a comprehensive investigation of state-of-the-art methodologies, clinical applications, and unraveling challenges.The EPMA journal · 2023Review
- Ischemic stroke prediction using machine learning in elderly Chinese population: The Rugao Longitudinal Ageing Study.Brain and behavior · 2023Article
- Artificial Intelligence for Automated DWI/FLAIR Mismatch Assessment on Magnetic Resonance Imaging in Stroke: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Big Data in Stroke: How to Use Big Data to Make the Next Management Decision.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2023Review
- Predicting Functional Outcome Using 24-Hour Post-Treatment Characteristics: Application of Machine Learning Algorithms in the STRATIS Registry.Annals of neurology · 2023Article
- Identifying patients with acute ischemic stroke within a 6-h window for the treatment of endovascular thrombectomy using deep learning and perfusion imaging.Frontiers in medicine · 2023Article
- The initiator of neuroexcitotoxicity and ferroptosis in ischemic stroke: Glutamate accumulation.Frontiers in molecular neuroscience · 2023Review
- A deep learning analysis of stroke onset time prediction and comparison to DWI-FLAIR mismatch.NeuroImage. Clinical · 2023Article
- Application of Machine Learning Techniques for Characterization of Ischemic Stroke with MRI Images: A Review.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Current clinical practice relies on clinical history to determine the time since stroke (TSS) onset. Imaging-based determination of acute stroke onset time could provide critical information to clinicians in deciding stroke treatment options, such as thrombolysis. The patients with unknown or unwitnessed TSS are usually excluded from thrombolysis, even if their symptoms began within the therapeutic window. In this paper, we demonstrate a machine learning approach for TSS classification using routinely acquired imaging sequences. We develop imaging features from the magnetic resonance (MR) images and train machine learning models to classify the TSS. We also propose a deep-learning model to extract hidden representations for the MR perfusion-weighted images and demonstrate classification improvement by incorporating these additional deep features. The cross-validation results show that our best classifier achieved an area under the curve of 0.765, with a sensitivity of 0.788 and a negative predictive value of 0.609, outperforming existing methods. We show that the features generated by our deep-learning algorithm correlate with the MR imaging features, and validate the robustness of the model on imaging parameter variations (e.g., year of imaging). This paper advances magnetic resonance imaging analysis one-step-closer to an operational decision support tool for stroke treatment guidance.
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