ArticleAnnals of clinical and translational neurology2019
Characterization of clot composition in acute cerebral infarct using machine learning techniques.
Article in Annals of clinical and translational neurology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 18 citations in OpenAlex.
- Deep learning-based prediction of atrial fibrillation from polar transformed time-frequency electrocardiogram.PloS one · 2025Article
- Automated Intracranial Clot Detection: A Promising Tool for Vascular Occlusion Detection in Non-Enhanced CT.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review.Biomedicines · 2023Review
- MRI Radiomics and Predictive Models in Assessing Ischemic Stroke Outcome-A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Thrombus Density in Acute Basilar Artery Occlusion Depends on Slice Thickness and the Method of Manual Thrombus Delineation.Life (Basel, Switzerland) · 2022Article
- MRI radiomic features-based machine learning approach to classify ischemic stroke onset time.Journal of neurology · 2022Article
- Radiology-Pathology Correlations of Intracranial Clots: Current Theories, Clinical Applications, and Future Directions.AJNR. American journal of neuroradiology · 2021Review
- Multimodality Characterization of the Clot in Acute Stroke.Frontiers in neurology · 2021Article
- Machine Learning in Action: Stroke Diagnosis and Outcome Prediction.Frontiers in neurology · 2021Review
- Prediction of Clinical Outcome in Patients with Large-Vessel Acute Ischemic Stroke: Performance of Machine Learning versus SPAN-100.AJNR. American journal of neuroradiology · 2021Article
- Big Data and Atrial Fibrillation: Current Understanding and New Opportunities.Journal of cardiovascular translational research · 2020Review
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveClot characteristics can provide information on the cause of cerebral artery occlusion and may guide acute revascularization and secondary prevention strategies. We developed a rapid automated clot analysis system using machine learning (ML) and validated its accuracy in patients undergoing endovascular treatment.
methodsPre-endovascular treatment gradient echo (GRE) images from consecutive patients with middle cerebral artery occlusion were utilized to develop and validate an ML system to predict whether atrial fibrillation (AF) was the underlying cause of ischemic stroke. The accuracy of the ML algorithm was compared with that of visual inspection by neuroimaging specialists for the presence of blooming artifact. Endovascular procedures and outcomes were compared in patients with and without AF.
resultsOf 67 patients, 29 (43.3%) had AF. Of these, 13 had known AF and 16 were newly diagnosed with cardiac monitoring. By visual inspection, interrater correlation for blooming artifact was 0.73 and sensitivity and specificity for AF were 0.79 and 0.63, respectively. For AF classification, the ML algorithms yielded an average accuracy of > 75.4% in fivefold cross-validation with clot signal profiles obtained from 52 patients and an area under the curve >0.87 for the average AF probability from five signal profiles in external validation (
interpretationML-based rapid clot analysis is feasible and can identify AF with high accuracy, enabling selection of endovascular treatment strategy.
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