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Computed Tomography Perfusion-Based Machine Learning Model Better Predicts Follow-Up Infarction in Patients With Acute Ischemic Stroke
Research output
:
Contribution to journal
›
Article
›
peer-review
27
Citations (Scopus)
Overview
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Dive into the research topics of 'Computed Tomography Perfusion-Based Machine Learning Model Better Predicts Follow-Up Infarction in Patients With Acute Ischemic Stroke'. Together they form a unique fingerprint.
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Immunology and Microbiology
Brain Blood Flow
60%
Computed Tomographic Angiography
20%
Computer Assisted Tomography
100%
Infarct Volume
40%
Perfusion
100%
Random Forest
20%
Reperfusion
40%
Medicine and Dentistry
Apoplexy
22%
Brain Blood Flow
33%
Brain Ischemia
100%
Computed Tomography Angiography
11%
Computer Assisted Tomography
100%
Infarction
100%
Neuroscience
Angiography
20%
Brain Ischemia
100%
Computed Tomography
100%
Keyphrases
Dual-energy CT Angiography
11%
RAPID Software
11%
Stroke Trials
11%
Threshold-free
11%
Volumetric Differences
22%