Use of Deep Learning to Predict Final Ischemic Stroke Lesions From Initial Magnetic Resonance Imaging | Zendy

Yannan Yu | Zendy; Yuan Xie | Zendy; Thoralf Thamm | Zendy; Enhao Gong | Zendy; Jiahong Ouyang | Zendy; Charles Huang | Zendy; Sören Christensen | Zendy; Michael P. Marks | Zendy; Maarten G. Lansberg | Zendy; Gregory W. Albers | Zendy; Greg Zaharchuk | Zendy

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Use of Deep Learning to Predict Final Ischemic Stroke Lesions From Initial Magnetic Resonance Imaging

Author(s) -

Yannan Yu,

Yuan Xie,

Thoralf Thamm,

Enhao Gong,

Jiahong Ouyang,

Charles Huang,

Sören Christensen,

Michael P. Marks,

Maarten G. Lansberg,

Gregory W. Albers,

Greg Zaharchuk

Publication year - 2020

Publication title -

jama network open

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.278

H-Index - 39

ISSN - 2574-3805

DOI - 10.1001/jamanetworkopen.2020.0772

Subject(s) - magnetic resonance imaging , medicine , stroke (engine) , infarction , voxel , sørensen–dice coefficient , radiology , segmentation , nuclear medicine , artificial intelligence , cardiology , image segmentation , computer science , myocardial infarction , mechanical engineering , engineering

This prognostic study assesses whether a deep learning model can predict final infarct lesions using magnetic resonance images acquired at initial presentation and compares the model with current clinical prediction methods among patients with acute ischemic stroke.

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