Automated detection of arterial landmarks and vascular occlusions in patients with acute stroke receiving digital subtraction angiography using deep learning.

Khankari, Jui; Yu, Yannan; Ouyang, Jiahong; Hussein, Ramy; Do, Huy M; Heit, Jeremy J; Zaharchuk, Greg

Khankari, Jui; Yu, Yannan; Ouyang, Jiahong; Hussein, Ramy; Do, Huy M; Heit, Jeremy J; Zaharchuk, Greg.

Affiliation

Khankari J; Department of Radiology, Stanford University, Stanford, California, USA.
Yu Y; Department of Radiology, Stanford University, Stanford, California, USA.
Ouyang J; Department of Electrical Engineering, Stanford University, Stanford, California, USA.
Hussein R; Department of Radiology, Stanford University, Stanford, California, USA.
Do HM; Department of Radiology and Neurosurgery, Stanford University, Stanford, California, USA.
Heit JJ; Radiology, Neuroadiology and Neurointervention Division, Stanford University, Stanford, California, USA.
Zaharchuk G; Department of Radiology, Stanford University, Stanford, California, USA gregz@stanford.edu.

J Neurointerv Surg ; 15(6): 521-525, 2023 Jun.

Article in En | MEDLINE | ID: mdl-35483913

Subject(s)

Arterial Occlusive Diseases; Deep Learning; Ischemic Stroke; Stroke; Humans; Angiography, Digital Subtraction/methods; Ischemic Stroke/diagnostic imaging; Ischemic Stroke/surgery; Artificial Intelligence; Stroke/diagnostic imaging; Stroke/surgery; Retrospective Studies

Key words

Angiography; Stroke

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Arterial Occlusive Diseases / Stroke / Deep Learning / Ischemic Stroke Type of study: Diagnostic_studies / Prognostic_studies Limits: Humans Language: En Journal: J Neurointerv Surg Year: 2023 Document type: Article Affiliation country: United States Country of publication: United kingdom

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