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Use of Deep Learning to Predict Final Ischemic Stroke Lesions From Initial Magnetic Resonance Imaging.
Yu, Yannan; Xie, Yuan; Thamm, Thoralf; Gong, Enhao; Ouyang, Jiahong; Huang, Charles; Christensen, Soren; Marks, Michael P; Lansberg, Maarten G; Albers, Gregory W; Zaharchuk, Greg.
Afiliación
  • Yu Y; Department of Radiology, Stanford University, Stanford, California.
  • Xie Y; Department of Radiology, Stanford University, Stanford, California.
  • Thamm T; Department of Radiology, Stanford University, Stanford, California.
  • Gong E; Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany.
  • Ouyang J; Department of Electrical Engineering, Stanford University, Stanford, California.
  • Huang C; Department of Electrical Engineering, Stanford University, Stanford, California.
  • Christensen S; Department of Electrical Engineering, Stanford University, Stanford, California.
  • Marks MP; Department of Neurology, Stanford University, Stanford, California.
  • Lansberg MG; Department of Radiology, Stanford University, Stanford, California.
  • Albers GW; Department of Neurology, Stanford University, Stanford, California.
  • Zaharchuk G; Department of Neurology, Stanford University, Stanford, California.
JAMA Netw Open ; 3(3): e200772, 2020 03 02.
Article en En | MEDLINE | ID: mdl-32163165
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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Imagen por Resonancia Magnética / Isquemia Encefálica / Selección de Paciente / Aprendizaje Profundo Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: JAMA Netw Open Año: 2020 Tipo del documento: Article
Texto completo
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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Imagen por Resonancia Magnética / Isquemia Encefálica / Selección de Paciente / Aprendizaje Profundo Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: JAMA Netw Open Año: 2020 Tipo del documento: Article