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Artificial intelligence in cardiac radiology.
van Assen, Marly; Muscogiuri, Giuseppe; Caruso, Damiano; Lee, Scott J; Laghi, Andrea; De Cecco, Carlo N.
Afiliación
  • van Assen M; Division of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University Hospital | Emory Healthcare, Inc., 1365 Clifton Road NE, Suite - AT503, Atlanta, GA, 30322, USA.
  • Muscogiuri G; Department of Imaging, Centro Cardiologico Monzino, IRCCS, Milan, Italy.
  • Caruso D; Dipartimento di Scienze Medico Chirurgiche e Medicina Traslazionale, Universita degli Studi Roma La Sapienza, Rome, Italy.
  • Lee SJ; Division of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University Hospital | Emory Healthcare, Inc., 1365 Clifton Road NE, Suite - AT503, Atlanta, GA, 30322, USA.
  • Laghi A; Dipartimento di Scienze Medico Chirurgiche e Medicina Traslazionale, Universita degli Studi Roma La Sapienza, Rome, Italy.
  • De Cecco CN; Division of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University Hospital | Emory Healthcare, Inc., 1365 Clifton Road NE, Suite - AT503, Atlanta, GA, 30322, USA. carlo.dececco@emory.edu.
Radiol Med ; 125(11): 1186-1199, 2020 Nov.
Article en En | MEDLINE | ID: mdl-32946002
Artificial intelligence (AI) is entering the clinical arena, and in the early stage, its implementation will be focused on the automatization tasks, improving diagnostic accuracy and reducing reading time. Many studies investigate the potential role of AI to support cardiac radiologist in their day-to-day tasks, assisting in segmentation, quantification, and reporting tasks. In addition, AI algorithms can be also utilized to optimize image reconstruction and image quality. Since these algorithms will play an important role in the field of cardiac radiology, it is increasingly important for radiologists to be familiar with the potential applications of AI. The main focus of this article is to provide an overview of cardiac-related AI applications for CT and MRI studies, as well as non-imaging-based applications for reporting and image optimization.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Radiología / Algoritmos / Inteligencia Artificial / Corazón Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Radiol Med Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Radiología / Algoritmos / Inteligencia Artificial / Corazón Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Radiol Med Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos