Your browser doesn't support javascript.
loading
Artificial intelligence in the pediatric echocardiography laboratory: Automation, physiology, and outcomes.
Nguyen, Minh B; Villemain, Olivier; Friedberg, Mark K; Lovstakken, Lasse; Rusin, Craig G; Mertens, Luc.
Afiliação
  • Nguyen MB; Division of Cardiology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada.
  • Villemain O; Department of Pediatric Cardiology, Baylor College of Medicine, Houston, TX, United States.
  • Friedberg MK; Division of Cardiology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada.
  • Lovstakken L; Division of Cardiology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada.
  • Rusin CG; Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway.
  • Mertens L; Department of Pediatric Cardiology, Baylor College of Medicine, Houston, TX, United States.
Front Radiol ; 2: 881777, 2022.
Article em En | MEDLINE | ID: mdl-37492680
ABSTRACT
Artificial intelligence (AI) is frequently used in non-medical fields to assist with automation and decision-making. The potential for AI in pediatric cardiology, especially in the echocardiography laboratory, is very high. There are multiple tasks AI is designed to do that could improve the quality, interpretation, and clinical application of echocardiographic data at the level of the sonographer, echocardiographer, and clinician. In this state-of-the-art review, we highlight the pertinent literature on machine learning in echocardiography and discuss its applications in the pediatric echocardiography lab with a focus on automation of the pediatric echocardiogram and the use of echo data to better understand physiology and outcomes in pediatric cardiology. We also discuss next steps in utilizing AI in pediatric echocardiography.
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Front Radiol Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Canadá

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Front Radiol Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Canadá