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Introduction to Radiomics and Artificial Intelligence: A Primer for Radiologists.
Haneberg, Adam G; Pierre, Kevin; Winter-Reinhold, Eric; Hochhegger, Bruno; Peters, Keith R; Grajo, Joseph; Arreola, Manuel; Asadizanjani, Navid; Bian, Jiang; Mancuso, Anthony; Forghani, Reza.
Afiliação
  • Haneberg AG; Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Division of Medical Physics, University of Florida College of Medicine, Gainesville, FL.
  • Pierre K; Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL.
  • Winter-Reinhold E; Augmented Intelligence and Precision Health Laboratory, Research Institute of the McGill University Health Centre, Montreal, QC, Canada.
  • Hochhegger B; Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL.
  • Peters KR; Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL.
  • Grajo J; Department of Radiology, University of Florida College of Medicine, Gainesville, FL.
  • Arreola M; Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL; Division of Medic
  • Asadizanjani N; Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL.
  • Bian J; Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL.
  • Mancuso A; Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL.
  • Forghani R; Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL; Division of Medic
Semin Roentgenol ; 58(2): 152-157, 2023 Apr.
Article em En | MEDLINE | ID: mdl-37087135
ABSTRACT
Health informatics and artificial intelligence (AI) are expected to transform the healthcare enterprise and the future practice of radiology. There is an increasing body of literature on radiomics and deep learning/AI applications in medical imaging. There are also a steadily increasing number of FDA cleared AI applications in radiology. It is therefore essential for radiologists to have a basic understanding of these approaches, whether in academia or private practice. In this article, we will provide an overview of the field and familiarize the readers with the fundamental concepts behind these approaches.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Radiologia / Inteligência Artificial Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: Semin Roentgenol Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Radiologia / Inteligência Artificial Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: Semin Roentgenol Ano de publicação: 2023 Tipo de documento: Article