Your browser doesn't support javascript.
loading
AI Applications to Breast MRI: Today and Tomorrow.
Lo Gullo, Roberto; Brunekreef, Joren; Marcus, Eric; Han, Lynn K; Eskreis-Winkler, Sarah; Thakur, Sunitha B; Mann, Ritse; Groot Lipman, Kevin; Teuwen, Jonas; Pinker, Katja.
Afiliação
  • Lo Gullo R; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA.
  • Brunekreef J; AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands.
  • Marcus E; AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands.
  • Han LK; Weill Cornell Medical College, New York-Presbyterian Hospital, New York City, New York, USA.
  • Eskreis-Winkler S; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA.
  • Thakur SB; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA.
  • Mann R; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, New York, USA.
  • Groot Lipman K; AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands.
  • Teuwen J; Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands.
  • Pinker K; AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands.
J Magn Reson Imaging ; 2024 Apr 05.
Article em En | MEDLINE | ID: mdl-38581127
ABSTRACT
In breast imaging, there is an unrelenting increase in the demand for breast imaging services, partly explained by continuous expanding imaging indications in breast diagnosis and treatment. As the human workforce providing these services is not growing at the same rate, the implementation of artificial intelligence (AI) in breast imaging has gained significant momentum to maximize workflow efficiency and increase productivity while concurrently improving diagnostic accuracy and patient outcomes. Thus far, the implementation of AI in breast imaging is at the most advanced stage with mammography and digital breast tomosynthesis techniques, followed by ultrasound, whereas the implementation of AI in breast magnetic resonance imaging (MRI) is not moving along as rapidly due to the complexity of MRI examinations and fewer available dataset. Nevertheless, there is persisting interest in AI-enhanced breast MRI applications, even as the use of and indications of breast MRI continue to expand. This review presents an overview of the basic concepts of AI imaging analysis and subsequently reviews the use cases for AI-enhanced MRI interpretation, that is, breast MRI triaging and lesion detection, lesion classification, prediction of treatment response, risk assessment, and image quality. Finally, it provides an outlook on the barriers and facilitators for the adoption of AI in breast MRI. LEVEL OF EVIDENCE 5 TECHNICAL EFFICACY Stage 6.
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: J Magn Reson Imaging / J. magn. reson. imaging / Journal of magnetic resonance imaging Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: J Magn Reson Imaging / J. magn. reson. imaging / Journal of magnetic resonance imaging Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos