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AI-powered innovations in pancreatitis imaging: a comprehensive literature synthesis.
Maletz, Sebastian; Balagurunathan, Yoga; Murphy, Kade; Folio, Les; Chima, Ranjit; Zaheer, Atif; Vadvala, Harshna.
Afiliación
  • Maletz S; University of South Florida Morsani College of Medicine, Tampa, USA.
  • Balagurunathan Y; Moffitt Cancer Center, Tampa, USA.
  • Murphy K; University of South Florida Morsani College of Medicine, Tampa, USA.
  • Folio L; University of South Florida Morsani College of Medicine, Tampa, USA.
  • Chima R; Moffitt Cancer Center, Tampa, USA.
  • Zaheer A; University of South Florida Morsani College of Medicine, Tampa, USA.
  • Vadvala H; Moffitt Cancer Center, Tampa, USA.
Abdom Radiol (NY) ; 2024 Aug 12.
Article en En | MEDLINE | ID: mdl-39133362
ABSTRACT
Early identification of pancreatitis remains a significant clinical diagnostic challenge that impacts patient outcomes. The evolution of quantitative imaging followed by deep learning models has shown great promise in the non-invasive diagnosis of pancreatitis and its complications. We provide an overview of advancements in diagnostic imaging and quantitative imaging methods along with the evolution of artificial intelligence (AI). In this article, we review the current and future states of methodology and limitations of AI in improving clinical support in the context of early detection and management of pancreatitis.
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Abdom Radiol (NY) Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Abdom Radiol (NY) Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos
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