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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.
Affiliation
  • 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 in 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.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Abdom Radiol (NY) Year: 2024 Document type: Article Affiliation country: United States Country of publication: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Abdom Radiol (NY) Year: 2024 Document type: Article Affiliation country: United States Country of publication: United States