A primer on artificial intelligence in pancreatic imaging.

Journal: Diagnostic and interventional imaging
Published Date: Mar 24, 2023

Abstract

Artificial Intelligence (AI) is set to transform medical imaging by leveraging the vast data contained in medical images. Deep learning and radiomics are the two main AI methods currently being applied within radiology. Deep learning uses a layered set of self-correcting algorithms to develop a mathematical model that best fits the data. Radiomics converts imaging data into mineable features such as signal intensity, shape, texture, and higher-order features. Both methods have the potential to improve disease detection, characterization, and prognostication. This article reviews the current status of artificial intelligence in pancreatic imaging and critically appraises the quality of existing evidence using the radiomics quality score.

Authors

  • Taha M Ahmed
    The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
  • Satomi Kawamoto
    The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland.
  • Ralph H Hruban
    Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
  • Elliot K Fishman
    The Russell H. Morgan Department of Radiology and Radiologic Science, Johns Hopkins School of Medicine, Baltimore, Maryland. Electronic address: efishman@jhmi.edu.
  • Philippe Soyer
    Department of Radiology, Hôpital Cochin-APHP, 27 Rue du Faubourg Saint-Jacques, Paris 75014, France.
  • Linda C Chu
    The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland. Electronic address: lindachu@jhmi.edu.

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