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Review
. 2021 Jan;33(2):231-241.
doi: 10.1111/den.13875. Epub 2020 Dec 5.

Artificial intelligence for the management of pancreatic diseases

Myrte Gorris  1 , Sanne A Hoogenboom  1 , Michael B Wallace  2 , Jeanin E van Hooft  1   3
Affiliations
Review

Artificial intelligence for the management of pancreatic diseases

Myrte Gorris et al. Dig Endosc. 2021 Jan.

Abstract

Novel artificial intelligence techniques are emerging in all fields of healthcare, including gastroenterology. The aim of this review is to give an overview of artificial intelligence applications in the management of pancreatic diseases. We performed a systematic literature search in PubMed and Medline up to May 2020 to identify relevant articles. Our results showed that the development of machine-learning based applications is rapidly evolving in the management of pancreatic diseases, guiding precision medicine in clinical, endoscopic and radiologic settings. Before implementation into clinical practice, further research should focus on the external validation of novel techniques, clarifying the accuracy and robustness of these models.

Keywords: artificial intelligence; diagnosis, computer-assisted; diagnostic imaging; endoscopy; pancreatic diseases.

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Conflict of interest statement

Author M.B.W. was supported by grants or donations from Fujifilm, Boston Scientific, Olympus, Medtronic, Ninepoint Medical and Cosmo/Aries Pharmaceuticals, author J.E.v.H. was supported by grants from Cook medical. Author M.B.W. owns stock of Virgo Inc, and is consulting for Virgo Inc, Cosmo/Aries Pharmaceuticals, Anx Robotica (2019), Covidien and GI Supply. On behalf of Mayo Clinic, M.B.W. is consulting for GI Supply (2018), Endokey, Endostart, Boston Scientific and Microtek and received general payments/minor food and beverage from Synergy Pharmaceuticals, Boston Scientific and Cook Medical. Author J.E.v.H. received consultancy fees from Boston Scientific, Medtronics and Cook Medical. Authors M.G. and S.A.H. declare no Conflict of Interests for this article.

Figures

Figure 1
Figure 1
Neural networks. Neural networks send signals from the input layer through a network of nodes. The network is trained by the process of adjusting the weights that amplify or damp the transmitted signals, carried by the links between the nodes. Deep learning networks have dozens of hidden layers and can model complex relationships within data. Reprinted with permission from M. Mitchell Waldrop. News Feature: What are the limits of deep learning? Proceedings of the National Academy of Sciences. Jan 2019, 116 (4) 1074–1077. Created by Lucy Reading‐Ikkanda.
See this image and copyright information in PMC

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