Literature DB >> 32439090

How Artificial Intelligence Will Impact Colonoscopy and Colorectal Screening.

Dennis L Shung1, Michael F Byrne2.   

Abstract

Artificial intelligence may improve value in colonoscopy-based colorectal screening and surveillance by improving quality and decreasing unnecessary costs. The quality of screening and surveillance as measured by adenoma detection rates can be improved through real-time computer-assisted detection of polyps. Unnecessary costs can be decreased with optical biopsies to identify low-risk polyps using computer-assisted diagnosis that can undergo the resect-and-discard or diagnose-and-leave strategy. Key challenges include the clinical integration of artificial intelligence-based technology into the endoscopists' workflow, the effect of this technology on endoscopy center efficiency, and the interpretability of the underlying deep learning algorithms. The future for image-based artificial intelligence in gastroenterology will include applications to improve the diagnosis and treatment of cancers throughout the gastrointestinal tract.
Copyright © 2020 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Artificial intelligence; Colonoscopy; Value-based care

Mesh:

Year:  2020        PMID: 32439090     DOI: 10.1016/j.giec.2020.02.010

Source DB:  PubMed          Journal:  Gastrointest Endosc Clin N Am        ISSN: 1052-5157


  3 in total

1.  Modern Machine Learning Practices in Colorectal Surgery: A Scoping Review.

Authors:  Stephanie Taha-Mehlitz; Silvio Däster; Laura Bach; Vincent Ochs; Markus von Flüe; Daniel Steinemann; Anas Taha
Journal:  J Clin Med       Date:  2022-04-26       Impact factor: 4.964

2.  Artificial Intelligence-Assisted Optical Biopsies of Colon Polyps: Hype or Reality?

Authors:  Hemant Goyal; Abhilash Perisetti; Sumant Inamdar; Benjamin Tharian; Jiannis Anastasiou
Journal:  Saudi J Med Med Sci       Date:  2022-01-13

3.  The Application and Development of Deep Learning in Radiotherapy: A Systematic Review.

Authors:  Danju Huang; Han Bai; Li Wang; Yu Hou; Lan Li; Yaoxiong Xia; Zhirui Yan; Wenrui Chen; Li Chang; Wenhui Li
Journal:  Technol Cancer Res Treat       Date:  2021 Jan-Dec
  3 in total

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