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Artificial intelligence in luminal endoscopy

Artificial intelligence is a strong focus of interest for global health development. Diagnostic endoscopy is an attractive substrate for artificial intelligence with a real potential to improve patient care through standardisation of endoscopic diagnosis and to serve as an adjunct to enhanced imagin...

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Autores principales: Gulati, Shraddha, Emmanuel, Andrew, Patel, Mehul, Williams, Sophie, Haji, Amyn, Hayee, Bu’Hussain, Neumann, Helmut
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7315657/
https://www.ncbi.nlm.nih.gov/pubmed/32637935
http://dx.doi.org/10.1177/2631774520935220
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author Gulati, Shraddha
Emmanuel, Andrew
Patel, Mehul
Williams, Sophie
Haji, Amyn
Hayee, Bu’Hussain
Neumann, Helmut
author_facet Gulati, Shraddha
Emmanuel, Andrew
Patel, Mehul
Williams, Sophie
Haji, Amyn
Hayee, Bu’Hussain
Neumann, Helmut
author_sort Gulati, Shraddha
collection PubMed
description Artificial intelligence is a strong focus of interest for global health development. Diagnostic endoscopy is an attractive substrate for artificial intelligence with a real potential to improve patient care through standardisation of endoscopic diagnosis and to serve as an adjunct to enhanced imaging diagnosis. The possibility to amass large data to refine algorithms makes adoption of artificial intelligence into global practice a potential reality. Initial studies in luminal endoscopy involve machine learning and are retrospective. Improvement in diagnostic performance is appreciable through the adoption of deep learning. Research foci in the upper gastrointestinal tract include the diagnosis of neoplasia, including Barrett’s, squamous cell and gastric where prospective and real-time artificial intelligence studies have been completed demonstrating a benefit of artificial intelligence–augmented endoscopy. Deep learning applied to small bowel capsule endoscopy also appears to enhance pathology detection and reduce capsule reading time. Prospective evaluation including the first randomised trial has been performed in the colon, demonstrating improved polyp and adenoma detection rates; however, these appear to be relevant to small polyps. There are potential additional roles of artificial intelligence relevant to improving the quality of endoscopic examinations, training and triaging of referrals. Further large-scale, multicentre and cross-platform validation studies are required for the robust incorporation of artificial intelligence–augmented diagnostic luminal endoscopy into our routine clinical practice.
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spelling pubmed-73156572020-07-06 Artificial intelligence in luminal endoscopy Gulati, Shraddha Emmanuel, Andrew Patel, Mehul Williams, Sophie Haji, Amyn Hayee, Bu’Hussain Neumann, Helmut Ther Adv Gastrointest Endosc Artificial Intelligence in Gastrointestinal Endoscopy Artificial intelligence is a strong focus of interest for global health development. Diagnostic endoscopy is an attractive substrate for artificial intelligence with a real potential to improve patient care through standardisation of endoscopic diagnosis and to serve as an adjunct to enhanced imaging diagnosis. The possibility to amass large data to refine algorithms makes adoption of artificial intelligence into global practice a potential reality. Initial studies in luminal endoscopy involve machine learning and are retrospective. Improvement in diagnostic performance is appreciable through the adoption of deep learning. Research foci in the upper gastrointestinal tract include the diagnosis of neoplasia, including Barrett’s, squamous cell and gastric where prospective and real-time artificial intelligence studies have been completed demonstrating a benefit of artificial intelligence–augmented endoscopy. Deep learning applied to small bowel capsule endoscopy also appears to enhance pathology detection and reduce capsule reading time. Prospective evaluation including the first randomised trial has been performed in the colon, demonstrating improved polyp and adenoma detection rates; however, these appear to be relevant to small polyps. There are potential additional roles of artificial intelligence relevant to improving the quality of endoscopic examinations, training and triaging of referrals. Further large-scale, multicentre and cross-platform validation studies are required for the robust incorporation of artificial intelligence–augmented diagnostic luminal endoscopy into our routine clinical practice. SAGE Publications 2020-06-23 /pmc/articles/PMC7315657/ /pubmed/32637935 http://dx.doi.org/10.1177/2631774520935220 Text en © The Author(s), 2020 https://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Artificial Intelligence in Gastrointestinal Endoscopy
Gulati, Shraddha
Emmanuel, Andrew
Patel, Mehul
Williams, Sophie
Haji, Amyn
Hayee, Bu’Hussain
Neumann, Helmut
Artificial intelligence in luminal endoscopy
title Artificial intelligence in luminal endoscopy
title_full Artificial intelligence in luminal endoscopy
title_fullStr Artificial intelligence in luminal endoscopy
title_full_unstemmed Artificial intelligence in luminal endoscopy
title_short Artificial intelligence in luminal endoscopy
title_sort artificial intelligence in luminal endoscopy
topic Artificial Intelligence in Gastrointestinal Endoscopy
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7315657/
https://www.ncbi.nlm.nih.gov/pubmed/32637935
http://dx.doi.org/10.1177/2631774520935220
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