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‘Artificial intelligence in Barrett’s Esophagus’

Despite advances in endoscopic imaging modalities, there are still significant miss rates of dysplasia and cancer in Barrett’s esophagus. Artificial intelligence (AI) is a promising tool that may potentially be a useful adjunct to the endoscopist in detecting subtle dysplasia and cancer. Studies hav...

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Detalles Bibliográficos
Autores principales: Hamade, Nour, Sharma, Prateek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8521738/
https://www.ncbi.nlm.nih.gov/pubmed/34671724
http://dx.doi.org/10.1177/26317745211049964
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author Hamade, Nour
Sharma, Prateek
author_facet Hamade, Nour
Sharma, Prateek
author_sort Hamade, Nour
collection PubMed
description Despite advances in endoscopic imaging modalities, there are still significant miss rates of dysplasia and cancer in Barrett’s esophagus. Artificial intelligence (AI) is a promising tool that may potentially be a useful adjunct to the endoscopist in detecting subtle dysplasia and cancer. Studies have shown AI systems have a sensitivity of more than 90% and specificity of more than 80% in detecting Barrett’s related dysplasia and cancer. Beyond visual detection and diagnosis, AI may also prove to be useful in quality control, streamlining clinical work, documentation, and lessening the administrative load on physicians. Research in this area is advancing at a rapid rate, and as the field expands, regulations and guidelines will need to be put into place to better regulate the growth and use of AI. This review provides an overview of the present and future role of AI in Barrett’s esophagus.
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spelling pubmed-85217382021-10-19 ‘Artificial intelligence in Barrett’s Esophagus’ Hamade, Nour Sharma, Prateek Ther Adv Gastrointest Endosc Artificial Intelligence in gastrointestinal endoscopy Despite advances in endoscopic imaging modalities, there are still significant miss rates of dysplasia and cancer in Barrett’s esophagus. Artificial intelligence (AI) is a promising tool that may potentially be a useful adjunct to the endoscopist in detecting subtle dysplasia and cancer. Studies have shown AI systems have a sensitivity of more than 90% and specificity of more than 80% in detecting Barrett’s related dysplasia and cancer. Beyond visual detection and diagnosis, AI may also prove to be useful in quality control, streamlining clinical work, documentation, and lessening the administrative load on physicians. Research in this area is advancing at a rapid rate, and as the field expands, regulations and guidelines will need to be put into place to better regulate the growth and use of AI. This review provides an overview of the present and future role of AI in Barrett’s esophagus. SAGE Publications 2021-10-12 /pmc/articles/PMC8521738/ /pubmed/34671724 http://dx.doi.org/10.1177/26317745211049964 Text en © The Author(s), 2021 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 page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Artificial Intelligence in gastrointestinal endoscopy
Hamade, Nour
Sharma, Prateek
‘Artificial intelligence in Barrett’s Esophagus’
title ‘Artificial intelligence in Barrett’s Esophagus’
title_full ‘Artificial intelligence in Barrett’s Esophagus’
title_fullStr ‘Artificial intelligence in Barrett’s Esophagus’
title_full_unstemmed ‘Artificial intelligence in Barrett’s Esophagus’
title_short ‘Artificial intelligence in Barrett’s Esophagus’
title_sort ‘artificial intelligence in barrett’s esophagus’
topic Artificial Intelligence in gastrointestinal endoscopy
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8521738/
https://www.ncbi.nlm.nih.gov/pubmed/34671724
http://dx.doi.org/10.1177/26317745211049964
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