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Implementation of artificial intelligence in upper gastrointestinal endoscopy
The application of artificial intelligence (AI) using deep learning has significantly expanded in the field of esophagogastric endoscopy. Recent studies have shown promising results in detecting and differentiating early gastric cancer using AI tools built using white light, magnified, or image‐enha...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9302271/ https://www.ncbi.nlm.nih.gov/pubmed/35873509 http://dx.doi.org/10.1002/deo2.72 |
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author | Nagao, Sayaka Tani, Yasuhiro Shibata, Junichi Tsuji, Yosuke Tada, Tomohiro Ishihara, Ryu Fujishiro, Mitsuhiro |
author_facet | Nagao, Sayaka Tani, Yasuhiro Shibata, Junichi Tsuji, Yosuke Tada, Tomohiro Ishihara, Ryu Fujishiro, Mitsuhiro |
author_sort | Nagao, Sayaka |
collection | PubMed |
description | The application of artificial intelligence (AI) using deep learning has significantly expanded in the field of esophagogastric endoscopy. Recent studies have shown promising results in detecting and differentiating early gastric cancer using AI tools built using white light, magnified, or image‐enhanced endoscopic images. Some studies have reported the use of AI tools to predict the depth of early gastric cancer based on endoscopic images. Similarly, studies based on using AI for detecting early esophageal cancer have also been reported, with an accuracy comparable to that of endoscopy specialists. Moreover, an AI system, developed to diagnose pharyngeal cancer, has shown promising performance with high sensitivity. These reports suggest that, if introduced for regular use in clinical settings, AI systems can significantly reduce the burden on physicians. This review summarizes the current status of AI applications in the upper gastrointestinal tract and presents directions for clinical practice implementation and future research. |
format | Online Article Text |
id | pubmed-9302271 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93022712022-07-22 Implementation of artificial intelligence in upper gastrointestinal endoscopy Nagao, Sayaka Tani, Yasuhiro Shibata, Junichi Tsuji, Yosuke Tada, Tomohiro Ishihara, Ryu Fujishiro, Mitsuhiro DEN Open Reviews The application of artificial intelligence (AI) using deep learning has significantly expanded in the field of esophagogastric endoscopy. Recent studies have shown promising results in detecting and differentiating early gastric cancer using AI tools built using white light, magnified, or image‐enhanced endoscopic images. Some studies have reported the use of AI tools to predict the depth of early gastric cancer based on endoscopic images. Similarly, studies based on using AI for detecting early esophageal cancer have also been reported, with an accuracy comparable to that of endoscopy specialists. Moreover, an AI system, developed to diagnose pharyngeal cancer, has shown promising performance with high sensitivity. These reports suggest that, if introduced for regular use in clinical settings, AI systems can significantly reduce the burden on physicians. This review summarizes the current status of AI applications in the upper gastrointestinal tract and presents directions for clinical practice implementation and future research. John Wiley and Sons Inc. 2022-03-15 /pmc/articles/PMC9302271/ /pubmed/35873509 http://dx.doi.org/10.1002/deo2.72 Text en © 2022 The Authors. DEN Open published by John Wiley & Sons Australia, Ltd on behalf of Japan Gastroenterological Endoscopy Society https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Reviews Nagao, Sayaka Tani, Yasuhiro Shibata, Junichi Tsuji, Yosuke Tada, Tomohiro Ishihara, Ryu Fujishiro, Mitsuhiro Implementation of artificial intelligence in upper gastrointestinal endoscopy |
title | Implementation of artificial intelligence in upper gastrointestinal endoscopy |
title_full | Implementation of artificial intelligence in upper gastrointestinal endoscopy |
title_fullStr | Implementation of artificial intelligence in upper gastrointestinal endoscopy |
title_full_unstemmed | Implementation of artificial intelligence in upper gastrointestinal endoscopy |
title_short | Implementation of artificial intelligence in upper gastrointestinal endoscopy |
title_sort | implementation of artificial intelligence in upper gastrointestinal endoscopy |
topic | Reviews |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9302271/ https://www.ncbi.nlm.nih.gov/pubmed/35873509 http://dx.doi.org/10.1002/deo2.72 |
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