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Artificial intelligence-assisted esophageal cancer management: Now and future
Esophageal cancer poses diagnostic, therapeutic and economic burdens in high-risk regions. Artificial intelligence (AI) has been developed for diagnosis and outcome prediction using various features, including clinicopathologic, radiologic, and genetic variables, which can achieve inspiring results....
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Baishideng Publishing Group Inc
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7504247/ https://www.ncbi.nlm.nih.gov/pubmed/32994686 http://dx.doi.org/10.3748/wjg.v26.i35.5256 |
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author | Zhang, Yu-Hang Guo, Lin-Jie Yuan, Xiang-Lei Hu, Bing |
author_facet | Zhang, Yu-Hang Guo, Lin-Jie Yuan, Xiang-Lei Hu, Bing |
author_sort | Zhang, Yu-Hang |
collection | PubMed |
description | Esophageal cancer poses diagnostic, therapeutic and economic burdens in high-risk regions. Artificial intelligence (AI) has been developed for diagnosis and outcome prediction using various features, including clinicopathologic, radiologic, and genetic variables, which can achieve inspiring results. One of the most recent tasks of AI is to use state-of-the-art deep learning technique to detect both early esophageal squamous cell carcinoma and esophageal adenocarcinoma in Barrett’s esophagus. In this review, we aim to provide a comprehensive overview of the ways in which AI may help physicians diagnose advanced cancer and make clinical decisions based on predicted outcomes, and combine the endoscopic images to detect precancerous lesions or early cancer. Pertinent studies conducted in recent two years have surged in numbers, with large datasets and external validation from multi-centers, and have partly achieved intriguing results of expert’s performance of AI in real time. Improved pre-trained computer-aided diagnosis algorithms in the future studies with larger training and external validation datasets, aiming at real-time video processing, are imperative to produce a diagnostic efficacy similar to or even superior to experienced endoscopists. Meanwhile, supervised randomized controlled trials in real clinical practice are highly essential for a solid conclusion, which meets patient-centered satisfaction. Notably, ethical and legal issues regarding the black-box nature of computer algorithms should be addressed, for both clinicians and regulators. |
format | Online Article Text |
id | pubmed-7504247 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Baishideng Publishing Group Inc |
record_format | MEDLINE/PubMed |
spelling | pubmed-75042472020-09-28 Artificial intelligence-assisted esophageal cancer management: Now and future Zhang, Yu-Hang Guo, Lin-Jie Yuan, Xiang-Lei Hu, Bing World J Gastroenterol Minireviews Esophageal cancer poses diagnostic, therapeutic and economic burdens in high-risk regions. Artificial intelligence (AI) has been developed for diagnosis and outcome prediction using various features, including clinicopathologic, radiologic, and genetic variables, which can achieve inspiring results. One of the most recent tasks of AI is to use state-of-the-art deep learning technique to detect both early esophageal squamous cell carcinoma and esophageal adenocarcinoma in Barrett’s esophagus. In this review, we aim to provide a comprehensive overview of the ways in which AI may help physicians diagnose advanced cancer and make clinical decisions based on predicted outcomes, and combine the endoscopic images to detect precancerous lesions or early cancer. Pertinent studies conducted in recent two years have surged in numbers, with large datasets and external validation from multi-centers, and have partly achieved intriguing results of expert’s performance of AI in real time. Improved pre-trained computer-aided diagnosis algorithms in the future studies with larger training and external validation datasets, aiming at real-time video processing, are imperative to produce a diagnostic efficacy similar to or even superior to experienced endoscopists. Meanwhile, supervised randomized controlled trials in real clinical practice are highly essential for a solid conclusion, which meets patient-centered satisfaction. Notably, ethical and legal issues regarding the black-box nature of computer algorithms should be addressed, for both clinicians and regulators. Baishideng Publishing Group Inc 2020-09-21 2020-09-21 /pmc/articles/PMC7504247/ /pubmed/32994686 http://dx.doi.org/10.3748/wjg.v26.i35.5256 Text en ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved. http://creativecommons.org/licenses/by-nc/4.0/ This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. |
spellingShingle | Minireviews Zhang, Yu-Hang Guo, Lin-Jie Yuan, Xiang-Lei Hu, Bing Artificial intelligence-assisted esophageal cancer management: Now and future |
title | Artificial intelligence-assisted esophageal cancer management: Now and future |
title_full | Artificial intelligence-assisted esophageal cancer management: Now and future |
title_fullStr | Artificial intelligence-assisted esophageal cancer management: Now and future |
title_full_unstemmed | Artificial intelligence-assisted esophageal cancer management: Now and future |
title_short | Artificial intelligence-assisted esophageal cancer management: Now and future |
title_sort | artificial intelligence-assisted esophageal cancer management: now and future |
topic | Minireviews |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7504247/ https://www.ncbi.nlm.nih.gov/pubmed/32994686 http://dx.doi.org/10.3748/wjg.v26.i35.5256 |
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