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Artificial Intelligence in Gastric Cancer Imaging With Emphasis on Diagnostic Imaging and Body Morphometry
Gastric cancer remains a significant global health concern, coercing the need for advancements in imaging techniques for ensuring accurate diagnosis and effective treatment planning. Artificial intelligence (AI) has emerged as a potent tool for gastric-cancer imaging, particularly for diagnostic ima...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Korean Gastric Cancer Association
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10412978/ https://www.ncbi.nlm.nih.gov/pubmed/37553127 http://dx.doi.org/10.5230/jgc.2023.23.e30 |
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author | Kim, Kyung Won Huh, Jimi Urooj, Bushra Lee, Jeongjin Lee, Jinseok Lee, In-Seob Park, Hyesun Na, Seongwon Ko, Yousun |
author_facet | Kim, Kyung Won Huh, Jimi Urooj, Bushra Lee, Jeongjin Lee, Jinseok Lee, In-Seob Park, Hyesun Na, Seongwon Ko, Yousun |
author_sort | Kim, Kyung Won |
collection | PubMed |
description | Gastric cancer remains a significant global health concern, coercing the need for advancements in imaging techniques for ensuring accurate diagnosis and effective treatment planning. Artificial intelligence (AI) has emerged as a potent tool for gastric-cancer imaging, particularly for diagnostic imaging and body morphometry. This review article offers a comprehensive overview of the recent developments and applications of AI in gastric cancer imaging. We investigated the role of AI imaging in gastric cancer diagnosis and staging, showcasing its potential to enhance the accuracy and efficiency of these crucial aspects of patient management. Additionally, we explored the application of AI body morphometry specifically for assessing the clinical impact of gastrectomy. This aspect of AI utilization holds significant promise for understanding postoperative changes and optimizing patient outcomes. Furthermore, we examine the current state of AI techniques for the prognosis of patients with gastric cancer. These prognostic models leverage AI algorithms to predict long-term survival outcomes and assist clinicians in making informed treatment decisions. However, the implementation of AI techniques for gastric cancer imaging has several limitations. As AI continues to evolve, we hope to witness the translation of cutting-edge technologies into routine clinical practice, ultimately improving patient care and outcomes in the fight against gastric cancer. |
format | Online Article Text |
id | pubmed-10412978 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Korean Gastric Cancer Association |
record_format | MEDLINE/PubMed |
spelling | pubmed-104129782023-08-11 Artificial Intelligence in Gastric Cancer Imaging With Emphasis on Diagnostic Imaging and Body Morphometry Kim, Kyung Won Huh, Jimi Urooj, Bushra Lee, Jeongjin Lee, Jinseok Lee, In-Seob Park, Hyesun Na, Seongwon Ko, Yousun J Gastric Cancer Review Article Gastric cancer remains a significant global health concern, coercing the need for advancements in imaging techniques for ensuring accurate diagnosis and effective treatment planning. Artificial intelligence (AI) has emerged as a potent tool for gastric-cancer imaging, particularly for diagnostic imaging and body morphometry. This review article offers a comprehensive overview of the recent developments and applications of AI in gastric cancer imaging. We investigated the role of AI imaging in gastric cancer diagnosis and staging, showcasing its potential to enhance the accuracy and efficiency of these crucial aspects of patient management. Additionally, we explored the application of AI body morphometry specifically for assessing the clinical impact of gastrectomy. This aspect of AI utilization holds significant promise for understanding postoperative changes and optimizing patient outcomes. Furthermore, we examine the current state of AI techniques for the prognosis of patients with gastric cancer. These prognostic models leverage AI algorithms to predict long-term survival outcomes and assist clinicians in making informed treatment decisions. However, the implementation of AI techniques for gastric cancer imaging has several limitations. As AI continues to evolve, we hope to witness the translation of cutting-edge technologies into routine clinical practice, ultimately improving patient care and outcomes in the fight against gastric cancer. The Korean Gastric Cancer Association 2023-07 2023-07-31 /pmc/articles/PMC10412978/ /pubmed/37553127 http://dx.doi.org/10.5230/jgc.2023.23.e30 Text en Copyright © 2023. Korean Gastric Cancer Association https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0 (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Article Kim, Kyung Won Huh, Jimi Urooj, Bushra Lee, Jeongjin Lee, Jinseok Lee, In-Seob Park, Hyesun Na, Seongwon Ko, Yousun Artificial Intelligence in Gastric Cancer Imaging With Emphasis on Diagnostic Imaging and Body Morphometry |
title | Artificial Intelligence in Gastric Cancer Imaging With Emphasis on Diagnostic Imaging and Body Morphometry |
title_full | Artificial Intelligence in Gastric Cancer Imaging With Emphasis on Diagnostic Imaging and Body Morphometry |
title_fullStr | Artificial Intelligence in Gastric Cancer Imaging With Emphasis on Diagnostic Imaging and Body Morphometry |
title_full_unstemmed | Artificial Intelligence in Gastric Cancer Imaging With Emphasis on Diagnostic Imaging and Body Morphometry |
title_short | Artificial Intelligence in Gastric Cancer Imaging With Emphasis on Diagnostic Imaging and Body Morphometry |
title_sort | artificial intelligence in gastric cancer imaging with emphasis on diagnostic imaging and body morphometry |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10412978/ https://www.ncbi.nlm.nih.gov/pubmed/37553127 http://dx.doi.org/10.5230/jgc.2023.23.e30 |
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