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Artificial Intelligence Enhances Studies on Inflammatory Bowel Disease
Inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn’s disease (CD), is an idiopathic condition related to a dysregulated immune response to commensal intestinal microflora in a genetically susceptible host. As a global disease, the morbidity of IBD reached a rate of 84...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8297505/ https://www.ncbi.nlm.nih.gov/pubmed/34307315 http://dx.doi.org/10.3389/fbioe.2021.635764 |
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author | Chen, Guihua Shen, Jun |
author_facet | Chen, Guihua Shen, Jun |
author_sort | Chen, Guihua |
collection | PubMed |
description | Inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn’s disease (CD), is an idiopathic condition related to a dysregulated immune response to commensal intestinal microflora in a genetically susceptible host. As a global disease, the morbidity of IBD reached a rate of 84.3 per 100,000 persons and reflected a continued gradual upward trajectory. The medical cost of IBD is also notably extremely high. For example, in Europe, it has €3,500 in CD and €2,000 in UC per patient per year, respectively. In addition, taking into account the work productivity loss and the reduced quality of life, the indirect costs are incalculable. In modern times, the diagnosis of IBD is still a subjective judgment based on laboratory tests and medical images. Its early diagnosis and intervention is therefore a challenging goal and also the key to control its progression. Artificial intelligence (AI)-assisted diagnosis and prognosis prediction has proven effective in many fields including gastroenterology. In this study, support vector machines were utilized to distinguish the significant features in IBD. As a result, the reliability of IBD diagnosis due to its impressive performance in classifying and addressing region problems was improved. Convolutional neural networks are advanced image processing algorithms that are currently in existence. Digestive endoscopic images can therefore be better understood by automatically detecting and classifying lesions. This study aims to summarize AI application in the area of IBD, objectively evaluate the performance of these methods, and ultimately understand the algorithm–dataset combination in the studies. |
format | Online Article Text |
id | pubmed-8297505 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82975052021-07-23 Artificial Intelligence Enhances Studies on Inflammatory Bowel Disease Chen, Guihua Shen, Jun Front Bioeng Biotechnol Bioengineering and Biotechnology Inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn’s disease (CD), is an idiopathic condition related to a dysregulated immune response to commensal intestinal microflora in a genetically susceptible host. As a global disease, the morbidity of IBD reached a rate of 84.3 per 100,000 persons and reflected a continued gradual upward trajectory. The medical cost of IBD is also notably extremely high. For example, in Europe, it has €3,500 in CD and €2,000 in UC per patient per year, respectively. In addition, taking into account the work productivity loss and the reduced quality of life, the indirect costs are incalculable. In modern times, the diagnosis of IBD is still a subjective judgment based on laboratory tests and medical images. Its early diagnosis and intervention is therefore a challenging goal and also the key to control its progression. Artificial intelligence (AI)-assisted diagnosis and prognosis prediction has proven effective in many fields including gastroenterology. In this study, support vector machines were utilized to distinguish the significant features in IBD. As a result, the reliability of IBD diagnosis due to its impressive performance in classifying and addressing region problems was improved. Convolutional neural networks are advanced image processing algorithms that are currently in existence. Digestive endoscopic images can therefore be better understood by automatically detecting and classifying lesions. This study aims to summarize AI application in the area of IBD, objectively evaluate the performance of these methods, and ultimately understand the algorithm–dataset combination in the studies. Frontiers Media S.A. 2021-07-08 /pmc/articles/PMC8297505/ /pubmed/34307315 http://dx.doi.org/10.3389/fbioe.2021.635764 Text en Copyright © 2021 Chen and Shen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioengineering and Biotechnology Chen, Guihua Shen, Jun Artificial Intelligence Enhances Studies on Inflammatory Bowel Disease |
title | Artificial Intelligence Enhances Studies on Inflammatory Bowel Disease |
title_full | Artificial Intelligence Enhances Studies on Inflammatory Bowel Disease |
title_fullStr | Artificial Intelligence Enhances Studies on Inflammatory Bowel Disease |
title_full_unstemmed | Artificial Intelligence Enhances Studies on Inflammatory Bowel Disease |
title_short | Artificial Intelligence Enhances Studies on Inflammatory Bowel Disease |
title_sort | artificial intelligence enhances studies on inflammatory bowel disease |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8297505/ https://www.ncbi.nlm.nih.gov/pubmed/34307315 http://dx.doi.org/10.3389/fbioe.2021.635764 |
work_keys_str_mv | AT chenguihua artificialintelligenceenhancesstudiesoninflammatoryboweldisease AT shenjun artificialintelligenceenhancesstudiesoninflammatoryboweldisease |