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Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening
Colorectal cancer (CRC) is the third most common cancer worldwide, with the highest incidence reported in high-income countries. However, because of the slow progression of neoplastic precursors, along with the opportunity for their endoscopic detection and resection, a well-designed endoscopic scre...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10047293/ https://www.ncbi.nlm.nih.gov/pubmed/36980409 http://dx.doi.org/10.3390/diagnostics13061102 |
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author | Spadaccini, Marco Massimi, Davide Mori, Yuichi Alfarone, Ludovico Fugazza, Alessandro Maselli, Roberta Sharma, Prateek Facciorusso, Antonio Hassan, Cesare Repici, Alessandro |
author_facet | Spadaccini, Marco Massimi, Davide Mori, Yuichi Alfarone, Ludovico Fugazza, Alessandro Maselli, Roberta Sharma, Prateek Facciorusso, Antonio Hassan, Cesare Repici, Alessandro |
author_sort | Spadaccini, Marco |
collection | PubMed |
description | Colorectal cancer (CRC) is the third most common cancer worldwide, with the highest incidence reported in high-income countries. However, because of the slow progression of neoplastic precursors, along with the opportunity for their endoscopic detection and resection, a well-designed endoscopic screening program is expected to strongly decrease colorectal cancer incidence and mortality. In this regard, quality of colonoscopy has been clearly related with the risk of post-colonoscopy colorectal cancer. Recently, the development of artificial intelligence (AI) applications in the medical field has been growing in interest. Through machine learning processes, and, more recently, deep learning, if a very high numbers of learning samples are available, AI systems may automatically extract specific features from endoscopic images/videos without human intervention, helping the endoscopists in different aspects of their daily practice. The aim of this review is to summarize the current knowledge on AI-aided endoscopy, and to outline its potential role in colorectal cancer prevention. |
format | Online Article Text |
id | pubmed-10047293 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100472932023-03-29 Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening Spadaccini, Marco Massimi, Davide Mori, Yuichi Alfarone, Ludovico Fugazza, Alessandro Maselli, Roberta Sharma, Prateek Facciorusso, Antonio Hassan, Cesare Repici, Alessandro Diagnostics (Basel) Review Colorectal cancer (CRC) is the third most common cancer worldwide, with the highest incidence reported in high-income countries. However, because of the slow progression of neoplastic precursors, along with the opportunity for their endoscopic detection and resection, a well-designed endoscopic screening program is expected to strongly decrease colorectal cancer incidence and mortality. In this regard, quality of colonoscopy has been clearly related with the risk of post-colonoscopy colorectal cancer. Recently, the development of artificial intelligence (AI) applications in the medical field has been growing in interest. Through machine learning processes, and, more recently, deep learning, if a very high numbers of learning samples are available, AI systems may automatically extract specific features from endoscopic images/videos without human intervention, helping the endoscopists in different aspects of their daily practice. The aim of this review is to summarize the current knowledge on AI-aided endoscopy, and to outline its potential role in colorectal cancer prevention. MDPI 2023-03-14 /pmc/articles/PMC10047293/ /pubmed/36980409 http://dx.doi.org/10.3390/diagnostics13061102 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Spadaccini, Marco Massimi, Davide Mori, Yuichi Alfarone, Ludovico Fugazza, Alessandro Maselli, Roberta Sharma, Prateek Facciorusso, Antonio Hassan, Cesare Repici, Alessandro Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening |
title | Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening |
title_full | Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening |
title_fullStr | Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening |
title_full_unstemmed | Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening |
title_short | Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening |
title_sort | artificial intelligence-aided endoscopy and colorectal cancer screening |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10047293/ https://www.ncbi.nlm.nih.gov/pubmed/36980409 http://dx.doi.org/10.3390/diagnostics13061102 |
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