Impact of Artificial Intelligence in Colorectal Polyp Detection and Characterization

IF 0.4 Q4 GASTROENTEROLOGY & HEPATOLOGY
S. Afzalpurkar, Mahesh K Goenka, Rakesh Kochhar
{"title":"Impact of Artificial Intelligence in Colorectal Polyp Detection and Characterization","authors":"S. Afzalpurkar, Mahesh K Goenka, Rakesh Kochhar","doi":"10.1055/s-0043-1777330","DOIUrl":null,"url":null,"abstract":"Abstract Colorectal cancer (CRC) is the third most common cancer in the world. Colonoscopy has contributed significantly to reduction of incidence and mortality of CRC. Integration of artificial intelligence (AI) into colonoscopy practice has addressed the various shortcomings of screening colonoscopies. AI-assisted colonoscopy will help in real-time recognition of type of polyp with probable histology. This will not only save time but will also help to mitigate human errors. Computer-aided detection and computer-aided characterization are two applications of AI, which are being studied extensively with a goal of improvement of polyp and adenoma detection rates. Several studies are being conducted across the globe, which either involve simple decision-making algorithms or complex patterns through neural networks, which imitate the human brain. Most data are collected retrospectively and the research is limited to single-center studies, which might have bias. Therefore, the future research on AI in colonoscopy should aim to develop more sophisticated convolutional neural network and deep learning models that will help to standardize the practice and ensure the same degree of accuracy with all the colonoscopies, irrespective of experience of performing endoscopists. In this review, we will take a closer look at the current state of AI and its integration into the field of colonoscopy.","PeriodicalId":43098,"journal":{"name":"Journal of Digestive Endoscopy","volume":"36 1","pages":""},"PeriodicalIF":0.4000,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Digestive Endoscopy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1055/s-0043-1777330","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"GASTROENTEROLOGY & HEPATOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Abstract Colorectal cancer (CRC) is the third most common cancer in the world. Colonoscopy has contributed significantly to reduction of incidence and mortality of CRC. Integration of artificial intelligence (AI) into colonoscopy practice has addressed the various shortcomings of screening colonoscopies. AI-assisted colonoscopy will help in real-time recognition of type of polyp with probable histology. This will not only save time but will also help to mitigate human errors. Computer-aided detection and computer-aided characterization are two applications of AI, which are being studied extensively with a goal of improvement of polyp and adenoma detection rates. Several studies are being conducted across the globe, which either involve simple decision-making algorithms or complex patterns through neural networks, which imitate the human brain. Most data are collected retrospectively and the research is limited to single-center studies, which might have bias. Therefore, the future research on AI in colonoscopy should aim to develop more sophisticated convolutional neural network and deep learning models that will help to standardize the practice and ensure the same degree of accuracy with all the colonoscopies, irrespective of experience of performing endoscopists. In this review, we will take a closer look at the current state of AI and its integration into the field of colonoscopy.
人工智能对结直肠息肉检测和定性的影响
摘要 大肠癌(CRC)是全球第三大常见癌症。结肠镜检查在降低 CRC 发病率和死亡率方面做出了巨大贡献。将人工智能(AI)融入结肠镜检查实践解决了结肠镜筛查的各种缺陷。人工智能辅助结肠镜检查将有助于实时识别息肉类型和可能的组织学。这不仅可以节省时间,还有助于减少人为错误。计算机辅助检测和计算机辅助特征描述是人工智能的两项应用,目前正在对这两项应用进行广泛研究,目的是提高息肉和腺瘤的检出率。全球正在开展多项研究,这些研究或涉及简单的决策算法,或涉及通过神经网络模仿人脑的复杂模式。大多数数据都是回顾性收集的,而且研究仅限于单中心研究,可能存在偏差。因此,未来有关结肠镜检查的人工智能研究应着眼于开发更复杂的卷积神经网络和深度学习模型,这将有助于规范操作,并确保所有结肠镜检查都具有相同的准确度,而与内镜医师的经验无关。在这篇综述中,我们将仔细研究人工智能的现状及其与结肠镜检查领域的结合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Journal of Digestive Endoscopy
Journal of Digestive Endoscopy GASTROENTEROLOGY & HEPATOLOGY-
自引率
28.60%
发文量
35
审稿时长
22 weeks
期刊介绍: The Journal of Digestive Endoscopy (JDE) is the official publication of the Society of Gastrointestinal Endoscopy of India that has over 1500 members. The society comprises of several key clinicians in this field from different parts of the country and has key international speakers in its advisory board. JDE is a double-blinded peer-reviewed, print and online journal publishing quarterly. It focuses on original investigations, reviews, case reports and clinical images as well as key investigations including but not limited to cholangiopancreatography, fluoroscopy, capsule endoscopy etc.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信