基于人工智能的结直肠疾病实时筛查结肠镜检查

IF 5.1 4区 医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL
Jia-Ling Jiang, Qianrong Xie, Zhuo Cheng, Jianqiang Cai, Tian Xia, Hang Yang, Bo Yang, Hui-min Peng, Xue-song Bai, Mingque Yan, Xue Li, Jun Zhou, Xuan Huang, Liang Wang, Haiyan Long, Pingxi Wang, Yanpeng Chu, Fanwei Zeng, Xiu-wei Zhang, Guangyu Wang, Fanxin Zeng
{"title":"基于人工智能的结直肠疾病实时筛查结肠镜检查","authors":"Jia-Ling Jiang, Qianrong Xie, Zhuo Cheng, Jianqiang Cai, Tian Xia, Hang Yang, Bo Yang, Hui-min Peng, Xue-song Bai, Mingque Yan, Xue Li, Jun Zhou, Xuan Huang, Liang Wang, Haiyan Long, Pingxi Wang, Yanpeng Chu, Fanwei Zeng, Xiu-wei Zhang, Guangyu Wang, Fanxin Zeng","doi":"10.1093/pcmedi/pbab013","DOIUrl":null,"url":null,"abstract":"Abstract Colonoscopy is an effective tool for early screening of colorectal diseases. However, the application of colonoscopy in distinguishing different intestinal diseases still faces great challenges of efficiency and accuracy. Here we constructed and evaluated a deep convolution neural network (CNN) model based on 117 055 images from 16 004 individuals, which achieved a high accuracy of 0.933 in the validation dataset in identifying patients with polyp, colitis, colorectal cancer (CRC) from normal. The proposed approach was further validated on multi-center real-time colonoscopy videos and images, which achieved accurate diagnostic performance on detecting colorectal diseases with high accuracy and precision to generalize across external validation datasets. The diagnostic performance of the model was further compared to the skilled endoscopists and the novices. In addition, our model has potential in diagnosis of adenomatous polyp and hyperplastic polyp with an area under the receiver operating characteristic curve of 0.975. Our proposed CNN models have potential in assisting clinicians in making clinical decisions with efficiency during application.","PeriodicalId":33608,"journal":{"name":"Precision Clinical Medicine","volume":"42 1","pages":"109 - 118"},"PeriodicalIF":5.1000,"publicationDate":"2021-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"AI based colorectal disease detection using real-time screening colonoscopy\",\"authors\":\"Jia-Ling Jiang, Qianrong Xie, Zhuo Cheng, Jianqiang Cai, Tian Xia, Hang Yang, Bo Yang, Hui-min Peng, Xue-song Bai, Mingque Yan, Xue Li, Jun Zhou, Xuan Huang, Liang Wang, Haiyan Long, Pingxi Wang, Yanpeng Chu, Fanwei Zeng, Xiu-wei Zhang, Guangyu Wang, Fanxin Zeng\",\"doi\":\"10.1093/pcmedi/pbab013\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Colonoscopy is an effective tool for early screening of colorectal diseases. However, the application of colonoscopy in distinguishing different intestinal diseases still faces great challenges of efficiency and accuracy. Here we constructed and evaluated a deep convolution neural network (CNN) model based on 117 055 images from 16 004 individuals, which achieved a high accuracy of 0.933 in the validation dataset in identifying patients with polyp, colitis, colorectal cancer (CRC) from normal. The proposed approach was further validated on multi-center real-time colonoscopy videos and images, which achieved accurate diagnostic performance on detecting colorectal diseases with high accuracy and precision to generalize across external validation datasets. The diagnostic performance of the model was further compared to the skilled endoscopists and the novices. In addition, our model has potential in diagnosis of adenomatous polyp and hyperplastic polyp with an area under the receiver operating characteristic curve of 0.975. Our proposed CNN models have potential in assisting clinicians in making clinical decisions with efficiency during application.\",\"PeriodicalId\":33608,\"journal\":{\"name\":\"Precision Clinical Medicine\",\"volume\":\"42 1\",\"pages\":\"109 - 118\"},\"PeriodicalIF\":5.1000,\"publicationDate\":\"2021-05-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Precision Clinical Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1093/pcmedi/pbab013\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Precision Clinical Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/pcmedi/pbab013","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 1

摘要

结肠镜检查是早期筛查结直肠疾病的有效工具。然而,结肠镜检查在区分不同肠道疾病中的应用在效率和准确性方面仍面临很大的挑战。本文基于来自16 004个人的117 055张图像构建并评估了一个深度卷积神经网络(CNN)模型,该模型在验证数据集中对息肉、结肠炎、结直肠癌(CRC)患者与正常人的识别准确率达到0.933。在多中心实时结肠镜检查视频和图像上进一步验证了该方法,在检测结直肠疾病方面取得了准确的诊断性能,具有较高的准确性和精密度,可以跨外部验证数据集进行推广。将该模型的诊断性能与熟练内窥镜医师和新手进行比较。此外,我们的模型在腺瘤性息肉和增生性息肉的诊断中具有潜力,受者工作特征曲线下面积为0.975。我们提出的CNN模型在帮助临床医生在应用过程中有效地做出临床决策方面具有潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI based colorectal disease detection using real-time screening colonoscopy
Abstract Colonoscopy is an effective tool for early screening of colorectal diseases. However, the application of colonoscopy in distinguishing different intestinal diseases still faces great challenges of efficiency and accuracy. Here we constructed and evaluated a deep convolution neural network (CNN) model based on 117 055 images from 16 004 individuals, which achieved a high accuracy of 0.933 in the validation dataset in identifying patients with polyp, colitis, colorectal cancer (CRC) from normal. The proposed approach was further validated on multi-center real-time colonoscopy videos and images, which achieved accurate diagnostic performance on detecting colorectal diseases with high accuracy and precision to generalize across external validation datasets. The diagnostic performance of the model was further compared to the skilled endoscopists and the novices. In addition, our model has potential in diagnosis of adenomatous polyp and hyperplastic polyp with an area under the receiver operating characteristic curve of 0.975. Our proposed CNN models have potential in assisting clinicians in making clinical decisions with efficiency during application.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Precision Clinical Medicine
Precision Clinical Medicine MEDICINE, RESEARCH & EXPERIMENTAL-
CiteScore
10.80
自引率
0.00%
发文量
26
审稿时长
5 weeks
期刊介绍: Precision Clinical Medicine (PCM) is an international, peer-reviewed, open access journal that provides timely publication of original research articles, case reports, reviews, editorials, and perspectives across the spectrum of precision medicine. The journal's mission is to deliver new theories, methods, and evidence that enhance disease diagnosis, treatment, prevention, and prognosis, thereby establishing a vital communication platform for clinicians and researchers that has the potential to transform medical practice. PCM encompasses all facets of precision medicine, which involves personalized approaches to diagnosis, treatment, and prevention, tailored to individual patients or patient subgroups based on their unique genetic, phenotypic, or psychosocial profiles. The clinical conditions addressed by the journal include a wide range of areas such as cancer, infectious diseases, inherited diseases, complex diseases, and rare diseases.
×
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