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dc.contributor.author
Jiang, Jiawei
dc.contributor.author
Xie, Qianrong
dc.contributor.author
Cheng, Zhuo
dc.contributor.author
Cai, Jianqiang
dc.contributor.author
Xia, Tian
dc.contributor.author
Yang, Hang
dc.contributor.author
Yang, Bo
dc.contributor.author
Peng, Hui
dc.contributor.author
Bai, Xuesong
dc.contributor.author
Yan, Mingque
dc.contributor.author
Li, Xue
dc.contributor.author
Zhou, Jun
dc.contributor.author
Huang, Xuan
dc.contributor.author
Wang, Liang
dc.contributor.author
Long, Haiyan
dc.contributor.author
Wang, Pingxi
dc.contributor.author
Chu, Yanpeng
dc.contributor.author
Zeng, Fan-Wei
dc.contributor.author
Zhang, Xiuqin
dc.contributor.author
Wang, Guangyu
dc.contributor.author
Zeng, Fanxin
dc.date.accessioned
2021-09-03T14:39:27Z
dc.date.available
2021-08-18T02:46:40Z
dc.date.available
2021-09-03T14:39:27Z
dc.date.issued
2021-06
dc.identifier.other
10.1093/pcmedi/pbab013
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/501220
dc.identifier.doi
10.3929/ethz-b-000501220
dc.description.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.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Oxford University Press
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc/4.0/
dc.subject
Artificial intelligence (AI)
en_US
dc.subject
Colorectal disease
en_US
dc.subject
Real-time colonoscopy
en_US
dc.title
AI based colorectal disease detection using real-time screening colonoscopy
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution-NonCommercial 4.0 International
dc.date.published
2021-05-20
ethz.journal.title
Precision Clinical Medicine
ethz.journal.volume
4
en_US
ethz.journal.issue
2
en_US
ethz.pages.start
109
en_US
ethz.pages.end
118
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.scopus
ethz.publication.place
Oxford
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2021-08-18T02:46:43Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-09-03T14:39:37Z
ethz.rosetta.lastUpdated
2022-03-29T11:29:25Z
ethz.rosetta.versionExported
true
ethz.COinS
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