Copyright
©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Nov 6, 2021; 9(31): 9376-9385
Published online Nov 6, 2021. doi: 10.12998/wjcc.v9.i31.9376
Published online Nov 6, 2021. doi: 10.12998/wjcc.v9.i31.9376
Deep learning driven colorectal lesion detection in gastrointestinal endoscopic and pathological imaging
Yu-Wen Cai, Li-Yuan Lu, Chen Chen, Ping Lin, Yu-Shan Xue, Department of Clinical Medicine, Fujian Medical University, Fuzhou 350004, Fujian Province, China
Fang-Fen Dong, Department of Medical Technology and Engineering, Fujian Medical University, Fuzhou 350004, Fujian Province, China
Yu-Heng Shi, Computer Science and Engineering College, University of Alberta, Edmonton T6G 2R3, Canada
Jian-Hua Chen, Su-Yu Chen, Endoscopy Center, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou 350014, Fujian Province, China
Xiong-Biao Luo, Department of Computer Science, Xiamen University, Xiamen 361005, Fujian, China
Author contributions: Cai YW and Dong FF performed the majority of the writing and prepared the figures and tables, and they contributed equally to the work and should be regarded as co-first authors; Shi YH performed data accusation and writing; Lu LY, Chen C, Lin P, Xue YS, and Chen JH provided the input in writing the paper; Chen SY and Luo XB designed the outline and coordinated the writing of the paper.
Conflict-of-interest statement: The authors declare that there are no conflicts of interest regarding the publication of this paper.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Su-Yu Chen, MD, Endoscopy Center, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, No. 420 Fuma Road, Jin’an District, Fuzhou 350014, Fujian Province, China. endosuyuchen@163.com
Received: June 15, 2021
Peer-review started: June 15, 2021
First decision: July 15, 2021
Revised: July 26, 2021
Accepted: August 13, 2021
Article in press: August 13, 2021
Published online: November 6, 2021
Processing time: 136 Days and 0.4 Hours
Peer-review started: June 15, 2021
First decision: July 15, 2021
Revised: July 26, 2021
Accepted: August 13, 2021
Article in press: August 13, 2021
Published online: November 6, 2021
Processing time: 136 Days and 0.4 Hours
Core Tip
Core Tip: The development of computer technology has promoted the progress of medical treatment. Artificial intelligence (AI) has been gradually applied in the medical field and achieved good results. The detection of colorectal lesions in the conventional gastrointestinal endoscopy is difficult, the diagnosis time is long, and there is often the problem of missed diagnosis and misdiagnosis. AI is a good aid for doctors. In this review, we summarize the application of AI in the detection of colorectal lesions in recent years, in order to provide reference for the follow-up development and research.