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Cai YW, Dong FF, Shi YH, Lu LY, Chen C, Lin P, Xue YS, Chen JH, Chen SY, Luo XB. Deep learning driven colorectal lesion detection in gastrointestinal endoscopic and pathological imaging. World J Clin Cases 2021; 9:9376-9385. [PMID: 34877273 PMCID: PMC8610875 DOI: 10.12998/wjcc.v9.i31.9376] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 07/26/2021] [Accepted: 08/13/2021] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer has the second highest incidence of malignant tumors and is the fourth leading cause of cancer deaths in China. Early diagnosis and treatment of colorectal cancer will lead to an improvement in the 5-year survival rate, which will reduce medical costs. The current diagnostic methods for early colorectal cancer include excreta, blood, endoscopy, and computer-aided endoscopy. In this paper, research on image analysis and prediction of colorectal cancer lesions based on deep learning is reviewed with the goal of providing a reference for the early diagnosis of colorectal cancer lesions by combining computer technology, 3D modeling, 5G remote technology, endoscopic robot technology, and surgical navigation technology. The findings will supplement the research and provide insights to improve the cure rate and reduce the mortality of colorectal cancer.
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Affiliation(s)
- Yu-Wen Cai
- 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
| | - Li-Yuan Lu
- Department of Clinical Medicine, Fujian Medical University, Fuzhou 350004, Fujian Province, China
| | - Chen Chen
- Department of Clinical Medicine, Fujian Medical University, Fuzhou 350004, Fujian Province, China
| | - Ping Lin
- Department of Clinical Medicine, Fujian Medical University, Fuzhou 350004, Fujian Province, China
| | - Yu-Shan Xue
- Department of Clinical Medicine, Fujian Medical University, Fuzhou 350004, Fujian Province, China
| | - Jian-Hua Chen
- Endoscopy Center, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou 350014, Fujian Province, China
| | - 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
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Sotiriadis A, Odibo AO. Systematic error and cognitive bias in obstetric ultrasound. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2019; 53:431-435. [PMID: 30701628 DOI: 10.1002/uog.20232] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2019] [Accepted: 01/24/2019] [Indexed: 06/09/2023]
Abstract
Linked Comment: Ultrasound Obstet Gynecol 2019; 53: 454-464.
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Affiliation(s)
- A Sotiriadis
- Second Department of Obstetrics and Gynecology, Faculty of Medicine, Aristotle University of Thessaloniki, 92 Tsimiski Str, 54622, Thessaloniki, Greece
| | - A O Odibo
- Department of Obstetrics and Gynecology, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
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