Artificial Intelligence–Assisted Colonoscopy for Colorectal Cancer Screening: A Multicenter Randomized Controlled Trial
Artificial intelligence (AI)–assisted colonoscopy improves polyp detection and characterization in colonoscopy. However, data from large-scale multicenter randomized controlled trials (RCT) in an asymptomatic population are lacking. This multicenter RCT aimed to compare AI-assisted colonoscopy with...
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| Published in | Clinical gastroenterology and hepatology Vol. 21; no. 2; pp. 337 - 346.e3 |
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| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
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United States
Elsevier Inc
01.02.2023
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| Abstract | Artificial intelligence (AI)–assisted colonoscopy improves polyp detection and characterization in colonoscopy. However, data from large-scale multicenter randomized controlled trials (RCT) in an asymptomatic population are lacking.
This multicenter RCT aimed to compare AI-assisted colonoscopy with conventional colonoscopy for adenoma detection in an asymptomatic population. Asymptomatic subjects 45–75 years of age undergoing colorectal cancer screening by direct colonoscopy or fecal immunochemical test were recruited in 6 referral centers in Hong Kong, Jilin, Inner Mongolia, Xiamen, and Beijing. In the AI-assisted colonoscopy, an AI polyp detection system (Eagle-Eye) with real-time notification on the same monitor of the endoscopy system was used. The primary outcome was overall adenoma detection rate (ADR). Secondary outcomes were mean number of adenomas per colonoscopy, ADR according to endoscopist’s experience, and colonoscopy withdrawal time. This study received Institutional Review Board approval (CRE-2019.393).
From November 2019 to August 2021, 3059 subjects were randomized to AI-assisted colonoscopy (n = 1519) and conventional colonoscopy (n = 1540). Baseline characteristics and bowel preparation quality between the 2 groups were similar. The overall ADR (39.9% vs 32.4%; P < .001), advanced ADR (6.6% vs 4.9%; P = .041), ADR of expert (42.3% vs 32.8%; P < .001) and nonexpert endoscopists (37.5% vs 32.1%; P = .023), and adenomas per colonoscopy (0.59 ± 0.97 vs 0.45 ± 0.81; P < .001) were all significantly higher in the AI-assisted colonoscopy. The median withdrawal time (8.3 minutes vs 7.8 minutes; P = .004) was slightly longer in the AI-assisted colonoscopy group.
In this multicenter RCT in asymptomatic patients, AI-assisted colonoscopy improved overall ADR, advanced ADR, and ADR of both expert and nonexpert attending endoscopists. (ClinicalTrials.gov, Number: NCT04422548). |
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| AbstractList | Background and AimsArtificial intelligence (AI)–assisted colonoscopy improves polyp detection and characterization in colonoscopy. However, data from large-scale multicenter randomized controlled trials (RCT) in an asymptomatic population are lacking. MethodsThis multicenter RCT aimed to compare AI-assisted colonoscopy with conventional colonoscopy for adenoma detection in an asymptomatic population. Asymptomatic subjects 45–75 years of age undergoing colorectal cancer screening by direct colonoscopy or fecal immunochemical test were recruited in 6 referral centers in Hong Kong, Jilin, Inner Mongolia, Xiamen, and Beijing. In the AI-assisted colonoscopy, an AI polyp detection system (Eagle-Eye) with real-time notification on the same monitor of the endoscopy system was used. The primary outcome was overall adenoma detection rate (ADR). Secondary outcomes were mean number of adenomas per colonoscopy, ADR according to endoscopist’s experience, and colonoscopy withdrawal time. This study received Institutional Review Board approval (CRE-2019.393). ResultsFrom November 2019 to August 2021, 3059 subjects were randomized to AI-assisted colonoscopy (n = 1519) and conventional colonoscopy (n = 1540). Baseline characteristics and bowel preparation quality between the 2 groups were similar. The overall ADR (39.9% vs 32.4%; P < .001), advanced ADR (6.6% vs 4.9%; P = .041), ADR of expert (42.3% vs 32.8%; P < .001) and nonexpert endoscopists (37.5% vs 32.1%; P = .023), and adenomas per colonoscopy (0.59 ± 0.97 vs 0.45 ± 0.81; P < .001) were all significantly higher in the AI-assisted colonoscopy. The median withdrawal time (8.3 minutes vs 7.8 minutes; P = .004) was slightly longer in the AI-assisted colonoscopy group. ConclusionsIn this multicenter RCT in asymptomatic patients, AI-assisted colonoscopy improved overall ADR, advanced ADR, and ADR of both expert and nonexpert attending endoscopists. ( ClinicalTrials.gov, Number: NCT04422548). Artificial intelligence (AI)–assisted colonoscopy improves polyp detection and characterization in colonoscopy. However, data from large-scale multicenter randomized controlled trials (RCT) in an asymptomatic population are lacking. This multicenter RCT aimed to compare AI-assisted colonoscopy with conventional colonoscopy for adenoma detection in an asymptomatic population. Asymptomatic subjects 45–75 years of age undergoing colorectal cancer screening by direct colonoscopy or fecal immunochemical test were recruited in 6 referral centers in Hong Kong, Jilin, Inner Mongolia, Xiamen, and Beijing. In the AI-assisted colonoscopy, an AI polyp detection system (Eagle-Eye) with real-time notification on the same monitor of the endoscopy system was used. The primary outcome was overall adenoma detection rate (ADR). Secondary outcomes were mean number of adenomas per colonoscopy, ADR according to endoscopist’s experience, and colonoscopy withdrawal time. This study received Institutional Review Board approval (CRE-2019.393). From November 2019 to August 2021, 3059 subjects were randomized to AI-assisted colonoscopy (n = 1519) and conventional colonoscopy (n = 1540). Baseline characteristics and bowel preparation quality between the 2 groups were similar. The overall ADR (39.9% vs 32.4%; P < .001), advanced ADR (6.6% vs 4.9%; P = .041), ADR of expert (42.3% vs 32.8%; P < .001) and nonexpert endoscopists (37.5% vs 32.1%; P = .023), and adenomas per colonoscopy (0.59 ± 0.97 vs 0.45 ± 0.81; P < .001) were all significantly higher in the AI-assisted colonoscopy. The median withdrawal time (8.3 minutes vs 7.8 minutes; P = .004) was slightly longer in the AI-assisted colonoscopy group. In this multicenter RCT in asymptomatic patients, AI-assisted colonoscopy improved overall ADR, advanced ADR, and ADR of both expert and nonexpert attending endoscopists. (ClinicalTrials.gov, Number: NCT04422548). Artificial intelligence (AI)-assisted colonoscopy improves polyp detection and characterization in colonoscopy. However, data from large-scale multicenter randomized controlled trials (RCT) in an asymptomatic population are lacking.BACKGROUND AND AIMSArtificial intelligence (AI)-assisted colonoscopy improves polyp detection and characterization in colonoscopy. However, data from large-scale multicenter randomized controlled trials (RCT) in an asymptomatic population are lacking.This multicenter RCT aimed to compare AI-assisted colonoscopy with conventional colonoscopy for adenoma detection in an asymptomatic population. Asymptomatic subjects 45-75 years of age undergoing colorectal cancer screening by direct colonoscopy or fecal immunochemical test were recruited in 6 referral centers in Hong Kong, Jilin, Inner Mongolia, Xiamen, and Beijing. In the AI-assisted colonoscopy, an AI polyp detection system (Eagle-Eye) with real-time notification on the same monitor of the endoscopy system was used. The primary outcome was overall adenoma detection rate (ADR). Secondary outcomes were mean number of adenomas per colonoscopy, ADR according to endoscopist's experience, and colonoscopy withdrawal time. This study received Institutional Review Board approval (CRE-2019.393).METHODSThis multicenter RCT aimed to compare AI-assisted colonoscopy with conventional colonoscopy for adenoma detection in an asymptomatic population. Asymptomatic subjects 45-75 years of age undergoing colorectal cancer screening by direct colonoscopy or fecal immunochemical test were recruited in 6 referral centers in Hong Kong, Jilin, Inner Mongolia, Xiamen, and Beijing. In the AI-assisted colonoscopy, an AI polyp detection system (Eagle-Eye) with real-time notification on the same monitor of the endoscopy system was used. The primary outcome was overall adenoma detection rate (ADR). Secondary outcomes were mean number of adenomas per colonoscopy, ADR according to endoscopist's experience, and colonoscopy withdrawal time. This study received Institutional Review Board approval (CRE-2019.393).From November 2019 to August 2021, 3059 subjects were randomized to AI-assisted colonoscopy (n = 1519) and conventional colonoscopy (n = 1540). Baseline characteristics and bowel preparation quality between the 2 groups were similar. The overall ADR (39.9% vs 32.4%; P < .001), advanced ADR (6.6% vs 4.9%; P = .041), ADR of expert (42.3% vs 32.8%; P < .001) and nonexpert endoscopists (37.5% vs 32.1%; P = .023), and adenomas per colonoscopy (0.59 ± 0.97 vs 0.45 ± 0.81; P < .001) were all significantly higher in the AI-assisted colonoscopy. The median withdrawal time (8.3 minutes vs 7.8 minutes; P = .004) was slightly longer in the AI-assisted colonoscopy group.RESULTSFrom November 2019 to August 2021, 3059 subjects were randomized to AI-assisted colonoscopy (n = 1519) and conventional colonoscopy (n = 1540). Baseline characteristics and bowel preparation quality between the 2 groups were similar. The overall ADR (39.9% vs 32.4%; P < .001), advanced ADR (6.6% vs 4.9%; P = .041), ADR of expert (42.3% vs 32.8%; P < .001) and nonexpert endoscopists (37.5% vs 32.1%; P = .023), and adenomas per colonoscopy (0.59 ± 0.97 vs 0.45 ± 0.81; P < .001) were all significantly higher in the AI-assisted colonoscopy. The median withdrawal time (8.3 minutes vs 7.8 minutes; P = .004) was slightly longer in the AI-assisted colonoscopy group.In this multicenter RCT in asymptomatic patients, AI-assisted colonoscopy improved overall ADR, advanced ADR, and ADR of both expert and nonexpert attending endoscopists. (ClinicalTrials.gov, Number: NCT04422548).CONCLUSIONSIn this multicenter RCT in asymptomatic patients, AI-assisted colonoscopy improved overall ADR, advanced ADR, and ADR of both expert and nonexpert attending endoscopists. (ClinicalTrials.gov, Number: NCT04422548). |
| Author | Sung, Joseph J.Y. Xu, Weiran Liu, Yunpeng Shutian, Zhang Cai, Shuntian Yuan, Peng Lau, James Y.W. Ma, Tantan Li, Fudong Xu, Hong Xu, Hongzhi Mak, Joyce W.Y. Wang, Jing Ye, Zhenshi Wu, Qi Lam, Thomas Y.T. Liang, Xiong Li, Xue Rong, Long Cao, Tingting Zhao, Guijun Liu, Guanyi Tang, Raymond S.Y. Wong, Sunny H. |
| Author_xml | – sequence: 1 givenname: Hong surname: Xu fullname: Xu, Hong organization: Department of Gastroenterology and Endoscopy Center, First Hospital of Jilin University, Jilin, China – sequence: 2 givenname: Raymond S.Y. surname: Tang fullname: Tang, Raymond S.Y. organization: Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China – sequence: 3 givenname: Thomas Y.T. surname: Lam fullname: Lam, Thomas Y.T. organization: Institute of Digestive Disease, Chinese University of Hong Kong, Hong Kong SAR, China – sequence: 4 givenname: Guijun surname: Zhao fullname: Zhao, Guijun organization: Department of Endoscopy Center, Inner Mongolia Key Laboratory of Endoscopic Digestive Diseases, Inner Mongolia People’s Hospital, Hohhot, China – sequence: 5 givenname: James Y.W. surname: Lau fullname: Lau, James Y.W. organization: Institute of Digestive Disease, Chinese University of Hong Kong, Hong Kong SAR, China – sequence: 6 givenname: Yunpeng surname: Liu fullname: Liu, Yunpeng organization: Department of Gastroenterology, Zhongshan Hospital Affiliated to Xiamen University, Xiamen, China – sequence: 7 givenname: Qi surname: Wu fullname: Wu, Qi organization: Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China – sequence: 8 givenname: Long surname: Rong fullname: Rong, Long organization: Endoscopy Center, Peking University First Hospital, Beijing, China – sequence: 9 givenname: Weiran surname: Xu fullname: Xu, Weiran organization: Department of Gastroenterology and Endoscopy Center, First Hospital of Jilin University, Jilin, China – sequence: 10 givenname: Xue surname: Li fullname: Li, Xue organization: Department of Endoscopy Center, Inner Mongolia Key Laboratory of Endoscopic Digestive Diseases, Inner Mongolia People’s Hospital, Hohhot, China – sequence: 11 givenname: Sunny H. surname: Wong fullname: Wong, Sunny H. organization: Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China – sequence: 12 givenname: Shuntian surname: Cai fullname: Cai, Shuntian organization: Department of Gastroenterology, Zhongshan Hospital Affiliated to Xiamen University, Xiamen, China – sequence: 13 givenname: Jing surname: Wang fullname: Wang, Jing organization: Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China – sequence: 14 givenname: Guanyi surname: Liu fullname: Liu, Guanyi organization: Endoscopy Center, Peking University First Hospital, Beijing, China – sequence: 15 givenname: Tantan surname: Ma fullname: Ma, Tantan organization: Department of Gastroenterology and Endoscopy Center, First Hospital of Jilin University, Jilin, China – sequence: 16 givenname: Xiong surname: Liang fullname: Liang, Xiong organization: Department of Endoscopy Center, Inner Mongolia Key Laboratory of Endoscopic Digestive Diseases, Inner Mongolia People’s Hospital, Hohhot, China – sequence: 17 givenname: Joyce W.Y. surname: Mak fullname: Mak, Joyce W.Y. organization: Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China – sequence: 18 givenname: Hongzhi surname: Xu fullname: Xu, Hongzhi organization: Department of Gastroenterology, Zhongshan Hospital Affiliated to Xiamen University, Xiamen, China – sequence: 19 givenname: Peng surname: Yuan fullname: Yuan, Peng organization: Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China – sequence: 20 givenname: Tingting surname: Cao fullname: Cao, Tingting organization: Department of Gastroenterology and Endoscopy Center, First Hospital of Jilin University, Jilin, China – sequence: 21 givenname: Fudong surname: Li fullname: Li, Fudong organization: Department of Gastroenterology and Endoscopy Center, First Hospital of Jilin University, Jilin, China – sequence: 22 givenname: Zhenshi surname: Ye fullname: Ye, Zhenshi organization: Department of Gastroenterology, Zhongshan Hospital Affiliated to Xiamen University, Xiamen, China – sequence: 23 givenname: Zhang surname: Shutian fullname: Shutian, Zhang organization: Department of Gastroenterology and Hepatology, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing, China – sequence: 24 givenname: Joseph J.Y. orcidid: 0000-0003-3125-5199 surname: Sung fullname: Sung, Joseph J.Y. email: josephsung@ntu.edu.sg organization: Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China |
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| Snippet | Artificial intelligence (AI)–assisted colonoscopy improves polyp detection and characterization in colonoscopy. However, data from large-scale multicenter... Background and AimsArtificial intelligence (AI)–assisted colonoscopy improves polyp detection and characterization in colonoscopy. However, data from... Artificial intelligence (AI)-assisted colonoscopy improves polyp detection and characterization in colonoscopy. However, data from large-scale multicenter... |
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| SubjectTerms | Adenoma - diagnosis Artificial Intelligence Colonic Polyps - diagnosis Colonoscopy Colorectal Cancer Screening Colorectal Neoplasms - diagnosis Early Detection of Cancer Gastroenterology and Hepatology Humans Randomized Controlled Trials as Topic |
| Title | Artificial Intelligence–Assisted Colonoscopy for Colorectal Cancer Screening: A Multicenter Randomized Controlled Trial |
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