Cited by 209Colorectal Cancer Screening and DetectionAI in cancer detectionColorectal Cancer Surgical Treatments

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Artificial intelligence-assisted colonoscopy for colorectal cancer screening: a multicenter randomized controlled trial

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Xu, Hong, Tang, Raymond S.Y., Lam, Thomas Y.T., Zhao, Guijun, Lau, James Y.W., Liu, Yunpeng, Wu, Qi, Rong, Long, Xu, Weiran, Li, Xue, Wong, Sunny H., Cai, Shuntian, Wang, Jing, Liu, Guanyi, Ma, Tantan, Liang, Xiong, Mak, Joyce W.Y., Xu, Hongzhi, Yuan, Peng, Cao, Tingting, Li, Fudong, Ye, Zhenshi, Shutian, Zhang, Sung, Joseph J.Y.·2023·Clinical Gastroenterology and Hepatology

DOI 10.1016/j.cgh.2022.07.006

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Abstract

BACKGROUND AND AIMS: 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. METHODS: 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). RESULTS: 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. CONCLUSIONS: 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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Zauber, A. G., Winawer, S. J., O’Brien, M. J., Lansdorp‐Vogelaar, I., van Ballegooijen, M., Hankey, B. F., Shi, W., Bond, J. H., Schapiro, M., Panish, J. F., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1100370 · Cited by 3,185
Adenoma detection rate and risk of colorectal cancer and death
Corley, D. A., Jensen, C. D., Marks, A. R., Zhao, W. K., Lee, J. K., Doubeni, C. A., Zauber, A. G., de Boer, J., Fireman, B. H., Schottinger, J. E., et al. (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1309086 · Cited by 2,270
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Baxter, N. N., Goldwasser, M. A., Paszat, L. F., Saskin, R., Urbach, D. R., & Rabeneck, L. (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-150-1-200901060-00306 · Cited by 1,251
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Dixon, M. F. (2002) · Gut · doi:10.1136/gut.51.1.130 · Cited by 688
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Show all 28 references

Research works printed but not linked: 2

Luo, 2021, Artificial intelligence-assisted colonoscopy for detection of colon polyps: a prospective, randomized cohort study, J Gastrointest Surg, 25, 2011, 10.1007/s11605-020-04802-4 · DOI
Redmon J, Farhadi A. YOLOv3: an incremental improvement. Presented at: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition; June 18-23, 2018; Salt Lake City, UT.

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Okumura, T., Imai, K., Misawa, M., Kudo, S., Hotta, K., Ito, S., Kishida, Y., Takada, K., Kawata, N., Maeda, Y. K., et al. (2024) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.16491 · Cited by 11
Effectiveness of artificial intelligence-assisted colonoscopy in detecting and diagnosing colorectal tumors: a systematic review and network meta-analysis
Tan, S., Zeng, P., Liu, S., Yang, Y., Chen, S., Zhang, W. Y., Li, X., Liu, D., Li, Y., & Chen, X. (2025) · International Journal of Colorectal Disease · doi:10.1007/s00384-025-04949-z · Cited by 11
Self-citation
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Zimmermann‐Fraedrich, K., Sehner, S., Rösch, T., Aschenbeck, J., Schubert, S., Liceni, T., Moog, G., Neumann, H., Berndt, R., Weigt, J., et al. (2025) · The American Journal of Gastroenterology · doi:10.14309/ajg.0000000000003500 · Cited by 10
Effects of artificial intelligence assistance on endoscopist performance: Comparison of diagnostic performance in superficial esophageal squamous cell carcinoma detection using video‐based models
Aoyama, N., Nakajo, K., Sasabe, M., Inaba, A., Nakanishi, Y., Seno, H., & Yano, T. (2025) · DEN Open · doi:10.1002/deo2.70083 · Cited by 10
Navigating the “Trough of Disillusionment” for CADe Polyp Detection: What Can We Learn About Negative AI Trials and the Physician-AI Hybrid?
Berzin, T. M., & Glissen Brown, J. (2023) · American Journal of Gastroenterology · doi:10.14309/ajg.0000000000002286 · Cited by 10
The application of the combination between artificial intelligence and endoscopy in gastrointestinal tumors
Shen, L., Xu, M., Meng, Y., Sun, H., Zhang, T., Yang, H., Li, Y., & Ma, X. (2024) · MedComm – Oncology · doi:10.1002/mog2.91 · Cited by 9
The implementation of computer-aided detection in an initial endoscopy training improves the quality measures of trainees’ future colonoscopies: a retrospective cohort study
Orzeszko, Z., Gach, T., Necka, S., Ochwat, K., Major, P., & Szura, M. (2025) · Surgical Endoscopy · doi:10.1007/s00464-025-11890-3 · Cited by 9
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Yabuuchi, Y., Hosotani, K., Morihisa, Y., Fujio, Y., Oshikawa, D., Oshita, M., Iketani, M., Tsukamoto, K., Sone, A., Nanjo, T., et al. (2025) · Digestive Endoscopy · doi:10.1111/den.15086 · Cited by 10
Effect of 3-Dimensional Imaging Device on Polyp and Adenoma Detection During Colonoscopy: A Randomized Controlled Trial
Sun, X., Zhang, Q., Wu, S., Xu, C., Zhang, Y., Hao, X., Meng, Y., Jiao, Y., Li, H., Zhu, S., et al. (2023) · The American Journal of Gastroenterology · doi:10.14309/ajg.0000000000002396 · Cited by 9
Self-citation
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Thomas, J., Ravichandran, R., Nag, A., Gupta, L., Singh, M., & Panjiyar, B. K. (2023) · Cureus · doi:10.7759/cureus.45278 · Cited by 8
“In 2023, Xu et al. [23] conducted a single-blind RCT that aimed to assess the efficacy of AI-assisted colonoscopy in an asymptomatic population eligible for CRC screening and involved endoscopists with varying levels of experience.”
Background
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Letafatkar, N., El‐Sehrawy, A. A. M. A., Prasad, K., Alkhayyat, A., Amini‐Salehi, E., Hasanpour, M., Namdar, M., Hashemi, M., Alotaibi, H., Rashidian, P., et al. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1532640 · Cited by 11
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Chen, D. C., Arnold, K., Sukhdeo, R., Alla, J. F., & Raman, S. (2025) · BMJ Oncology · doi:10.1136/bmjonc-2025-000733 · Cited by 16
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Self-citation
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Dimopoulou, K., Spinou, M., Ioannou, A., Nakou, E., Zormpas, P., & Tribonias, G. (2025) · World Journal of Gastroenterology · doi:10.3748/wjg.v31.i40.111499 · Cited by 8
Tumor cell- and infiltrating immune cell-based supervised learning artificial intelligence multimodal platform for tumor prognosis
Cai, X., Peng, C.-R., Ding, C.-Y., Cui, Y., Gao, L., Xu, Z., Li, L., Zhang, J., & Li, T. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-01125-y · Cited by 9
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Ali, S. A., Kokila, S., Alagarsamy, M., & Kumar, U. A. (2026) · Microsystem Technologies · doi:10.1007/s00542-026-06030-3 · Cited by 8
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Shinozaki, S., Watanabe, J., Kanno, T., Nakazawa, K., & Yano, T. (2026) · Digestive Endoscopy · doi:10.1111/den.70138 · Cited by 8
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Kim, J. H., Wang, J., Pence, C., Magahis, P. T., Dawod, E., Schnoll‐Sussman, F. H., Sharaiha, R. Z., & Wan, D. W. (2025) · Clinical Endoscopy · doi:10.5946/ce.2024.271 · Cited by 8
Self-citation
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Misumi, Y., Nonaka, K., Takeuchi, M., Kamitani, Y., Uechi, Y., Watanabe, M., Kishino, M., Omori, T., Yonezawa, M., Isomoto, H., et al. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12144840 · Cited by 8
Artificial intelligence in clinical oncology: Multimodal integration and translational development
Lin, R., Zhao, Z., Liu, Z., Kang, J., Zhang, K., Huang, X., & Yu, Y. (2026) · Cancer Letters · doi:10.1016/j.canlet.2026.218493 · Cited by 7
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Pedersen, T. A., Mori, Y., Botteri, E., Engjom, T., Seip, B., Dimcevski, G. G., & Havre, R. F. (2026) · Endoscopy · doi:10.1055/a-2858-7084 · Cited by 10
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Galati, J. S., Lin, K., & Gross, S. A. (2023) · Expert Review of Medical Devices · doi:10.1080/17434440.2023.2280773 · Cited by 7
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Mota, J., Almeida, M. J., Martins, M., Mendes, F., Cardoso, P., Afonso, J. P. L., Ribeiro, T., Ferreira, J. P. S., Fonseca, F., Limbert, M., et al. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13195842 · Cited by 7
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Zhang, C., Tao, X., Pan, J., Huang, L., Dong, Z., Lin, J., Su, H. W., Zhu, Y., Du, H., Xiao, B., et al. (2024) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.16847 · Cited by 7
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Soleymanjahi, S., Rajashekar, N., Chung, S., Grimshaw, A. A., Tvedt, M. J. K., Foroutan, F., Sultan, S., Shung, D. L., & Kolb, J. M. (2025) · Gastro Hep Advances · doi:10.1016/j.gastha.2025.100746 · Cited by 6
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Bazerbachi, F., Murad, F., Kubiliun, N. M., Adams, M. A., Shahidi, N. C., Visrodia, K., Essex, E. A., Raju, G. S., Greenberg, C. C., Day, L. W., et al. (2025) · VideoGIE · doi:10.1016/j.vgie.2024.09.013 · Cited by 5
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Self-citation
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Suska, K., Piotrowski, M., Jacenik, D., & Fichna, J. (2026) · Molecules · doi:10.3390/molecules31081339 · Cited by 5
Long-term impact of artificial intelligence on colorectal adenoma detection in high-risk colonoscopy
Chow, K. W., Bell, M. T., Cumpian, N. A., Amour, M., Hsu, R. H., Eysselein, V. E., Srivastava, N., Fleischman, M. W., & Reicher, S. (2024) · World Journal of Gastrointestinal Endoscopy · doi:10.4253/wjge.v16.i6.335 · Cited by 5
Artificial intelligence and the push for small adenomas: all we need?
Zimmermann‐Fraedrich, K., & Rösch, T. (2023) · Endoscopy · doi:10.1055/a-2038-7078 · Cited by 5
AI-based algorithm for clinical decision support system in colonoscopy
Мтвралашвили, Д. А., Шахматов, Д. Г., Ликутов, А. А., Zapolskiy, A. G., Suslova, D. I., Borodinov, A. A., Sushkov, O. I., & Иванович, А. С. (2023) · Koloproktologia · doi:10.33878/2073-7556-2023-22-2-92-102 · Cited by 5
The 2023 top 10 list of endoscopy topics in medical publishing: an annual review by the American Society for Gastrointestinal Endoscopy Editorial Board
Martínez, M., Bartel, M. J., Chua, T. Y., Dakhoul, L., Fatima, H., Jensen, D., Lara, L. F., Tadros, M., Villa, E. C., Yang, D. J., et al. (2024) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2024.05.002 · Cited by 5
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Rabba, W., Asif, F., Younis, M. Y., Nasrullah, H., Fatima, L., & Arif, M. A. (2025) · Cureus · doi:10.7759/cureus.98528 · Cited by 6
Validation of Artificial Intelligence Computer-Aided Detection of Colonic Neoplasm in Colonoscopy
Lee, H., Chung, J., Kim, K. O., Kwon, K. A., Kim, J. H., Yun, S.-C., Jung, S. W., Sheeraz, A., Yoon, Y. J., Kim, J. H., et al. (2024) · Diagnostics · doi:10.3390/diagnostics14232762 · Cited by 4
Vendor‐Agnostic Vision Transformer‐Based Artificial Intelligence for Peroral Cholangioscopy: Diagnostic Performance in Biliary Strictures Compared With Convolutional Neural Networks and Endoscopists
Sato, R., Matsumoto, K., Tomiya, M., Tanimoto, T., Ohto, A., Oki, K., Kajitani, S., Kikuchi, T., Matsumi, A., Miyamoto, K., et al. (2025) · Digestive Endoscopy · doi:10.1111/den.70028 · Cited by 4

Authors: Xu, H., Tang, R. S. Y., Lam, T. Y. T., Zhao, G., Lau, J. Y. W., Liu, Y., Wu, Q., Rong, L., Xu, W., Li, X., Wong, S. H., Cai, S., Wang, J., Liu, G., Ma, T., Liang, X., Mak, J. W. Y., Xu, H., Yuan, P., Cao, T., Li, F., Ye, Z., Shutian, Z., & Sung, J. J. Y.
Affiliations: Xiamen University (CN)
Chinese University of Hong Kong (HK)
Inner Mongolia People's Hospital (CN)
Peking University Cancer Hospital (CN)
Peking University First Hospital (CN)
First Hospital of Jilin University (CN)
Nanyang Technological University (SG)
Corresponding author: Sung, Joseph J.Y.
ORCID: Xu, Hong
Tang, Raymond S.Y.
Lam, Thomas Y.T.
Zhao, Guijun
Lau, James Y.W.
Liu, Yunpeng
Wu, Qi
Rong, Long
Xu, Weiran
Li, Xue
Wong, Sunny H.
Cai, Shuntian
Liu, Guanyi
Mak, Joyce W.Y.
Xu, Hongzhi
Cao, Tingting
Li, Fudong
Sung, Joseph J.Y.
Venue: Clinical Gastroenterology and Hepatology
Publisher: Elsevier BV
Published: 2023-02
Volume: 21
Issue: 2
Pages: 337-346.e3
ISSN: 1542-3565 (ISSN-L)
DOI: 10.1016/j.cgh.2022.07.006
OpenAlex: W4286257050
Cited by: 209
References: 28
Citations by year: 2026: 37 · 2025: 79 · 2024: 54 · 2023: 36
FWCI: 17.97
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Locations: Clinical Gastroenterology and Hepatology (publishedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
DR-NTU (Nanyang Technological University) (submittedVersion)
Topics: Health Sciences › Medicine › Oncology › Colorectal Cancer Screening and Detection
Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Health Sciences › Medicine › Oncology › Colorectal Cancer Surgical Treatments
Keywords: AI-assisted colonoscopy, colorectal cancer screening, adenoma detection rate, polyp detection, asymptomatic population, endoscopist experience, colonoscopy withdrawal time, fecal immunochemical test, bowel preparation, advanced adenoma, Hong Kong, China, Mongolia
MeSH terms: Adenoma, Artificial Intelligence, Colonic Polyps, Colonoscopy, Humans, Colorectal Neoplasms, Randomized Controlled Trials as Topic, Early Detection of Cancer

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Artificial intelligence-assisted colonoscopy for colorectal cancer screening: a multicenter randomized controlled trial

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