Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study
Abstract
Background Colonoscopy performance varies among endoscopists, impairing the discovery of colorectal cancers and precursor lesions. We aimed to construct a real-time quality improvement system (ENDOANGEL) to monitor real-time withdrawal speed and colonoscopy withdrawal time and to remind endoscopists of blind spots caused by endoscope slipping. We also aimed to evaluate the effectiveness of this system for improving adenoma yield of everyday colonoscopy. Methods The ENDOANGEL system was developed using deep neural networks and perceptual hash algorithms. We recruited consecutive patients aged 18-75 years from Renmin Hospital of Wuhan University in China who provided written informed consent. We randomly assigned patients (1:1) using computer-generated random numbers and block randomisation (block size of four) to either colonoscopy with the ENDOANGEL system or unassisted colonoscopy (control). Endoscopists were not masked to the random assignment but analysts and patients were unaware of random assignments. The primary endpoint was the adenoma detection rate (ADR), which is the proportion of patients having one or more adenomas detected at colonoscopy. The primary analysis was done per protocol (ie, in all patients having colonoscopy done in accordance with the assigned intervention) and by intention to treat (ie, in all randomised patients). This trial is registered with http://www.chictr.org.cn, ChiCTR1900021984. Findings Between June 18, 2019, and Sept 6, 2019, 704 patients…
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In Gong et al. 2020, the ENDOANGEL-assisted group had a higher ADR (16%) than the control group (8%) (p=0.0010).
“With 704 participants, the ENDOANGEL-assisted group exhibited a notably higher ADR (16%) compared to the control group (8%) (p=0.0010).”
From Advancing Colorectal Cancer Screening: A Comprehensive Systematic Review of Artificial Intelligence (AI)-Assisted Versus Routine Colonoscopy (2023) · cites this paper as [16]
In Gong et al. 2020, the ENDOANGEL-assisted group had a higher PDR (47%) than the control group (34%) (p=0.0016).
“PDR was also higher (47%) than the control group (34%) (p=0.0016).”
From Advancing Colorectal Cancer Screening: A Comprehensive Systematic Review of Artificial Intelligence (AI)-Assisted Versus Routine Colonoscopy (2023) · cites this paper as [16]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196 · see citing works
Mori, Y., Kudo, S.-E., Misawa, M., Saito, Y., Ikematsu, H., Hotta, K., Ohtsuka, K., Urushibara, F., Kataoka, S., Ogawa, Y., et al. (2018) · Annals of Internal Medicine · doi:10.7326/m18-0249 · Cited by 509 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-021-01614-0 · Cited by 3,212 · see citing works
Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Chan, A.-W., Darzi, A., Holmes, C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1034-x · Cited by 1,421 · see citing works
Cruz Rivera, S., Liu, X., Chan, A.-W., Denniston, A. K., Calvert, M. J., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Darzi, A., Holmes, C., Yau, C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1037-7 · Cited by 748 · see citing works
Liu, X., Rivera, S. C., Moher, D., Calvert, M. J., & Denniston, A. K. (2020) · BMJ · doi:10.1136/bmj.m3164 · Cited by 702 · see citing works
Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., Ashrafian, H., Beam, A. L., Chan, A.-W., Collins, G. S., Deeks, A. D. J., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30218-1 · Cited by 555 · see citing works
Hassan, C., Spadaccini, M., Iannone, A., Maselli, R., Jovani, M., Chandrasekar, V. T., Antonelli, G., Yu, H., Areia, M., Dinis-Ribeiro, M., et al. (2021) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2020.06.059 · Cited by 529 · see citing works
Self-citationCruz Rivera, S., Liu, X., Chan, A.-W., Denniston, A. K., Calvert, M. J., Ashrafian, H., Beam, A. L., Collins, G. S., Darzi, A., Deeks, J. J., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30219-3 · Cited by 486 · see citing works
Rivera, S. C., Liu, X., Chan, A.-W., Denniston, A. K., & Calvert, M. J. (2020) · BMJ · doi:10.1136/bmj.m3210 · Cited by 463 · see citing works
Artificial intelligence in surgery
Varghese, C., Harrison, E. M., O’Grady, G., & Topol, E. J. (2024) · Nature Medicine · doi:10.1038/s41591-024-02970-3 · Cited by 287 · see citing works
Randomized Clinical Trials of Machine Learning Interventions in Health Care: A Systematic Review
Plana, D., Shung, D. L., Grimshaw, A. A., Saraf, A., Sung, J. J. Y., & Kann, B. H. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.33946 · Cited by 236 · see citing works
Hassan, C., Spadaccini, M., Mori, Y., Foroutan, F., Facciorusso, A., Gkolfakis, P., Tziatzios, G., Triantafyllou, K., Antonelli, G., Khalaf, K., et al. (2023) · Annals of Internal Medicine · doi:10.7326/m22-3678 · Cited by 216 · see citing works
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., et al. (2023) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2022.07.006 · Cited by 209 · see citing works
Randomized Controlled Trials of Artificial Intelligence in Clinical Practice: Systematic Review
Lam, T. Y. T., Cheung, M. F. K., Munro, Y. L., Lim, K. M., Shung, D., & Sung, J. J. Y. (2022) · Journal of Medical Internet Research · doi:10.2196/37188 · Cited by 120 · see citing works
“To evaluate the noninferiority of day 30 dosimetry between a machine learning–based treatment planning system for prostate low-dose-rate brachytherapy and the conventional manual planning technique Nicolae et al [37], 2020”
Berzin, T. M., Parasa, S., Wallace, M. B., Gross, S. A., Repici, A., & Sharma, P. (2020) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2020.06.035 · Cited by 102 · see citing works
Karsenti, D., Tharsis, G., Perrot, B., Cattan, P., Percie du Sert, A., Venezia, F., Zrihen, E., Gillet, A., Lab, J.-P., Tordjman, G., et al. (2023) · The Lancet Gastroenterology & Hepatology · doi:10.1016/s2468-1253(23)00104-8 · Cited by 62 · see citing works
Guerrero Vinsard, D., Fetzer, J. R., Agrawal, U., Singh, J., Damani, D. N., Sivasubramaniam, P., Poigai Arunachalam, S., Leggett, C. L., Raffals, L. E., & Coelho-Prabhu, N. (2023) · iGIE · doi:10.1016/j.igie.2023.03.004 · Cited by 43 · see citing works
Enhancing human-AI collaboration: The case of colonoscopy
Introzzi, L., Zonca, J., Cabitza, F., Cherubini, P., & Reverberi, C. (2024) · Digestive and Liver Disease · doi:10.1016/j.dld.2023.10.018 · Cited by 28 · see citing works
Artificial Intelligence in the Diagnosis of Colorectal Cancer: A Literature Review
Uchikov, P., Khalid, U., Kraev, K., Hristov, B., Kraeva, M., Tenchev, T., Chakarov, D., Sandeva, M., Dragusheva, S., Taneva, D., et al. (2024) · Diagnostics · doi:10.3390/diagnostics14050528 · Cited by 22 · see citing works
Artificial intelligence in colonoscopy: A review on the current status
Larsen, S. L. V., & Mori, Y. (2022) · DEN Open · doi:10.1002/deo2.109 · Cited by 21 · see citing works
Artificial Intelligence-assisted colonoscopy and colorectal cancer screening: Where are we going?
Spadaccini, M., Troya, J., Khalaf, K., Facciorusso, A., Maselli, R., Hann, A., & Repici, A. (2024) · Digestive and Liver Disease · doi:10.1016/j.dld.2024.01.203 · Cited by 20 · see citing works
Thomas, J., Ravichandran, R., Nag, A., Gupta, L., Singh, M., & Panjiyar, B. K. (2023) · Cureus · doi:10.7759/cureus.45278 · Cited by 8 · see citing works
“Among the 13 studies, eight were conducted in China, two in Japan, one in France, one in Italy, and one in the USA; three out of 13 trials were non-blinded [8,14,15], eight trials were single-blinded RCTs [16,17,19,20,22-25], and two were double-blinded RCTs [18,21].”
Background
- Authors
- Gong, D., Wu, L., Zhang, J., Mu, G., Shen, L., Liu, J., Wang, Z., Zhou, W., An, P., Huang, X., Jiang, X., Li, Y., Wan, X., Hu, S., Chen, Y., Hu, X., Xu, Y., Zhu, X., Li, S., Yao, L., He, X., Chen, D., Huang, L., Wei, X., Wang, X., & Yu, H.
- Affiliations
- Renmin Hospital of Wuhan University (CN)
- Wuhan University (CN)
- Corresponding author
- Yu, Honggang
- ORCID
- Venue
- The Lancet Gastroenterology & Hepatology
- Publisher
- Elsevier BV
- Published
- 2020-04
- Volume
- 5
- Issue
- 4
- Pages
- 352-361
- ISSN
- 2468-1156 (ISSN-L)
- 2468-1253
- OpenAlex
- W3002021892
- Cited by
- 393
- References
- 2
- Citations by year
- 2026: 34 · 2025: 56 · 2024: 52 · 2023: 85 · 2022: 55 · 2021: 74 · 2020: 37
- FWCI
- 22.67
- Type
- journal article
- Language
- en
- Update notices
- erratum (2020-04-01)
- Open access
- closed
- Locations
- The Lancet. Gastroenterology & hepatology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (81672387)
- Major Technology Innovation of Hubei Province (2018-916-000-008)
- 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
- colorectal adenoma detection, colonoscopy withdrawal time, blind spots, deep neural network, adenoma detection rate, colorectal cancer prevention, real-time feedback system, colonoscopy training
- MeSH terms
- Adenoma, Adult, Algorithms, China, Colonic Polyps, Colonoscopy, Diagnosis, Computer-Assisted, Female, Humans, Male, Middle Aged, Optic Disk, Case-Control Studies, Single-Blind Method, Neural Networks, Computer, Early Diagnosis, Intention to Treat Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
