The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Real-Time Computer-Aided Detection of Colorectal Neoplasia During Colonoscopy: A Systematic Review and Meta-analysis
Abstract
BACKGROUND: Artificial intelligence computer-aided detection (CADe) of colorectal neoplasia during colonoscopy may increase adenoma detection rates (ADRs) and reduce adenoma miss rates, but it may increase overdiagnosis and overtreatment of nonneoplastic polyps. PURPOSE: To quantify the benefits and harms of CADe in randomized trials. DESIGN: Systematic review and meta-analysis. (PROSPERO: CRD42022293181). DATA SOURCES: Medline, Embase, and Scopus databases through February 2023. STUDY SELECTION: Randomized trials comparing CADe-assisted with standard colonoscopy for polyp and cancer detection. DATA EXTRACTION: Adenoma detection rate (proportion of patients with ≥1 adenoma), number of adenomas detected per colonoscopy, advanced adenoma (≥10 mm with high-grade dysplasia and villous histology), number of serrated lesions per colonoscopy, and adenoma miss rate were extracted as benefit outcomes. Number of polypectomies for nonneoplastic lesions and withdrawal time were extracted as harm outcomes. For each outcome, studies were pooled using a random-effects model. Certainty of evidence was assessed using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) framework. DATA SYNTHESIS: Twenty-one randomized trials on 18 232 patients were included. The ADR was higher in the CADe group than in the standard colonoscopy group (44.0% vs. 35.9%; relative risk, 1.24 [95% CI, 1.16 to 1.33]; low-certainty evidence), corresponding to a 55% (risk ratio, 0.45 [CI, 0.35 to 0.58]) relative reduction in miss rate (moderate-certainty evidence). More nonneoplastic polyps were removed in the CADe than the standard group (0.52 vs. 0.34 per colonoscopy; mean difference [MD], 0.18 polypectomy [CI, 0.11 to 0.26 polypectomy]; low-certainty evidence). Mean inspection time increased only marginally with CADe (MD, 0.47 minute [CI, 0.23 to 0.72 minute]; moderate-certainty evidence). LIMITATIONS: This review focused on surrogates of patient-important outcomes. Most patients, however, may consider cancer incidence and cancer-related mortality important outcomes. The effect of CADe on such patient-important outcomes remains unclear. CONCLUSION: The use of CADe for polyp detection during colonoscopy results in increased detection of adenomas but not advanced adenomas and in higher rates of unnecessary removal of nonneoplastic polyps. PRIMARY FUNDING SOURCE: European Commission Horizon 2020 Marie Skłodowska-Curie Individual Fellowship.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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 the citing paper
“Meta-analyses report a ~14% relative improvement in the ADR over standard colonoscopy [39,40].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [40]
Citations
Cites
Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21660 · Cited by 108,699
Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & the PRISMA Group* (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-151-4-200908180-00135 · Cited by 37,554
GRADE: an emerging consensus on rating quality of evidence and strength of recommendations
Guyatt, G. H., Oxman, A. D., Vist, G. E., Kunz, R., Falck-Ytter, Y., Alonso-Coello, P., & Schünemann, H. J. (2008) · BMJ · doi:10.1136/bmj.39489.470347.ad · Cited by 23,305
Genetic Alterations during Colorectal-Tumor Development
Vogelstein, B., Fearon, E. R., Hamilton, S. R., Kern, S. E., Preisinger, A. C., Leppert, M., de Vries-Smits, A. M. M., & Bos, J. L. (1988) · New England Journal of Medicine · doi:10.1056/nejm198809013190901 · Cited by 6,704
IntHout, J., Ioannidis, J. P., & Borm, G. F. (2014) · BMC Medical Research Methodology · doi:10.1186/1471-2288-14-25 · Cited by 2,419
Plea for routinely presenting prediction intervals in meta-analysis.
IntHout, J., Ioannidis, J. P. A., Rovers, M. M., & Goeman, J. J. (2016) · BMJ Open · doi:10.1136/bmjopen-2015-010247 · Cited by 2,264
Bias and Efficiency of Meta-Analytic Variance Estimators in the Random-Effects Model
Viechtbauer, W. (2005) · Journal of Educational and Behavioral Statistics · doi:10.3102/10769986030003261 · Cited by 1,699 · cited 1×
“The restricted maximum likelihood estimator was used to calculate the heterogeneity variance (13).”
MethodWang, P., Berzin, T. M., Glissen Brown, J. R., Bharadwaj, S., Becq, A., Xiao, X., Liu, P., Li, L., Song, Y., Zhang, D., et al. (2019) · Gut · doi:10.1136/gutjnl-2018-317500 · Cited by 865
Effect of Colonoscopy Screening on Risks of Colorectal Cancer and Related Death
Bretthauer, M., Løberg, M., Wieszczy, P., Kalager, M., Emilsson, L., Garborg, K., Rupinski, M., Dekker, E., Spaander, M., Bugajski, M., et al. (2022) · New England Journal of Medicine · doi:10.1056/nejmoa2208375 · Cited by 817
Zhao, S., Wang, S., Pan, P., Xia, T., Chang, X., Yang, X., Guo, L., Meng, Q., Yang, F., Qian, W., et al. (2019) · Gastroenterology · doi:10.1053/j.gastro.2019.01.260 · Cited by 660
Efficacy of real-time computer-aided detection of colorectal neoplasia in a randomized trial
Repici, A., Badalamenti, M., Maselli, R., Correale, L., Radaelli, F., Rondonotti, E., Ferrara, E., Spadaccini, M., Alkandari, A., Fugazza, A., et al. (2020) · Gastroenterology · doi:10.1053/j.gastro.2020.04.062 · Cited by 646
Byrne, M. F., Chapados, N., Soudan, F., Oertel, C., Linares Pérez, M., Kelly, R., Iqbal, N., Chandelier, F., & Rex, D. K. (2019) · Gut · doi:10.1136/gutjnl-2017-314547 · Cited by 635
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
Wang, P., Liu, X., Berzin, T. M., Glissen Brown, J. R., Liu, P., Zhou, C., Lei, L., Li, L., Guo, Z., Lei, S., et al. (2020) · The Lancet Gastroenterology & Hepatology · doi:10.1016/s2468-1253(19)30411-x · Cited by 415
Gong, D., Wu, L., Zhang, J., Mu, G., Shen, L., Liu, J., Wang, Z., Zhou, W., An, P., Huang, X., et al. (2020) · The Lancet Gastroenterology & Hepatology · doi:10.1016/s2468-1253(19)30413-3 · Cited by 393
Su, J.-R., Li, Z., Shao, X.-J., Ji, C.-R., Ji, R., Zhou, R.-C., Li, G.-C., Liu, G.-Q., He, Y.-S., Zuo, X.-L., et al. (2020) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2019.08.026 · Cited by 286
Impact of artificial intelligence on miss rate of colorectal neoplasia
Wallace, M. B., Sharma, P., Bhandari, P., East, J., Antonelli, G., Lorenzetti, R., Vieth, M., Speranza, I., Spadaccini, M., Desai, M., et al. (2022) · Gastroenterology · doi:10.1053/j.gastro.2022.03.007 · Cited by 273
Artificial intelligence and colonoscopy experience: lessons from two randomised trials
Repici, A., Spadaccini, M., Antonelli, G., Correale, L., Maselli, R., Galtieri, P. A., Pellegatta, G., Capogreco, A., Milluzzo, S. M., Lollo, G., et al. (2021) · Gut · doi:10.1136/gutjnl-2021-324471 · Cited by 255
Glissen Brown, J. R., Mansour, N. M., Wang, P., Chuchuca, M. A., Minchenberg, S. B., Chandnani, M., Liu, L., Gross, S. A., Sengupta, N., & Berzin, T. M. (2022) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2021.09.009 · Cited by 235
Wang, P., Liu, P., Glissen Brown, J. R., Berzin, T. M., Zhou, G., Lei, S., Liu, X., Li, L., & Xiao, X. (2020) · Gastroenterology · doi:10.1053/j.gastro.2020.06.023 · Cited by 231
Atkin, W. S., Valori, R., Kuipers, E. J., Hoff, G. S., Senore, C., Segnan, N., Jover, R., Schmiegel, W., Lambert, R. G. W., & Pox, C. P. (2012) · Endoscopy · doi:10.1055/s-0032-1309821 · Cited by 220
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
Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy
Huang, J., Liu, W.-N., Zhang, Y.-Y., Bian, X.-Q., Wang, L.-J., Yang, Q., & Zhang, X.-D. (2020) · Saudi Journal of Gastroenterology · doi:10.4103/sjg.sjg_377_19 · Cited by 179
Association of Physician Adenoma Detection Rates With Postcolonoscopy Colorectal Cancer
Schottinger, J. E., Jensen, C. D., Ghai, N. R., Chubak, J., Lee, J. K., Kamineni, A., Halm, E. A., Skinner, C. S., Udaltsova, N. V., Zhao, W. K., et al. (2022) · JAMA · doi:10.1001/jama.2022.6644 · Cited by 154
Shaukat, A., Lichtenstein, D. R., Somers, S. C., Chung, D. C., Perdue, D. G., Gopal, M., Colucci, D. R., Phillips, S. A., Marka, N. A., Church, T. R., et al. (2022) · Gastroenterology · doi:10.1053/j.gastro.2022.05.028 · Cited by 149
Spadaccini, M., Iannone, A., Maselli, R., Badalamenti, M., Desai, M., Chandrasekar, V. T., Patel, H. K., Fugazza, A., Pellegatta, G., Galtieri, P. A., et al. (2021) · The Lancet. Gastroenterology & hepatology · doi:10.1016/s2468-1253(21)00215-6 · Cited by 133
Kamba, S., Tamai, N., Saitoh, I., Matsui, H., Horiuchi, H., Kobayashi, M., Sakamoto, T., Ego, M., Fukuda, A., Tonouchi, A., et al. (2021) · Journal of Gastroenterology · doi:10.1007/s00535-021-01808-w · Cited by 124
Yao, L., Zhang, L., Liu, J., Zhou, W., He, C., Zhang, J., Wu, L., Wang, H., Xu, Y., Gong, D., et al. (2022) · Endoscopy · doi:10.1055/a-1706-6174 · Cited by 116
Liu, P., Wang, P., Brown, J. R. G., Berzin, T. M., Zhou, G., Liu, W., Xiao, X., Chen, Z., Zhang, Z., Chao Hui, Z. H. O. U., et al. (2020) · Therapeutic Advances in Gastroenterology · doi:10.1177/1756284820979165 · Cited by 98
Role of Artificial Intelligence in Colonoscopy Detection of Advanced Neoplasias
Mangas‐Sanjuán, C., de–Castro, L., Cubiella, J., Díez‐Redondo, P., Suárez, A., Pellisé, M., Fernández, N., Zarraquiños, S., Núñez, H., Álvarez‐García, V., et al. (2023) · Annals of Internal Medicine · doi:10.7326/m22-2619 · Cited by 90
Wei, M. T., Shankar, U., Parvin, R., Abbas, S. H., Chaudhary, S., Friedlander, Y., & Friedland, S. (2023) · The American Journal of Gastroenterology · doi:10.14309/ajg.0000000000002239 · Cited by 81
Rondonotti, E., Paolo, D. D., Rizzotto, E. R., Alvisi, C., Buscarini, E., Spadaccini, M., Tamanini, G., Paggi, S., Amato, A., Scardino, G., et al. (2022) · Endoscopy · doi:10.1055/a-1849-6878 · Cited by 78
Gimeno-García, A. Z., Hernández Negrin, D., Hernández, A., Nicolás-Pérez, D., Rodríguez, E., Montesdeoca, C., Alarcon, O., Romero, R., Baute Dorta, J. L., Cedrés, Y., et al. (2023) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2022.09.029 · Cited by 78
Aniwan, S., Mekritthikrai, K., Kerr, S. J., Tiankanon, K., Vandaungden, K., Sritunyarat, Y., Piyachaturawat, P., Luangsukrerk, T., Kulpatcharapong, S., Wisedopas, N., et al. (2022) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2022.09.023 · Cited by 66
Pharmacokinetics of vincristine in cancer patients treated with nifedipine
FEDELI, L., Colozza, M., Boschetti, E., Sabalich, I., Aristei, C., Guerciolini, R., Favero, A. D., Rossetti, R., Tonato, M., Rambotti, P., et al. (1989) · Cancer · doi:10.1002/1097-0142(19891101)64:9 · Cited by 55
Nakashima, H., Kitazawa, N., Fukuyama, C., Kawachi, H., Kawahira, H., Momma, K., & Sakaki, N. (2023) · Digestion · doi:10.1159/000528085 · Cited by 49
Hassan, C., Piovani, D., Spadaccini, M., Parigi, T. L., Khalaf, K., Facciorusso, A., Fugazza, A., Rösch, T., Bretthauer, M., Mori, Y., et al. (2022) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2022.10.009 · Cited by 40
Flagged first, then by citations
Cited by
Sorted by citations
Han, R., Acosta, J. N., Shakeri, Z., Ioannidis, J. P. A., Topol, E. J., & Rajpurkar, P. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00047-5 · Cited by 336
Budzyń, K., Romańczyk, M., Kitala, D., Kołodziej, P., Bugajski, M., Adami, H. O., Blom, J., Buszkiewicz, M., Halvorsen, N., Hassan, C., et al. (2025) · The Lancet Gastroenterology & Hepatology · doi:10.1016/s2468-1253(25)00133-5 · Cited by 313
Self-citationArtificial Intelligence–Assisted Colonoscopy for Polyp Detection
Soleymanjahi, S., Huebner, J., Elmansy, L., Rajashekar, N., Lüdtke, N., Paracha, R., Thompson, R., Grimshaw, A. A., Foroutan, F., Sultan, S., et al. (2024) · Annals of Internal Medicine · doi:10.7326/annals-24-00981 · Cited by 123
Patel, H. K., Mori, Y., Hassan, C., Rizkala, T., Radadiya, D. K., Nathani, P., Srinivasan, S., Misawa, M., Maselli, R., Antonelli, G., et al. (2023) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2023.11.029 · Cited by 88
Lou, S., Du, F., Song, W., Xia, Y., Yue, X., Yang, D., Cui, B., Liu, Y., & Han, P. (2023) · EClinicalMedicine · doi:10.1016/j.eclinm.2023.102341 · Cited by 83
“83 In other words, AI-aided colonoscopy is more effective in identifying people at a high risk of CRC. 31 However, owing to the limited sample size of each study, they often focused on small, non-advanced lesions, especially diminutive adenomas.”
BackgroundMethodResultInfluential
Metadata
- Authors
- Hassan, C., Spadaccini, M., Mori, Y., Foroutan, F., Facciorusso, A., Gkolfakis, P., Tziatzios, G., Triantafyllou, K., Antonelli, G., Khalaf, K., Rizkala, T., Vandvik, P. O., Fugazza, A., Rondonotti, E., Glissen-Brown, J. R., Kamba, S., Maida, M., Correale, L., Bhandari, P., Jover, R., Sharma, P., Rex, D. K., & Repici, A.
- Affiliations
- Humanitas University (IT)
- University of Oslo (NO)
- University Health Network (CA)
- University of Foggia (IT)
- Université Libre de Bruxelles (BE)
- National and Kapodistrian University of Athens (GR)
- Hepatogastroenterology Unit, Second Department of Internal Medicine-Propaedeutic, Medical School, National and Kapodistrian University of Athens, Attikon University General Hospital, Athens, Greece (G.T., K.T.)
- Sapienza University of Rome (IT)
- Lovisenberg Diakonale Sykehus (NO)
- IRCCS Humanitas Research Hospital (IT)
- Ospedale Valduce (IT)
- Center for Advanced Endoscopy, Division of Gastroenterology and Hepatology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts (J.R.G.)
- Jikei University School of Medicine (JP)
- Gastroenterology and Endoscopy Unit, S. Elia-Raimondi Hospital, Caltanissetta, Italy (M.M.)
- Queen Alexandra Hospital (GB)
- Hospital General Universitario de Alicante Doctor Balmis (ES)
- Kansas City VA Medical Center (US)
- Indiana University Hospital (US)
- Corresponding author
- Hassan, Cesare
- Spadaccini, Marco
- ORCID
- Hassan, Cesare
- Spadaccini, Marco
- Mori, Yuichi
- Foroutan, Farid
- Facciorusso, Antonio
- Gkolfakis, Paraskevas
- Tziatzios, Georgios
- Antonelli, Giulio
- Khalaf, Kareem
- Rizkala, Tommy
- Vandvik, Per Olav
- Fugazza, Alessandro
- Rondonotti, Emanuele
- Glissen-Brown, Jeremy R.
- Kamba, Shunsuke
- Maida, Marcello
- Correale, Loredana
- Bhandari, Pradeep
- Jover, Rodrigo
- Sharma, Prateek
- Rex, Douglas K.
- Repici, Alessandro
- Venue
- Annals of Internal Medicine
- Publisher
- American College of Physicians
- Published
- 2023-09
- Volume
- 176
- Issue
- 9
- Pages
- 1209-1220
- ISSN
- 0003-4819 (ISSN-L)
- 1539-3704
- OpenAlex
- W4386213466
- Cited by
- 216
- References
- 37
- Citations by year
- 2026: 62 · 2025: 88 · 2024: 58 · 2023: 8
- FWCI
- 6.99
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Annals of Internal Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Japan Society for the Promotion of Science (23K24615)
- 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
- computer-aided detection, colorectal neoplasia, colonoscopy, adenoma detection rate, advanced adenoma, serrated lesions, polypectomy, overdiagnosis, artificial intelligence, colorectal cancer screening, withdrawal time, inspection time, adenoma miss rate, colorectal cancer
- MeSH terms
- Artificial Intelligence, Colonoscopy, Computers, Humans, Colorectal Neoplasms, Databases, Factual
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
