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CC BY-NC-ND 4.0 · Endosc Int Open 2021; 09(04): E513-E521
DOI: 10.1055/a-1341-0457
Review

Impact of real-time use of artificial intelligence in improving adenoma detection during colonoscopy: A systematic review and meta-analysis

Munish Ashat
1   Department of Gastroenterology and Hepatology, University of Iowa Hospitals and Clinics, Iowa City, Iowa, United States
,
Jagpal Singh Klair
2   Digestive Disease Institute, Virginia Mason Medical Center, Seattle, Washington, United States
,
Dhruv Singh
3   Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, New York, United States
,
Arvind Rangarajan Murali
1   Department of Gastroenterology and Hepatology, University of Iowa Hospitals and Clinics, Iowa City, Iowa, United States
,
Rajesh Krishnamoorthi
2   Digestive Disease Institute, Virginia Mason Medical Center, Seattle, Washington, United States
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Abstract

Background and study aims With the advent of deep neural networks (DNN) learning, the field of artificial intelligence (AI) is rapidly evolving. Recent randomized controlled trials (RCT) have investigated the influence of integrating AI in colonoscopy and its impact on adenoma detection rates (ADRs) and polyp detection rates (PDRs). We performed a systematic review and meta-analysis to reliably assess if the impact is statistically significant enough to warrant the adoption of AI -assisted colonoscopy (AIAC) in clinical practice.

Methods We conducted a comprehensive search of multiple electronic databases and conference proceedings to identify RCTs that compared outcomes between AIAC and conventional colonoscopy (CC). The primary outcome was ADR. The secondary outcomes were PDR and total withdrawal time (WT).

Results Six RCTs (comparing AIAC vs CC) with 5058 individuals undergoing average-risk screening colonoscopy were included in the meta-analysis. ADR was significantly higher with AIAC compared to CC (33.7 % versus 22.9 %; odds ratio (OR) 1.76, 95 % confidence interval (CI) 1.55–2.00; I2 = 28 %). Similarly, PDR was significantly higher with AIAC (45.6 % versus 30.6 %; OR 1.90, 95 %CI, 1.68–2.15, I2 = 0 %). The overall WT was higher for AIAC compared to CC (mean difference [MD] 0.46 (0.00–0.92) minutes, I2 = 94 %).

Conclusions There is an increase in adenoma and polyp detection with the utilization of AIAC.

Supplementary material



Publication History

Received: 24 June 2020

Accepted: 23 November 2020

Article published online:
17 March 2021

© 2021. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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Comment to this article:

Endoscopy International Open – recently publishedEndoscopy 2021; 53(05): 564-564
DOI: 10.1055/a-1373-8840

 

  • References

  • 1 Siegel RL, Miller KD, Goding Sauer A. et al. Colorectal cancer statistics. CA: A Can J Clin 2020; DOI: 10.3322/caac.21601.
    CrossrefPubMedGoogle Scholar
  • 2 Bibbins-Domingo K, Grossman DC, Curry SJ. et al. Screening for Colorectal Cancer: US Preventive Services Task Force Recommendation Statement. JAMA 2016; 315: 2564-2575
    CrossrefPubMedGoogle Scholar
  • 3 Rex DK, Boland CR, Dominitz JA. et al. Colorectal Cancer Screening: Recommendations for Physicians and Patients from the U.S. Multi-Society Task Force on Colorectal Cancer. Am J Gastroenterol 2017; 112: 1016-1030
    CrossrefPubMedGoogle Scholar
  • 4 Doubeni CA, Corley DA, Quinn VP. et al. Effectiveness of screening colonoscopy in reducing the risk of death from right and left colon cancer: a large community-based study. Gut 2018; 67: 291-298
    CrossrefPubMedGoogle Scholar
  • 5 Barret M, Chaussade S, Coriat R. Adenoma detection rate and risk of colorectal cancer and death. N Engl J Med 2014; 370: 2540-2541
    PubMedGoogle Scholar
  • 6 Rex DK, Schoenfeld PS, Cohen J. et al. Quality indicators for colonoscopy. Gastrointest Endosc 2015; 81: 31-53
    CrossrefPubMedGoogle Scholar
  • 7 Ahn SB, Han DS, Bae JH. et al. The miss rate for colorectal adenoma determined by quality-adjusted, back-to-back colonoscopies. Gut Liver 2012; 6: 64-70
    CrossrefPubMedGoogle Scholar
  • 8 Aslanian HR, Shieh FK, Chan FW. et al. Nurse observation during colonoscopy increases polyp detection: a randomized prospective study. Am J Gastroenterol 2013; 108: 166-172
    CrossrefPubMedGoogle Scholar
  • 9 Gong D, Wu L, Zhang J. et al. Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study. Lancet Gastroenterol Hepatol 2020; 5: 352-361
    CrossrefPubMedGoogle Scholar
  • 10 Liu WN, Zhang YY, Bian XQ. et al. Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy. Saudi J Gastroenterol 2020; 26: 13-19
    CrossrefPubMedGoogle Scholar
  • 11 Su JR, Li Z, Shao XJ. et al. Impact of a real-time automatic quality control system on colorectal polyp and adenoma detection: a prospective randomized controlled study (with videos). Gastrointest Endosc 2020; 91: 415-424.e414
    CrossrefPubMedGoogle Scholar
  • 12 Wang P, Berzin TM, Glissen Brown JR. et al. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut 2019; 68: 1813-1819
    CrossrefPubMedGoogle Scholar
  • 13 Wang P, Liu X, Berzin TM. et al. Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study. Lancet Gastroenterol Hepatol 2020; 5: 343-351
    CrossrefPubMedGoogle Scholar
  • 14 Kriegeskorte N, Golan T. Neural network models and deep learning. Curr Biology 2019; 29: R231-R236
    CrossrefPubMedGoogle Scholar
  • 15 Mori Y, Kudo SE, Chiu PW. et al. Impact of an automated system for endocytoscopic diagnosis of small colorectal lesions: an international web-based study. Endoscopy 2016; 48: 1110-1118
    Article in Thieme ConnectPubMedGoogle Scholar
  • 16 Urban G, Tripathi P, Alkayali T. et al. Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy. Gastroenterology 2018; 155: 1069-1078.e1068
    CrossrefPubMedGoogle Scholar
  • 17 Mori Y, Kudo SE, Wakamura K. et al. Novel computer-aided diagnostic system for colorectal lesions by using endocytoscopy (with videos). Gastrointest Endosc 2015; 81: 621-629
    CrossrefPubMedGoogle Scholar
  • 18 Byrne MF, Chapados N, Soudan F. et al. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut 2019; 68: 94-100
    CrossrefPubMedGoogle Scholar
  • 19 Schünemann H, Brozek J, Guyatt G, Oxman A. Handbook for grading the quality of evidence and the strength of recommendations using the GRADE approach. Available at: https://gdt.gradepro.org/app/handbook/handbook.html
    PubMedGoogle Scholar
  • 20 Higgins JPT, Altman DG, Gøtzsche PC. et al. The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials. BMJ 2011; 343: d5928
    CrossrefPubMedGoogle Scholar
  • 21 Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol 2010; 25: 603-605
    CrossrefPubMedGoogle Scholar
  • 22 Easterbrook PJ, Berlin JA, Gopalan R. et al. Publication bias in clinical research. Lancet (London, England) 1991; 337: 867-872
    CrossrefPubMedGoogle Scholar
  • 23 Klare P, Sander C, Prinzen M. et al. Automated polyp detection in the colorectum: a prospective study (with videos). Gastrointest Endosc 2019; 89: 576-582.e571
    CrossrefPubMedGoogle Scholar
  • 24 Repici A, Badalamenti M, Maselli R. et al. Efficacy of real-time computer-aided detection of colorectal neoplasia in a randomized trial. Gastroenterology 2020; DOI: 10.1053/j.gastro.2020.04.062.
    CrossrefPubMedGoogle Scholar
  • 25 Zauber AG, Winawer SJ, O'Brien MJ. et al. Colonoscopic polypectomy and long-term prevention of colorectal-cancer deaths. N Engl J Med 2012; 366: 687-696
    CrossrefPubMedGoogle Scholar
  • 26 Pohl H, Robertson DJ. Colorectal cancers detected after colonoscopy frequently result from missed lesions. Clin Gastroenterol Hepatol 2010; 8: 858-864
    CrossrefPubMedGoogle Scholar
  • 27 Lanspa SJ, Lynch HT. Quality indicators for colonoscopy and the risk of interval cancer. N Engl J Med 2010; 363: 1371 , author reply 1373
    CrossrefPubMedGoogle Scholar
  • 28 Corley DA, Jensen CD, Marks AR. et al. Adenoma detection rate and risk of colorectal cancer and death. N Engl J Med 2014; 370: 1298-1306
    CrossrefPubMedGoogle Scholar
  • 29 Aziz M, Sharma S, Fatima R. et al. How to increase proximal adenoma detection rate: a meta-analysis comparing water exchange, water immersion and air/CO2 insufflation methods for colonoscopy. Ann Gastroenterol 2020; 33: 178-186
    PubMedGoogle Scholar
  • 30 Lawrence Z, Gross SA. The use of attachment devices to aid in adenoma detection. Curr Treatment Options Gastroenterol 2020; DOI: 10.1007/s11938-020-00280-4.
    CrossrefPubMedGoogle Scholar
  • 31 Atkinson NSS, Ket S, Bassett P. et al. Narrow-band imaging for detection of neoplasia at colonoscopy: a meta-analysis of data from individual patients in randomized controlled trials. Gastroenterol 2019; 157: 462-471
    CrossrefPubMedGoogle Scholar
  • 32 Aziz M, Desai M, Hassan S. et al. Improving serrated adenoma detection rate in the colon by electronic chromoendoscopy and distal attachment: systematic review and meta-analysis. Gastrointest Endosc 2019; 90: 721-731.e721
    CrossrefPubMedGoogle Scholar
  • 33 Brenner H, Hoffmeister M, Arndt V. et al. Protection from right- and left-sided colorectal neoplasms after colonoscopy: population-based study. J Natl Cancer Inst 2010; 102: 89-95
    CrossrefPubMedGoogle Scholar
  • 34 Nakagawa-Senda H, Hori M, Matsuda T. et al. Prognostic impact of tumor location in colon cancer: the Monitoring of Cancer Incidence in Japan (MCIJ) project. BMC Cancer 2019; 19: 431
    CrossrefPubMedGoogle Scholar
  • 35 Brenner H, Hoffmeister M, Stegmaier C. et al. Risk of progression of advanced adenomas to colorectal cancer by age and sex: estimates based on 840,149 screening colonoscopies. Gut 2007; 56: 1585-1589
    CrossrefPubMedGoogle Scholar