Impact of the clinical use of artificial intelligence–assisted neoplasia detection for colonoscopy: a large-scale prospective, propensity score–matched study (with video)

Published:August 02, 2021DOI:https://doi.org/10.1016/j.gie.2021.07.022

      Background and Aims

      Recently, the use of computer-aided detection (CADe) for colonoscopy has been investigated to improve the adenoma detection rate (ADR). We aimed to assess the efficacy of a regulatory-approved CADe in a large-scale study with high numbers of patients and endoscopists.

      Methods

      This was a propensity score–matched prospective study that took place at a university hospital between July 2020 and December 2020. We recruited patients aged ≥20 years who were scheduled for colonoscopy. Patients with polyposis, inflammatory bowel disease, or incomplete colonoscopy were excluded. We used a regulatory-approved CADe system and conducted a propensity score matching–based comparison of the ADR between patients examined with and without CADe as the primary outcome.

      Results

      During the study period, 2261 patients underwent colonoscopy with the CADe system or routine colonoscopy, and 172 patients were excluded in accordance with the exclusion criteria. Thirty endoscopists (9 nonexperts and 21 experts) were involved in this study. Propensity score matching was conducted using 5 factors, resulting in 1836 patients included in the analysis (918 patients in each group). The ADR was significantly higher in the CADe group than in the control group (26.4% vs 19.9%, respectively; relative risk, 1.32; 95% confidence interval, 1.12-1.57); however, there was no significant increase in the advanced neoplasia detection rate (3.7% vs 2.9%, respectively).

      Conclusions

      The use of the CADe system for colonoscopy significantly increased the ADR in a large-scale prospective study including 30 endoscopists (Clinical trial registration number: UMIN000040677.)

      Abbreviations:

      ADR (adenoma detection rate), AI (artificial intelligence), APC (adenoma per colonoscopy), CADe (computer-aided detection), PDR (polyp detection rate)
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      References

        • Zauber A.G.
        • Winawer S.J.
        • O'Brien M.J.
        • et al.
        Colonoscopic polypectomy and long-term prevention of colorectal-cancer deaths.
        N Engl J Med. 2012; 366: 687-696
        • Rex D.K.
        • Boland C.R.
        • Dominitz J.A.
        • 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
        • Kaminski M.F.
        • Regula J.
        • Kraszewska E.
        • et al.
        Quality indicators for colonoscopy and the risk of interval cancer.
        N Engl J Med. 2010; 362: 1795-1803
        • Corley D.A.
        • Jensen C.D.
        • Marks A.R.
        • et al.
        Adenoma detection rate and risk of colorectal cancer and death.
        N Engl J Med. 2014; 370: 1298-1306
        • van Rijn J.C.
        • Reitsma J.B.
        • Stoker J.
        • et al.
        Polyp miss rate determined by tandem colonoscopy: a systematic review.
        Am J Gastroenterol. 2006; 101: 343-350
        • Zhao S.
        • Wang S.
        • Pan P.
        • et al.
        Magnitude, risk factors, and factors associated with adenoma miss rate of tandem colonoscopy: a systematic review and meta-analysis.
        Gastroenterology. 2019; 156: 1661-1674
        • le Clercq C.M.
        • Bouwens M.W.
        • Rondagh E.J.
        • et al.
        Postcolonoscopy colorectal cancers are preventable: a population-based study.
        Gut. 2014; 63: 957-963
        • Misawa M.
        • Kudo S.E.
        • Mori Y.
        • et al.
        Development of a computer-aided detection system for colonoscopy and a publicly accessible large colonoscopy video database (with video).
        Gastrointest Endosc. 2021; 93: 960-967
        • Wang P.
        • Xiao X.
        • Glissen Brown J.R.
        • et al.
        Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy.
        Nat Biomed Eng. 2018; 2: 741-748
        • 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
        • Yamada M.
        • Saito Y.
        • Imaoka H.
        • et al.
        Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy.
        Sci Rep. 2019; 9: 14465
        • Wang P.
        • Berzin T.M.
        • Glissen Brown J.R.
        • et al.
        Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study.
        Gut. 2019; 68: 1813-1819
        • 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
        • Repici A.
        • Badalamenti M.
        • Maselli R.
        • et al.
        Efficacy of real-time computer-aided detection of colorectal neoplasia in a randomized trial.
        Gastroenterology. 2020; 159: 512-520
        • Liu W.N.
        • Zhang Y.Y.
        • Bian X.Q.
        • et al.
        Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy.
        Saudi J Gastroenterol. 2020; 26: 13-19
        • Wang P.
        • Liu X.
        • Berzin T.M.
        • 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
        • Barua I.
        • Vinsard D.G.
        • Jodal H.C.
        • et al.
        Artificial intelligence for polyp detection during colonoscopy: a systematic review and meta-analysis.
        Endoscopy. 2021; 53: 277-284
        • Misawa M.
        • Kudo S.E.
        • Mori Y.
        • et al.
        Artificial intelligence-assisted polyp detection for colonoscopy: initial experience.
        Gastroenterology. 2018; 154: 2027-2029
        • Tanaka S.
        • Saitoh Y.
        • Matsuda T.
        • et al.
        Evidence-based clinical practice guidelines for management of colorectal polyps.
        J Gastroenterol. 2015; 50: 252-260
      1. The Paris endoscopic classification of superficial neoplastic lesions: esophagus, stomach, and colon: November 30 to December 1, 2002.
        Gastrointest Endosc. 2003; 58: S3-43
        • Aronchick C.A.
        • Lipshutz W.H.
        • Wright S.H.
        • et al.
        A novel tableted purgative for colonoscopic preparation: efficacy and safety comparisons with Colyte and Fleet Phospho-Soda.
        Gastrointest Endosc. 2000; 52: 346-352
      2. Redmon J, Farhadi A. Yolov3: an incremental improvement. Available at: https://arxiv.org/abs/1804.02767. Accessed August 31, 2021.

        • Gupta S.
        • Lieberman D.
        • Anderson J.C.
        • et al.
        Recommendations for follow-up after colonoscopy and polypectomy: a consensus update by the US Multi-Society Task Force on Colorectal Cancer.
        Gastrointest Endosc. 2020; 91: 463-485
        • Hassan C.
        • Antonelli G.
        • Dumonceau J.M.
        • et al.
        Post-polypectomy colonoscopy surveillance: European Society of Gastrointestinal Endoscopy (ESGE) guideline—update 2020.
        Endoscopy. 2020; 52: 687-700
        • Saito Y.
        • Oka S.
        • Kawamura T.
        • et al.
        Colonoscopy screening and surveillance guidelines.
        Dig Endosc. 2021; 33: 486-519
        • Austin P.C.
        Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies.
        Pharm Stat. 2011; 10: 150-161
        • Kanda Y.
        Investigation of the freely available easy-to-use software 'EZR' for medical statistics.
        Bone Marrow Transplant. 2013; 48: 452-458
        • Repici A.
        • Spadaccini M.
        • Antonelli G.
        • et al.
        Artificial intelligence and colonoscopy experience: lessons from two randomised trials.
        Gut. 2021; (Epub 2021 Jun 29)
        • Lieberman D.A.
        • Weiss D.G.
        • Harford W.V.
        • et al.
        Five-year colon surveillance after screening colonoscopy.
        Gastroenterology. 2007; 133: 1077-1085
        • Itoh H.
        • Oda M.
        • Mori Y.
        • et al.
        • Single-shot three-dimensional reconstruction for colonoscopic image analysis
        Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling.
        International Society for Optics and Photonics, 2021: 115980E
        • McGill S.K.
        • Rosenman J.
        • Wang R.
        • et al.
        Artificial intelligence identifies and quantifies colonoscopy blind spots.
        Endoscopy. 2021; (Epub 2021 Feb 4)
        • Freedman D.
        • Blau Y.
        • Katzir L.
        • et al.
        Detecting deficient coverage in colonoscopies.
        IEEE Trans Med Imaging. 2020; 39: 3451-3462
        • Wang P.
        • Liu P.
        • Glissen Brown J.R.
        • et al.
        Lower adenoma miss rate of computer-aided detection-assisted colonoscopy vs routine white-light colonoscopy in a prospective tandem study.
        Gastroenterology. 2020; 159: 1252-1261
        • Patel H.K.
        • Chandrasekar V.T.
        • Srinivasan S.
        • et al.
        Second-generation distal attachment cuff improves adenoma detection rate: meta-analysis of randomized controlled trials.
        Gastrointest Endosc. 2021; 93: 544-553
        • Kudo T.
        • Saito Y.
        • Ikematsu H.
        • et al.
        New-generation full-spectrum endoscopy versus standard forward-viewing colonoscopy: a multicenter, randomized, tandem colonoscopy trial (J-FUSE Study).
        Gastrointest Endosc. 2018; 88: 854-864
        • Hassan C.
        • Spadaccini M.
        • Iannone A.
        • et al.
        Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis.
        Gastrointest Endosc. 2021; 93: 77-85.e6