Advertisement

Accuracy of artificial intelligence on histology prediction and detection of colorectal polyps: a systematic review and meta-analysis

Published:February 28, 2020DOI:https://doi.org/10.1016/j.gie.2020.02.033

      Background and Aims

      We performed a meta-analysis of all published studies to determine the diagnostic accuracy of artificial intelligence (AI) on histology prediction and detection of colorectal polyps.

      Method

      We searched Embase, PubMed, Medline, Web of Science, and Cochrane library databases to identify studies using AI for colorectal polyp histology prediction and detection. The quality of included studies was measured by the Quality Assessment of Diagnostic Accuracy Studies tool. We used a bivariate meta-analysis following a random-effects model to summarize the data and plotted hierarchical summary receiver operating characteristic curves. The area under the hierarchical summary receiver operating characteristic curve (AUC) served as an indicator of the diagnostic accuracy and during head-to-head comparisons.

      Results

      A total of 7680 images of colorectal polyps from 18 studies were included in the analysis of histology prediction. The accuracy of the AI (AUC) was .96 (95% confidence interval [CI], .95-.98), with a corresponding pooled sensitivity of 92.3% (95% CI, 88.8%-94.9%) and specificity of 89.8% (95% CI, 85.3%-93.0%). The AUC of AI using narrow-band imaging (NBI) was significantly higher than the AUC using non-NBI (.98 vs .84, P < .01). The performance of AI was superior to nonexpert endoscopists (.97 vs .90, P < .01). For characterization of diminutive polyps using a deep learning model with nonmagnifying NBI, the pooled negative predictive value was 95.1% (95% CI, 87.7%-98.1%). For polyp detection, the pooled AUC was .90 (95% CI, .67-1.00) with a sensitivity of 95.0% (95% CI, 91.0%-97.0%) and a specificity of 88.0% (95% CI, 58.0%-99.0%).

      Conclusions

      AI was accurate in histology prediction and detection of colorectal polyps, including diminutive polyps. The performance of AI was better under NBI and was superior to nonexpert endoscopists. Despite the difference in AI models and study designs, AI performances are rather consistent, which could serve as a reference for future AI studies.

      Abbreviations:

      AI (artificial intelligence), AUC (area under the hierarchical summary receiver operating characteristic curve), NBI (narrow band imaging), WLI (white-light imaging)
      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Gastrointestinal Endoscopy
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • McGill S.K.
        • Evangelou E.
        • Ioannidis J.P.
        • et al.
        Narrow band imaging to differentiate neoplastic and non-neoplastic colorectal polyps in real time: a meta-analysis of diagnostic operating characteristics.
        Gut. 2013; 62: 1704-1713
        • Hirata I.
        • Nakagawa Y.
        • Ohkubo M.
        • et al.
        Usefulness of magnifying narrow-band imaging endoscopy for the diagnosis of gastric and colorectal lesions.
        Digestion. 2012; 85: 74-79
        • Takeuchi Y.
        • Hanafusa M.
        • Kanzaki H.
        • et al.
        Proposal of a new “resect and discard” strategy using magnifying narrow band imaging: pilot study of diagnostic accuracy.
        Dig Endosc. 2014; 26: 90-97
        • Hewett D.G.
        • Kaltenbach T.
        • Sano Y.
        • et al.
        Validation of a simple classification system for endoscopic diagnosis of small colorectal polyps using narrow-band imaging.
        Gastroenterology. 2012; 143: 599-607
        • Rex D.K.
        • Kahi C.
        • O’Brien M.
        • et al.
        The American Society for Gastrointestinal Endoscopy PIVI (Preservation and Incorporation of Valuable Endoscopic Innovations) on real-time endoscopic assessment of the histology of diminutive colorectal polyps.
        Gastrointest Endosc. 2011; 73: 419-422
        • Abu Dayyeh B.K.
        • Thosani N.
        • Konda V.
        • et al.
        ASGE technology committee systematic review and meta-analysis assessing the ASGE PIVI thresholds for adopting real-time endoscopic assessment of the histology of diminutive colorectal polyps.
        Gastrointest Endosc. 2015; 81: 502.e1-502.e16
        • Hassan C.
        • Pickhardt P.J.
        • Rex D.K.
        A resect and discard strategy would improve cost-effectiveness of colorectal cancer screening.
        Clin Gastroenterol Hepatol. 2010; 8: 865-869
        • Gupta N.
        • Brill J.V.
        • Canto M.
        • et al.
        AGA white paper: training and implementation of endoscopic image enhancement technologies.
        Clin Gastroenterol Hepatol. 2017; 15: 820-826
        • East J.E.
        • Vleugels J.L.
        • Roelandt P.
        • et al.
        Advanced endoscopic imaging: European Society of Gastrointestinal Endoscopy (ESGE) technology review.
        Endoscopy. 2016; 48: 1029-1045
        • Tischendorf J.
        • Gross S.
        • Winograd R.
        • et al.
        Computer-aided classification of colorectal polyps based on vascular patterns: a pilot study.
        Endoscopy. 2010; 42: 203-207
        • Gross S.
        • Trautwein C.
        • Behrens A.
        • et al.
        Computer-based classification of small colorectal polyps by using narrow-band imaging with optical magnification.
        Gastrointest Endosc. 2011; 74: 1354-1359
        • Takemura Y.
        • Yoshida S.
        • Tanaka S.
        • et al.
        Computer-aided system for predicting the histology of colorectal tumors by using narrow-band imaging magnifying colonoscopy (with video).
        Gastrointest Endosc. 2012; 75: 179-185
        • André B.
        • Vercauteren T.
        • Buchner A.M.
        • et al.
        Software for automated classification of probe-based confocal laser endomicroscopy videos of colorectal polyps.
        World J Gastroenterol. 2012; 18: 5560-5569
        • Rath T.
        • Tontini G.E.
        • Vieth M.
        • et al.
        In vivo real-time assessment of colorectal polyp histology using an optical biopsy forceps system based on laser-induced fluorescence spectroscopy.
        Endoscopy. 2016; 48: 557-562
        • Mori Y.
        • Kudo S.
        • Wakamura K.
        • et al.
        Novel computer-aided diagnostic system for colorectal lesions by using endocytoscopy (with videos).
        Gastrointest Endosc. 2015; 81: 621-629
        • Mori Y.
        • Kudo S.
        • Chiu P.
        • et al.
        Impact of an automated system for endocytoscopic diagnosis of small colorectal lesions: an international web-based study.
        Endoscopy. 2016; 48: 1110-1118
        • Kominami Y.
        • Yoshida S.
        • Tanaka S.
        • et al.
        Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy.
        Gastrointest Endosc. 2016; 83: 643-649
        • Byrne M.F.
        • 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
        • Chen P.J.
        • Lin M.C.
        • Lai M.J.
        • et al.
        Accurate classification of diminutive colorectal polyps using computer-aided analysis.
        Gastroenterology. 2018; 154: 568-575
        • Mori Y.
        • Kudo S.E.
        • Misawa M.
        • et al.
        Real-time use of artificial intelligence in identification of diminutive polyps during colonoscopy a prospective study.
        Ann Intern Med. 2018; 169: 357-366
        • Zachariah R.
        • Ninh A.
        • Dao T.
        • et al.
        Video validation of novel multiclass convolution neural network for real time optical pathology of adenomas, sessile serrated polyps and hyperplastic polyps [abstract].
        Gastrointest Endosc. 2019; 89 (AB655)
        • Zhu X.
        • Nemoto D.
        • Wang Y.
        • et al.
        Detection and diagnosis of sessile serrated adenoma/polyps using convolutional neural network (artificial intelligence).
        Gastrointest Endosc. 2018; 87 ([abstract]): AB251
        • Min M.
        • Su S.
        • He W.
        • et al.
        Computer-aided diagnosis of colorectal polyps using linked color imaging colonoscopy to predict histology.
        Sci Rep. 2019; 9: 2881
        • Sánchez-Montes C.
        • Sánchez F.J.
        • Bernal J.
        • et al.
        Computer-aided prediction of polyp histology on white light colonoscopy using surface pattern analysis.
        Endoscopy. 2019; 51: 261-265
        • Lui T.K.L.
        • To W.P.
        • Ko K.K.
        • et al.
        Superiority of the artificial intelligence image classifier for histological prediction of diminutive colorectal polyps based on non-magnifying endoscopic images.
        Hong Kong Med J. 2019; 25: 31
        • Horiuchi H.
        • Tamai N.
        • Kamba S.
        • et al.
        Real-time computer-aided diagnosis of diminutive rectosigmoid polyps using an auto-fluorescence imaging system and novel color intensity analysis software.
        Scand J Gastroenterol. 2019; 54: 800-805
        • Kudo S.
        • Misawa M.
        • Mori Y.
        • et al.
        Artificial intelligence-assisted system improves endoscopic identification of colorectal neoplasms.
        Clin Gastroenterol Hepatol. 2019; 13 (S1542-3565(19))
        • 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
        • Figueiredo P.
        • Figueiredo I.
        • Pinto L.
        • et al.
        Polyp detection with computer-aided diagnosis in white light colonoscopy: comparison of three different methods.
        Endosc Int Open. 2019; 7: E209-E215
        • 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
        • Misawa M.
        • Kudo S.
        • Mori Y.
        • et al.
        Artificial intelligence- assisted polyp detection system for colonoscopy, based on the largest available collection of clinical video data for machine learning [abstract].
        Gastrointest Endosc. 2019; 89: AB646
        • Matsui H.
        • Kamba S.
        • Koizumi A.
        • et al.
        The detection rate of colorectal polyps with an artificial intelligence algorithm in the dynamic analysis using video clips [abstract].
        Gastrointest Endosc. 2019; 89: AB75-A76
        • Ka-Luen Lui T.
        • Yee K.
        • Wong K.
        • et al.
        Use of artificial intelligence image classifier for real-time detection of colonic polyps.
        Gastrointest Endosc. 2019; 89 ([abstract]): AB135
        • Liberati A.
        • Altman D.G.
        • Tetzlaff J.
        • et al.
        The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration.
        J Clin Epidemiol. 2009; 62: e1-34
        • Stroup D.F.
        • Berlin J.A.
        • Morton S.C.
        • et al.
        Meta-analysis of observational studies in epidemiology: a proposal for reporting.
        J Am Med Assoc. 2000; 283: 2008-2012
        • Whiting P.F.
        • Rutjes A.W.S.
        • Westwood M.E.
        • et al.
        Quadas-2: a revised tool for the quality assessment of diagnostic accuracy studies.
        Ann Intern Med. 2011; 155: 529-536
        • Devillé W.L.
        • Buntinx F.
        • Bouter L.M.
        • et al.
        Conducting systematic reviews of diagnostic studies: didactic guidelines.
        BMC Med Res Methodol. 2002; 2: 1-13
        • Mori Y.
        • Kudo S.
        • Berzin T.
        • et al.
        Computer-aided diagnosis for colonoscopy.
        Endoscopy. 2017; 49: 813-819
        • 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
        • Paggi S.
        • Rondonotti E.
        • Amato A.
        • et al.
        Resect and discard strategy in clinical practice: a prospective cohort study.
        Endoscopy. 2012; 44: 889-904
        • Lui T.
        • Wong K.
        • Mak L.
        • et al.
        Endoscopic prediction of deeply submucosal invasive carcinoma with use of artificial intelligence.
        Endosc Int Open. 2019; 07: E514-E520
        • Ito N.
        • Kawahira H.
        • Nakashima H.
        • et al.
        Endoscopic diagnostic support system for cT1b colorectal cancer using deep learning.
        Oncology. 2018; 96: 44-50
        • Zhou J.
        • Wu L.
        • Wan X.
        • et al.
        A novel artificial intelligence system for the assessment of bowel preparation (with video).
        Gastrointest Endosc. 2020; 91: 428-435
        • Rombaoa C.
        • Kalra A.
        • Dao T.
        • et al.
        Automated insertion time, cecal intubation and withdrawal time during live colonoscopy using convolutional neural networks—a video validation study [abstract].
        Gastrointest Endosc. 2019; 89: AB619
        • So N.Y.H.
        • WK Leung
        • TKL Liu
        Artificial intelligence-based identification of caecum by static colonoscopy images [abstract].
        Hong Kong Med J. 2019; 25: 38
        • Bisschops R.
        • East J.E.
        • Hassan C.
        • et al.
        Advanced imaging for detection and differentiation of colorectal neoplasia: European Society of Gastrointestinal Endoscopy (ESGE) guideline—update 2019.
        Endoscopy. 2019; 51: 1155-1179