New insights on missed colonic lesions during colonoscopy through artificial intelligence–assisted real-time detection (with video)

      Background and Aims

      Meta-analysis shows that up to 26% of adenomas could be missed during colonoscopy. We investigated whether the use of artificial intelligence (AI)-assisted real-time detection could provide new insights into mechanisms underlying missed lesions during colonoscopy.


      A validated real-time deep-learning AI model for the detection of colonic polyps was first tested in videos of tandem colonoscopy of the proximal colon for missed lesions. The real-time AI model was then prospectively validated in a total colonoscopy in which the endoscopist was blinded to real-time AI findings. Segmental unblinding of the AI findings were provided, and the colonic segment was then re-examined when missed lesions were detected by AI but not the endoscopist. All polyps were removed for histologic examination as the criterion standard.


      Sixty-five videos of tandem examination of the proximal colon were reviewed by AI. AI detected 79.1% (19/24) of missed proximal adenomas in the video of the first-pass examination. In 52 prospective colonoscopies, real-time AI detection detected at least 1 missed adenoma in 14 patients (26.9%) and increased the total number of adenomas detected by 23.6%. Multivariable analysis showed that a missed adenoma(s) was more likely when there were multiple polyps (adjusted odds ratio, 1.05; 95% confidence interval, 1.02-1.09; P < .0001) or colonoscopy was performed by less-experienced endoscopists (adjusted odds ratio, 1.30; 95% confidence interval, 1.05-1.62; P = .02).


      Our findings provide new insights on the prominent role of human factors, including inexperience and distraction, on missed colonic lesions. With the use of real-time AI assistance, up to 80% of missed adenomas could be prevented. (Clinical trial registration number: NCT04227795.)


      AI (artificial intelligence), R-FCN (region-based fully connected convolutional neural network)
      To read this article in full you will need to make a payment


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


        • Singh S.
        • Singh P.P.
        • Murad M.H.
        • et al.
        Prevalence, risk factors, and outcomes of interval colorectal cancers: a systematic review and meta-analysis.
        Am J Gastroenterol. 2014; 109: 1375-1389
        • Robertson D.J.
        • Lieberman D.A.
        • Winawer S.J.
        • et al.
        Colorectal cancers soon after colonoscopy: a pooled multicohort analysis.
        Gut. 2014; 63: 949-956
        • 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
        • Lee J.
        • Park S.W.
        • Kim Y.S.
        • et al.
        Risk factors of missed colorectal lesions after colonoscopy.
        Medicine (Baltimore). 2017; 96e7468
        • Leufkens A.M.
        • van Oijen M.G.
        • Vleggaar F.P.
        • et al.
        Factors influencing the miss rate of polyps in a back-to-back colonoscopy study.
        Endoscopy. 2012; 44: 470-475
        • Heresbach D.
        • Barrioz T.
        • Lapalus M.G.
        • et al.
        Miss rate for colorectal neoplastic polyps: a prospective multicenter study of back-to-back video colonoscopies.
        Endoscopy. 2008; 40: 284-290
        • Hong S.N.
        • Sung I.K.
        • Kim J.H.
        • et al.
        The effect of the bowel preparation status on the risk of missing polyp and adenoma during screening colonoscopy: a tandem colonoscopic study.
        Clin Endosc. 2012; 45: 404-411
        • Hewett D.G.
        • Rex D.K.
        Cap-fitted colonoscopy: a randomized, tandem colonoscopy study of adenoma miss rates.
        Gastrointest Endosc. 2010; 72: 775-781
        • Gkolfakis P.
        • Tziatzios G.
        • Facciorusso A.
        • et al.
        Meta-analysis indicates that add-on devices and new endoscopes reduce colonoscopy adenoma miss rate.
        Eur J Gastroenterol Hepatol. 2018; 30: 1482-1490
        • Ikematsu H.
        • Saito Y.
        • Tanaka S.
        • et al.
        The impact of narrow band imaging for colon polyp detection: a multicenter randomized controlled trial by tandem colonoscopy.
        J Gastroenterol. 2012; 47: 1099-1107
        • Pioche M.
        • Denis A.
        • Allescher H.D.
        • et al.
        Impact of 2 generational improvements in colonoscopes on adenoma miss rates: results of a prospective randomized multicenter tandem study.
        Gastrointest Endosc. 2018; 88: 107-116
        • Harrison M.
        • Singh N.
        • Rex D.K.
        Impact of proximal colon retroflexion on adenoma miss rates.
        Am J Gastroenterol. 2004; 99: 519-522
        • Chandran S.
        • Parker F.
        • Vaughan R.
        • et al.
        Right-sided adenoma detection with retroflexion versus forward-view colonoscopy.
        Gastrointest Endosc. 2015; 81: 608-613
        • Triantafyllou K.
        • Tziatzios G.
        • Sioulas A.D.
        • et al.
        Diagnostic yield of scope retroflexion in the right colon: a prospective cohort study.
        Dig Liver Dis. 2016; 48: 176-181
        • 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
        • Singh B.L.H.
        • Sharma A.
        • Davis L.S.
        R-FCN-3000 at 30fps: decoupling detection and classification.
        (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition)2018: 1081-1090
        • He K.
        • Zhang K.
        • Ren S.
        • et al.
        Deep residual learning for image recognition.
        (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition)2016: 770-778
        • Silva J.
        • Histace A.
        • Romain O.
        • et al.
        Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer.
        Int J Comput Assist Radiol Surg. 2014; 9: 283-293
        • Lin T.
        • Maire M.
        • Belongie S.
        • et al.
        Microsoft COCO: common objects in context.
        (Computer Vision- European Conference on Computer Vision)2014: 740-755
        • Repici A.
        • Ciscato C.
        • Correale L.
        • et al.
        Narrow-band Imaging International Colorectal Endoscopic Classification to predict polyp histology: REDEFINE study (with videos).
        Gastrointest Endosc. 2016; 84: 479-486
        • Westwood D.A.
        • Alexakis N.
        • Connor S.J.
        Transparent cap-assisted colonoscopy versus standard adult colonoscopy: a systematic review and meta-analysis.
        Dis Colon Rectum. 2012; 55: 218-225
        • Dik V.K.
        • Gralnek I.M.
        • Segol O.
        • et al.
        Multicenter, randomized, tandem evaluation of EndoRings colonoscopy—results of the CLEVER study.
        Endoscopy. 2015; 47: 1151-1158