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Artificial Intelligence-assisted System Improves Endoscopic Identification of Colorectal Neoplasms

Published:September 13, 2019DOI:https://doi.org/10.1016/j.cgh.2019.09.009

      Background & Aims

      Precise optical diagnosis of colorectal polyps could improve the cost-effectiveness of colonoscopy and reduce polypectomy-related complications. However, it is difficult for community-based non-experts to obtain sufficient diagnostic performance. Artificial intelligence-based systems have been developed to analyze endoscopic images; they identify neoplasms with high accuracy and low interobserver variation. We performed a multi-center study to determine the diagnostic accuracy of EndoBRAIN, an artificial intelligence-based system that analyzes cell nuclei, crypt structure, and microvessels in endoscopic images, in identification of colon neoplasms.

      Methods

      The EndoBRAIN system was initially trained using 69,142 endocytoscopic images, taken at 520-fold magnification, from patients with colorectal polyps who underwent endoscopy at 5 academic centers in Japan from October 2017 through March 2018. We performed a retrospective comparative analysis of the diagnostic performance of EndoBRAIN vs that of 30 endoscopists (20 trainees and 10 experts); the endoscopists assessed images from 100 cases produced via white-light microscopy, endocytoscopy with methylene blue staining, and endocytoscopy with narrow-band imaging. EndoBRAIN was used to assess endocytoscopic, but not white-light, images. The primary outcome was the accuracy of EndoBrain in distinguishing neoplasms from non-neoplasms, compared with that of endoscopists, using findings from pathology analysis as the reference standard.

      Results

      In analysis of stained endocytoscopic images, EndoBRAIN identified colon lesions with 96.9% sensitivity (95% CI, 95.8%–97.8%), 100% specificity (95% CI, 99.6%–100%), 98% accuracy (95% CI, 97.3%–98.6%), a 100% positive-predictive value (95% CI, 99.8%–100%), and a 94.6% negative-predictive (95% CI, 92.7%–96.1%); these values were all significantly greater than those of the endoscopy trainees and experts. In analysis of narrow-band images, EndoBRAIN distinguished neoplastic from non-neoplastic lesions with 96.9% sensitivity (95% CI, 95.8–97.8), 94.3% specificity (95% CI, 92.3–95.9), 96.0% accuracy (95% CI, 95.1–96.8), a 96.9% positive-predictive value, (95% CI, 95.8–97.8), and a 94.3% negative-predictive value (95% CI, 92.3–95.9); these values were all significantly higher than those of the endoscopy trainees, sensitivity and negative-predictive value were significantly higher but the other values are comparable to those of the experts.

      Conclusions

      EndoBRAIN accurately differentiated neoplastic from non-neoplastic lesions in stained endocytoscopic images and endocytoscopic narrow-band images, when pathology findings were used as the standard. This technology has been authorized for clinical use by the Japanese regulatory agency and should be used in endoscopic evaluation of small polyps more widespread clinical settings. UMIN clinical trial no: UMIN000028843.

      Keywords

      Abbreviations used in this paper:

      AI (artificial intelligence), CAD (computer-aided diagnosis), EC (endocytoscopy), FDA (Food and Drug Administration), NBI (narrow-band imaging), NPV (negative predictive value), PIVI (Preservation and Incorporation of Valuable Endoscopic Innovations), PMDA (Pharmaceuticals and Medical Devices Agency), PPV (positive predictive value), SSA/Ps (sessile serrated adenomas/polyps), SaMD (software as a medical device), WLI (white light imaging)
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      References

        • Kaminski M.F.
        • Hassan C.
        • Bisschops R.
        • et al.
        Advanced imaging for detection and differentiation of colorectal neoplasia: European Society of Gastrointestinal Endoscopy (ESGE) Guideline.
        Endoscopy. 2014; 46: 435-449
        • 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
        • East J.E.
        • Suzuki N.
        • Bassett P.
        • et al.
        Narrow band imaging with magnification for the characterization of small and diminutive colonic polyps: pit pattern and vascular pattern intensity.
        Endoscopy. 2008; 40: 811-817
        • Kudo S.
        • Rubio C.A.
        • Teixeira C.R.
        • et al.
        Pit pattern in colorectal neoplasia: endoscopic magnifying view.
        Endoscopy. 2001; 33: 367-373
        • Patel S.G.
        • Schoenfeld P.
        • Kim H.
        • et al.
        Real-time characterization of diminutive colorectal polyp histology using narrow-band imaging: implications for the resect and discard strategy.
        Gastroenterology. 2016; 150: 406-418
        • Ladabaum U.
        • Fioritto A.
        • Mitani A.
        • et al.
        Real-time optical biopsy of colon polyps with narrow band imaging in community practice does not yet meet key thresholds for clinical decisions.
        Gastroenterology. 2013; 144: 81-91
        • Rees C.J.
        • Rajasekhar P.T.
        • Wilson A.
        • et al.
        Narrow band imaging optical diagnosis of small colorectal polyps in routine clinical practice: the Detect Inspect Characterise Resect and Discard 2 (DISCARD 2) study.
        Gut. 2016; 66: 887-895
        • 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
        • 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. 2015; 83: 643-649
        • 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
        • Misawa M.
        • Kudo S.E.
        • Mori Y.
        • et al.
        Accuracy of computer-aided diagnosis based on narrow-band imaging endocytoscopy for diagnosing colorectal lesions: comparison with experts.
        Int J Comput Assist Radiol Surg. 2017; 12: 757-766
        • Misawa M.
        • Kudo S.E.
        • Mori Y.
        • et al.
        Characterization of colorectal lesions using a computer-aided diagnostic system for narrow-band imaging endocytoscopy.
        Gastroenterology. 2016; 150: 1531-1532
        • Kudo S.
        • Wakamura K.
        • Ikehara N.
        • et al.
        Diagnosis of colorectal lesions with a novel endocytoscopic classification: a pilot study.
        Endoscopy. 2011; 43: 869-875
        • Kudo S.E.
        • Misawa M.
        • Wada Y.
        • et al.
        Endocytoscopic microvasculature evaluation is a reliable new diagnostic method for colorectal lesions (with video).
        Gastrointest Endosc. 2015; 82: 912-923
        • 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
        • Rex D.K.
        • Overhiser A.J.
        • Chen S.C.
        • et al.
        Estimation of impact of American College of Radiology recommendations on CT colonography reporting for resection of high-risk adenoma findings.
        Am J Gastroenterol. 2009; 104: 149-153
      1. The Paris endoscopic classification of superficial neoplastic lesions: esophagus, stomach, and colon: November 30 to December 1, 2002.
        Gastrointest Endosc. 2003; 58: S3-S43
        • Ting D.S.W.
        • Cheung C.Y.
        • Lim G.
        • et al.
        Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes.
        JAMA. 2017; 318: 2211-2223
        • Esteva A.
        • Kuprel B.
        • Novoa R.A.
        • et al.
        Corrigendum: dermatologist-level classification of skin cancer with deep neural networks.
        Nature. 2017; 546: 686
        • Davies J.
        • Burke D.
        • Olliver J.R.
        • et al.
        Methylene blue but not indigo carmine causes DNA damage to colonocytes in vitro and in vivo at concentrations used in clinical chromoendoscopy.
        Gut. 2007; 56: 155-156
        • Repici A.
        • Ciscato C.
        • Wallace M.
        • et al.
        Evaluation of genotoxicity related to oral methylene blue chromoendoscopy.
        Endoscopy. 2018; 50: 1027-1032
        • Takeda K.
        • Kudo S.E.
        • Mori Y.
        • et al.
        Accuracy of diagnosing invasive colorectal cancer using computer-aided endocytoscopy.
        Endoscopy. 2017; 49: 798-802
        • Ponugoti P.
        • Lin J.
        • Odze R.
        • et al.
        Prevalence of sessile serrated adenoma/polyp in hyperplastic-appearing diminutive rectosigmoid polyps.
        Gastrointest Endosc. 2017; 85: 622-627
        • Atkinson N.S.
        • East J.E.
        Optical biopsy and sessile serrated polyps: is DISCARD dead? Long live DISCARD-lite!.
        Gastrointest Endosc. 2015; 82: 118-121
        • Chinzei K.
        • Shimizu A.
        • Mori K.
        • et al.
        Regulatory science on AI-based medical devices and systems.
        Advanced Biomedical Engineering. 2018; 7: 118-123
        • Kutsukawa M.
        • Kudo S-e
        • Ikehara N.
        • et al.
        Efficiency of endocytoscopy in differentiating types of serrated polyps.
        Gastrointest Endosc. 2014; 79: 648-656
      2. Hetzel JT, Huang CS, Coukos JA, et al. Variation in the detection of serrated polyps in an average risk colorectal cancer screening cohort. Am J Gastroenterol;105:2656–2664.

      References

        • Ichimasa K.
        • Kudo S.E.
        • Mori Y.
        • et al.
        Double staining with crystal violet and methylene blue is appropriate for colonic endocytoscopy: an in vivo prospective pilot study.
        Dig Endosc. 2014; 26: 403-408
        • Kudo S.E.
        • Wakamura K.
        • Ikehara N.
        • et al.
        Diagnosis of colorectal lesions with a novel endocytoscopic classification: a pilot study.
        Endoscopy. 2011; 43: 869-875
        • Kudo T.
        • Kudo Se
        • Wakamura K.
        • et al.
        Diagnostic performance of endocytoscopy for evaluating the invasion depth of different morphological types of colorectal tumors.
        Dig Endosc. 2015; 27: 755-762
        • Kutsukawa M.
        • Kudo S-e
        • Ikehara N.
        • et al.
        Efficiency of endocytoscopy in differentiating types of serrated polyps.
        Gastrointest Endosc. 2014; 79: 648-656
        • Haralick R.M.
        • Shanmugam K.
        • Dinstein I.H.
        Textural features for image classification.
        IEEE Trans Syst Man Cybern B Cybern. 1973; 6: 610-621
        • Misawa M.
        • Kudo S.E.
        • Mori Y.
        • et al.
        Accuracy of computer-aided diagnosis based on narrow-band imaging endocytoscopy for diagnosing colorectal lesions: comparison with experts.
        Int J Comput Assist Radiol Surg. 2017; 12: 757-766
        • Cortes C.
        • Vapnik V.
        Support-vector networks.
        Mach Learn. 1995; 20: 273-297
        • Sato Y.
        • Westin C.-F.
        • Bhalerao A.
        • et al.
        Tissue classification based on 3D local intensity structures for volume rendering.
        IEEE Trans Vis Comput Graph. 2000; 6: 160-180