Endoscopy 2017; 49(08): 813-819
DOI: 10.1055/s-0043-109430
Evidence in perspective
© Georg Thieme Verlag KG Stuttgart · New York

Computer-aided diagnosis for colonoscopy

Yuichi Mori
1   Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
Shin-ei Kudo
1   Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
Tyler M. Berzin
2   Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
Masashi Misawa
1   Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
Kenichi Takeda
1   Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
› Author Affiliations
Further Information

Publication History

submitted 09 January 2017

accepted after revision 03 April 2017

Publication Date:
24 May 2017 (online)

With recent breakthroughs in artificial intelligence, computer-aided diagnosis (CAD) for colonoscopy is gaining increasing attention. CAD allows automated detection and classification (i. e. pathological prediction) of colorectal polyps during real-time endoscopy, potentially helping endoscopists to avoid missing and mischaracterizing polyps. Although the evidence has not caught up with technological progress, CAD has the potential to improve the quality of colonoscopy, with some CAD systems for polyp classification achieving diagnostic performance exceeding the threshold required for optical biopsy. The present article provides an overview of this topic from the perspective of endoscopists, with a particular focus on evidence, limitations, and clinical applications.

  • References

  • 1 Kumar S, Thosani N, Ladabaum U. et al. Adenoma miss rates associated with a 3-minute versus 6-minute colonoscopy withdrawal time: a prospective, randomized trial. Gastrointest Endosc 2016; DOI: 10.1016/j.gie.2016.11.030.
  • 2 Rees CJ, Rajasekhar PT, 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 2017; 66: 887-895
  • 3 Fernandez-Esparrach G, Bernal J, Lopez-Ceron M. et al. Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps. Endoscopy 2016; 48: 837-842
  • 4 Corley DA, Jensen CD, Marks AR. et al. Adenoma detection rate and risk of colorectal cancer and death. NEJM 2014; 370: 1298-1306
  • 5 Karkanis SA, Iakovidis DK, Maroulis DE. et al. Computer-aided tumor detection in endoscopic video using color wavelet features. IEEE Trans Inf Technol Biomed 2003; 7: 141-152
  • 6 Tajbakhsh N, Gurudu SR, Liang J. Automated polyp detection in colonoscopy videos using shape and context information. IEEE Trans Med Imaging 2016; 35: 630-644
  • 7 Hassan C, Pickhardt PJ, Rex DK. A resect and discard strategy would improve cost-effectiveness of colorectal cancer screening. Clin Gastroenterol Hepatol 2010; 8: 865-869 e861–e863
  • 8 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
  • 9 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
  • 10 Rath T, Tontini GE, 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
  • 11 Rex DK, 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
  • 12 Tischendorf JJ, Gross S, Winograd R. et al. Computer-aided classification of colorectal polyps based on vascular patterns: a pilot study. Endoscopy 2010; 42: 203-207
  • 13 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
  • 14 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
  • 15 Misawa M, Kudo SE, Mori Y. et al. Characterization of colorectal lesions using a computer-aided diagnostic system for narrow-band imaging endocytoscopy. Gastroenterology 2016; 150: 1531-1532
  • 16 Li T, Cohen J, Craig M. et al. A novel computer vision program accurately identifies colonoscopic colorectal adenomas. Gastrointest Endosc 2016; 83: AB482
  • 17 Zhang R, Zheng Y, Mak TW. et al. Automatic detection and classification of colorectal polyps by transferring low-level CNN features from nonmedical domain. IEEE J Biomed Health Inform 2017; 21: 41-47
  • 18 Byrne MF, Rex DK, Chapados N. et al. Artificial intelligence (AI) in endoscopy-deep learning for optical biopsy of colorectal polyps in real-time on unaltered endoscopic videos. United European Gastroenterol J 2016; 4: A155
  • 19 Takemura Y, Yoshida S, Tanaka S. et al. Quantitative analysis and development of a computer-aided system for identification of regular pit patterns of colorectal lesions. Gastrointest Endosc 2010; 72: 1047-1051
  • 20 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
  • 21 Andre B, Vercauteren T, Buchner AM. et al. Software for automated classification of probe-based confocal laser endomicroscopy videos of colorectal polyps. World J Gastroenterol 2012; 18: 5560-5569
  • 22 Kuiper T, Alderlieste YA, Tytgat KM. et al. Automatic optical diagnosis of small colorectal lesions by laser-induced autofluorescence. Endoscopy 2015; 47: 56-62
  • 23 Aihara H, Saito S, Inomata H. et al. Computer-aided diagnosis of neoplastic colorectal lesions using 'real-time' numerical color analysis during autofluorescence endoscopy. Eur J Gastroenterol Hepatol 2013; 25: 488-494
  • 24 Inomata H, Tamai N, Aihara H. et al. Efficacy of a novel auto-fluorescence imaging system with computer-assisted color analysis for assessment of colorectal lesions. World J Gastroenterol 2013; 19: 7146-7153
  • 25 Le QV, Ranzato MA, Matthieu D. et al. Building high-level features using large scale unsupervised learning. In: Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 2013 May 26–31 Vancouver, Canada: IEEE; 2013: 8595-8598
  • 26 Philpotts LE. Can computer-aided detection be detrimental to mammographic interpretation?. Radiology 2009; 253: 17-22
  • 27 Yuan Y, Meng MQ. Deep learning for polyp recognition in wireless capsule endoscopy images. Med Phys 2017; 44: 1379-1389