Clin Endosc.  2024 Jan;57(1):18-23. 10.5946/ce.2023.092.

Computer-aided polyp characterization in colonoscopy: sufficient performance or not?

Affiliations
  • 1Clinical Effectiveness Research Group, Oslo University Hospital and University of Oslo, Oslo
  • 2Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway
  • 3Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan

Abstract

Computer-assisted polyp characterization (computer-aided diagnosis, CADx) facilitates optical diagnosis during colonoscopy. Several studies have demonstrated high sensitivity and specificity of CADx tools in identifying neoplastic changes in colorectal polyps. To implement CADx tools in colonoscopy, there is a need to confirm whether these tools satisfy the threshold levels that are required to introduce optical diagnosis strategies such as “diagnose-and-leave,” “resect-and-discard” or “DISCARD-lite.” In this article, we review the available data from prospective trials regarding the effect of multiple CADx tools and discuss whether they meet these thresholds.

Keyword

Artificial intelligence; Colonoscopy; Computer-assisted diagnosis; Optical diagnosis

Figure

  • Fig. 1. Example of computer-assisted polyp characterization (CADx) with EndoBRAIN (Cybernet Systems Corp.). NBI, narrow-band imaging.


Reference

1. Keum N, Giovannucci E. Global burden of colorectal cancer: emerging trends, risk factors and prevention strategies. Nat Rev Gastroenterol Hepatol. 2019; 16:713–732.
2. Vleugels JL, Greuter MJ, Hazewinkel Y, et al. Implementation of an optical diagnosis strategy saves costs and does not impair clinical outcomes of a fecal immunochemical test-based colorectal cancer screening program. Endosc Int Open. 2017; 5:E1197–E1207.
3. 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.
4. Kudo S, Hirota S, Nakajima T, et al. Colorectal tumours and pit pattern. J Clin Pathol. 1994; 47:880–885.
5. 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.
6. Houwen BB, Hassan C, Coupé VM, et al. Definition of competence standards for optical diagnosis of diminutive colorectal polyps: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement. Endoscopy. 2022; 54:88–99.
7. 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.
8. Atkinson NS, East JE. Optical biopsy and sessile serrated polyps: is DISCARD dead? Long live DISCARD-lite! Gastrointest Endosc. 2015; 82:118–121.
9. Willems P, Djinbachian R, Ditisheim S, et al. Uptake and barriers for implementation of the resect and discard strategy: an international survey. Endosc Int Open. 2020; 8:E684–E692.
10. Barua I, Wieszczy P, Kudo SE, et al. Real-time artificial intelligence-based optical diagnosis of neoplastic polyps during colonoscopy. NEJM Evid. 2022; 1:EVIDoa2200003.
11. Chan HP, Hadjiiski LM, Samala RK. Computer-aided diagnosis in the era of deep learning. Med Phys. 2020; 47:e218–e227.
12. Rondonotti E, Hassan C, Tamanini G, et al. Artificial intelligence-assisted optical diagnosis for the resect-and-discard strategy in clinical practice: the Artificial intelligence BLI Characterization (ABC) study. Endoscopy. 2023; 55:14–22.
13. 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.
14. Kuiper T, Alderlieste YA, Tytgat KM, et al. Automatic optical diagnosis of small colorectal lesions by laser-induced autofluorescence. Endoscopy. 2015; 47:56–62.
15. 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.
16. 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.
17. Mori Y, Kudo SE, 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.
18. 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.
19. Minegishi Y, Kudo SE, Miyata Y, et al. Comprehensive diagnostic performance of real-time characterization of colorectal lesions using an artificial intelligence-assisted system: a prospective study. Gastroenterology. 2022; 163:323–325.
20. Gupta S, Lieberman D, Anderson JC, et al. Recommendations for follow-up after colonoscopy and polypectomy: a consensus update by the US Multi-Society Task Force on Colorectal Cancer. Gastroenterology. 2020; 158:1131–1153.
21. Hassan C, Antonelli G, Dumonceau JM, et al. Post-polypectomy colonoscopy surveillance: European Society of Gastrointestinal Endoscopy (ESGE) Guideline. Update 2020. Endoscopy. 2020; 52:687–700.
22. Weigt J, Repici A, Antonelli G, et al. Performance of a new integrated computer-assisted system (CADe/CADx) for detection and characterization of colorectal neoplasia. Endoscopy. 2022; 54:180–184.
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