Computer-aided quality assessment (CAQ): the next step for artificial intelligence in colonoscopy?

Abstract

Recent developments and breakthroughs in artificial intelligence (AI) for colonoscopy have the potential to improve the quality of colonoscopy. Computer-aided detection for colorectal polyps has been shown to increase the adenoma detection rate by more than 10%. Furthermore, recently developed computer-aided quality assessment (CAQ) systems, such as real-time withdrawal speed monitoring, are expected to provide additional gain in colonoscopy practice. However, the added clinical value of combining AI techniques is uncertain. This paper provides an overview of the latest evidence on CAQ systems and identifies knowledge gaps that need to be filled before widespread implementation.

Keywords

Artificial intelligence (AI) / computer-aided quality assessment (CAQ) / computer-aided detection (CADe) / computer-aided diagnosis (CADx)

References

[1] Sung H,Siegel RL.Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J Clin2021;71:209-49

[2] Schreuders EH,Rabeneck L.Colorectal cancer screening: a global overview of existing programmes.Gut2015;64:1637-49

[3] Zauber AG,O’Brien MJ.Colonoscopic polypectomy and long-term prevention of colorectal-cancer deaths.N Engl J Med2012;23:687-96 PMCID:PMC3322371

[4] Kaminski MF,Kraszewska E.Quality Indicators for Colonoscopy and the Risk of Interval Cancer.N Engl J Med2010;362:1795-803

[5] Zhao S,Pan P.Magnitude, risk factors, and factors associated with adenoma miss rate of tandem colonoscopy: a systematic review and meta-analysis.Gastroenterology2019;156:1661-74.e11

[6] Robertson DJ,Winawer SJ.Colorectal cancers soon after colonoscopy: a pooled multicohort analysis.Gut2014;63:949-56 PMCID:PMC4383397

[7] Rex DK,Lemmel GT.Colonoscopic miss rates of adenomas determined by back-to-back colonoscopies.Gastroenterology1997;112:24-8

[8] Anderson R,Valori R.Causes of post-colonoscopy colorectal cancers based on world endoscopy organization system of analysis.Gastroenterology2020;158:1287-99.e2

[9] Barua I,Jodal H.Artificial intelligence for polyp detection during colonoscopy: a systematic review and meta-analysis.Endoscopy2021;53:277-84

[10] Hassan C,Iannone A.Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis.Gastrointest Endosc2021;93:77-85.e6

[11] Corley DA,Marks AR.Adenoma detection rate and risk of colorectal cancer and death.N Engl J Med2014;370:1298-306

[12] Wang P,Glissen Brown JR.Lower adenoma miss rate of computer-aided detection-assisted colonoscopy vs routine white-light colonoscopy in a prospective tandem study.Gastroenterology2020;159:1252-61.e5

[13] Gong D,Zhang J.Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study.Lancet Gastroenterol Hepatol2020;5:352-61

[14] Su JR,Shao XJ.Impact of a real-time automatic quality control system on colorectal polyp and adenoma detection: a prospective randomized controlled study (with videos).Gastrointest Endosc2020;91:415-24.e4.

[15] Yao L,Liu J.Effect of an artificial intelligence-based quality improvement system significantly improved the efficacy of computer-aided detection system in colonoscopy: a four-group parallel study.Endoscopy2021;25

[16] Vavricka SR,Degen L.Monitoring colonoscopy withdrawal time significantly improves the adenoma detection rate and the performance of endoscopists.Endoscopy2016;48:256-62

[17] Sawhney MS,Neeman N.Effect of institution-wide policy of colonoscopy withdrawal time ≥ 7 minutes on polyp detection.Gastroenterology2008;135:1892-8

[18] Barclay RL,Doughty AS,Greenlaw RL.Colonoscopic withdrawal times and adenoma detection during screening colonoscopy. N Engl J Med 2006;355:2533-41.