Diagnostic accuracy of a novel artificial intelligence system for adenoma detection in daily practice: a prospective nonrandomized comparative study

Abstract

Background Adenoma detection rate (ADR) varies significantly between endoscopists, with adenoma miss rates (AMRs) up to 26 %. Artificial intelligence (AI) systems may improve endoscopy quality and reduce the rate of interval cancer. We evaluated the efficacy of an AI system in real-time colonoscopy and its influence on AMR and ADR.

Methods This prospective, nonrandomized, comparative study analyzed patients undergoing diagnostic colonoscopy at a single endoscopy center in Germany from June to October 2020. Every patient was examined concurrently by an endoscopist and AI using two opposing screens. The AI system, overseen by a second observer, was not visible to the endoscopist. AMR was the primary outcome. Both methods were compared using McNemar test.

Results 150 patients were included (mean age 65 years [standard deviation 14]; 69 women). There was no significant or clinically relevant difference (P = 0.75) in AMR between the AI system (6/197, 3.0 %; 95 % confidence interval [CI] 1.1–6.5) and routine colonoscopy (4/197, 2.0 %; 95 %CI 0.6–5.1). The polyp miss rate of the AI system (14/311, 4.5 %; 95 %CI 2.5–7.4) was not significantly different (P = 0.72) from routine colonoscopy (17/311, 5.5 %; 95 %CI 3.2–8.6). There was no significant difference (P = 0.50) in ADR between routine colonoscopy (78/150, 52.0 %; 95 %CI 43.7–60.2) and the AI system (76/150, 50.7 %; 95 %CI 42.4–58.9). Routine colonoscopy detected adenomas in two patients that were missed by the AI system.

Conclusion The AI system performance was comparable to that of experienced endoscopists during real-time colonoscopy with similar high ADR (> 50 %).

• References

• 1 Sung H, Ferlay J, Siegel RL. et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021; 71: 209-249
• 2 Fayad NF, Kahi CJ. Colonoscopy quality assessment. Gastrointest Endosc Clin N Am 2015; 25: 373-386
• 3 Corley DA, Jensen CD, Marks AR. et al. Adenoma detection rate and risk of colorectal cancer and death. N Engl J Med 2014; 370: 1298-1306
• 4 van Rijn JC, Reitsma JB, Stoker J. et al. Polyp miss rate determined by tandem colonoscopy: a systematic review. Am J Gastroenterol 2006; 101: 343-350
• 5 Ahn SB, Han DS, Bae JH. et al. The miss rate for colorectal adenoma determined by quality-adjusted, back-to-back colonoscopies. Gut Liver 2012; 6: 64-70
• 6 Leufkens AM, van Oijen MG, Vleggaar FP. et al. Factors influencing the miss rate of polyps in a back-to-back colonoscopy study. Endoscopy 2012; 44: 470-475
• 7 Zorzi M, Senore C, Da Re F. et al. Quality of colonoscopy in an organised colorectal cancer screening programme with immunochemical faecal occult blood test: the EQuIPE study (Evaluating Quality Indicators of the Performance of Endoscopy). Gut 2015; 64: 1389-1396
• 8 Goyal H, Mann R, Gandhi Z. et al. Scope of artificial intelligence in screening and diagnosis of colorectal cancer. J Clin Med 2020; 9: 3313
• 9 Hassan C, Spadaccini M, Iannone A. et al. Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis. Gastrointest Endosc 2021; 93: 77-85
• 10 Repici A, Badalamenti M, Maselli R. et al. Efficacy of real-time computer-aided detection of colorectal neoplasia in a randomized trial. Gastroenterology 2020; 159: 512-520
• 11 Klare P, Sander C, Prinzen M. et al. Automated polyp detection in the colorectum: a prospective study (with videos). Gastrointest Endosc 2019; 89: 576-582
• 12 Wang P, Berzin TM, Glissen Brown JR. et al. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut 2019; 68: 1813-1819
• 13 Milluzzo SM, Cesaro P, Grazioli LM. et al. Artificial intelligence in lower gastrointestinal endoscopy: the current status and future perspective. Clin Endosc 2021; 54: 329-339
• 14 Hassan C, Wallace MB, Sharma P. et al. New artificial intelligence system: first validation study versus experienced endoscopists for colorectal polyp detection. Gut 2020; 69: 799-800
• 15 Lai EJ, Calderwood AH, Doros G. et al. The Boston Bowel Preparation Scale: a valid and reliable instrument for colonoscopy-oriented research. Gastrointest Endosc 2009; 69: 620-625
• 16 Meester RGS, van Herk MMAGC, Lansdorp-Vogelaar I. et al. Prevalence and clinical features of sessile serrated polyps: a systematic review. Gastroenterology 2020; 159: 105-118
• 17 Hassan C, Badalamenti M, Maselli R. et al. Computer-aided detection-assisted colonoscopy: classification and relevance of false positives. Gastrointest Endosc 2020; 92: 900-904
• 18 Hartstein JD, Vemulapalli KC, Rex DK. The predictive value of small versus diminutive adenomas for subsequent advanced neoplasia. Gastrointest Endosc 2020; 91: 614-621
• 19 Jung YS, Kim TJ, Nam E. et al. Comparative systematic review and meta-analysis of 1- to 5-mm versus 6- to 9-mm adenomas on the risk of metachronous advanced colorectal neoplasia. Gastrointest Endosc 2020; 92: 692-701