Evaluation of a real-time computer-aided polyp detection system during screening colonoscopy: AI-DETECT study

 

 Buy Article Permissions and Reprints

Abstract

Background Polyp detection and resection during colonoscopy significantly reduce long-term colorectal cancer risk. Computer-aided detection (CADe) may increase polyp identification but has undergone limited clinical evaluation. Our aim was to assess the effectiveness of CADe at colonoscopy within a bowel cancer screening program (BCSP).

Methods This prospective, randomized controlled trial involved all eight screening-accredited colonoscopists at an English National Health Service (NHS) BCSP center (February 2020 to December 2021). Patients were randomized to CADe or standard colonoscopy. Patients meeting NHS criteria for bowel cancer screening were included. The primary outcome of interest was polyp detection rate (PDR).

Results 658 patients were invited and 44 were excluded. A total of 614 patients were randomized to CADe (n = 308) or standard colonoscopy (n = 306); 35 cases were excluded from the per-protocol analysis due to poor bowel preparation (n = 10), an incomplete procedure (n = 24), or a data issue (n = 1). Endocuff Vision was frequently used and evenly distributed (71.7 % CADe and 69.2 % standard). On intention-to-treat (ITT) analysis, there was a borderline significant difference in PDR (85.7 % vs. 79.7 %; P = 0.05) but no significant difference in adenoma detection rate (ADR; 71.4 % vs. 65.0 %; P = 0.09) for CADe vs. standard groups, respectively. On per-protocol analysis, no significant difference was observed in these rates. There was no significant difference in procedure times.

Conclusions In high-performing colonoscopists in a BCSP who routinely used Endocuff Vision, CADe improved PDR but not ADR. CADe appeared to have limited benefit in a BCSP setting where procedures are performed by experienced colonoscopists.

Publication History

Received: 18 May 2022

Accepted after revision: 16 September 2022

Article published online:
12 December 2022

© 2022. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

• References

• 1 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
  Google Scholar
• 2 Zhao S, Wang S, Pan P. et al. Magnitude, risk factors, and factors associated with adenoma miss rate of tandem colonoscopy: a systematic review and meta-analysis. Gastroenterology 2019; 156: 1661-1674
  Google Scholar
• 3 Burr NE, Derbyshire E, Taylor J. et al. Variation in post-colonoscopy colorectal cancer across colonoscopy providers in English National Health Service: population based cohort study. BMJ 2019; 367: l6090
  CrossrefPubMedGoogle Scholar
• 4 Rutter MD, Beintaris I, Valori R. et al. World Endoscopy Organization consensus statements on post-colonoscopy and post-imaging colorectal cancer. Gastroenterology 2018; 155: 909-925
  CrossrefPubMedGoogle Scholar
• 5 Pannala R, Krishnan K, Melson J. et al. Artificial intelligence in gastrointestinal endoscopy. VideoGIE 2020; 5: 598-613
  CrossrefPubMedGoogle Scholar
• 6 Rameshshanker R, Saunders BP. Number of significant polyps detected per six-minute withdrawal time at colonoscopy (SP6): a new measure of colonoscopy efficiency and quality. Frontline Gastroenterol 2020; 11: 491-493
  CrossrefPubMedGoogle Scholar
• 7 Liu X, Cruz Rivera S, Moher D. et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med 2020; 26: 1364-1374
  CrossrefPubMedGoogle Scholar
• 8 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
  CrossrefPubMedGoogle Scholar
• 9 Su J-R, Li Z, Shao X-J. et al. Impact of a real-time automatic quality control system on colorectal polyp and adenoma detection: a prospective randomized controlled study (with videos). Gastrointest Endosc 2020; 91: 415-424
  CrossrefPubMedGoogle Scholar
• 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
  Google Scholar
• 11 Repici A, Spadaccini M, Antonelli G. et al. Artificial intelligence and colonoscopy experience: lessons from two randomised trials. Gut 2022; 71: 757-765
  Google Scholar
• 12 Wallace MB, Sharma P, Bhandari P. et al. Impact of artificial intelligence on miss rate of colorectal neoplasia. Gastroenterology 2022; 163: 295-304
  Google Scholar
• 13 Shaukat A, Lichtenstein DR, Somers SC. et al. Computer-aided detection improves adenomas per colonoscopy for screening and surveillance colonoscopy: a randomized trial. Gastroenterology 2022; 163: 732-741
  CrossrefPubMedGoogle Scholar
• 14 Rondonotti E, di Paolo D, Rosa Rizzotto E. et al. Efficacy of a computer aided detection (CADe) system in a FIT-based organized colorectal cancer screening program: a randomized controlled trial (AIFIT study). Endoscopy 2022; DOI: 10.1055/a-1849-6878.
  Article in Thieme ConnectPubMedGoogle Scholar
• 15 Pesatori EV, Milluzzo SM, Cesaro P. et al. Incremental yield of artificial intelligence in a fecal occult blood test based organized screening population program. Gastrointest Endosc 2022; 95: AB243-AB244
  CrossrefPubMedGoogle Scholar
• 16 Ngu WS, Bevan R, Tsiamoulos ZP. et al. Improved adenoma detection with Endocuff Vision: the ADENOMA randomised controlled trial. Gut 2019; 68: 280-288
  CrossrefPubMedGoogle Scholar

• Supplementary Material