Improvement in adenoma detection rate by artificial intelligence-assisted colonoscopy: Multicenter quasi-randomized controlled trial

Abstract

Background and study aims

Adenoma detection rate (ADR) is a key performance measure with variability among endoscopists. Artificial intelligence (AI) in colonoscopy could reduce this variability and has shown to improve ADR. This study assessed the impact of AI on ADR among Danish endoscopists of varying experience levels.

Patients and methods

We conducted a prospective, quasi-randomized, controlled, multicenter trial involving patients aged 18 and older undergoing screening, surveillance, and diagnostic colonoscopy at four centers. Participants were assigned to AI-assisted colonoscopy (GI Genius, Medtronic) or conventional colonoscopy. Endoscopists were classified as experts (> 1000 colonoscopies) or non-experts (≤ 1000 colonoscopies). The primary outcome was ADR. We performed a subgroup analysis stratified on endoscopist experience and a subset analysis of the screening population.

Results

A total of 795 patients were analyzed: 400 in the AI group and 395 in the control group. The AI group demonstrated a significantly higher ADR than the control group (59.1% vs. 46.6%, P < 0.001). The increase was significant among experts (59.9% vs. 47.3%, P < 0.002) but not among non-experts. AI assistance significantly improved ADR (74.4% vs. 58.1%, P = 0.003) in screening colonoscopies. Polyp detection rate (PDR) was also higher in the AI group (69.8% vs. 56.2%, P < 0.001). There was no significant difference in the non-neoplastic resection rate (NNRR) (15.1% vs. 17.1%, P = 0.542).

Conclusions

AI-assisted colonoscopy significantly increased ADR by 12.5% overall, with a notable 16.3% increase in the screening population. The unchanged NNRR indicates that the higher PDR was due to increased ADR, not unnecessary resections.

Keywords

Endoscopy Lower GI Tract - Polyps / adenomas / ... - CRC screening - Endoscopic resection (polypectomy, ESD, EMRc, ...) - Colorectal cancer

• 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 Doubeni CA, Corley DA, Quinn VP. et al. Effectiveness of screening colonoscopy in reducing the risk of death from right and left colon cancer: a large community-based study. Gut 2018; 67: 291-298
• 3 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 e11
• 4 Corley DA, Levin TR, Doubeni CA. Adenoma detection rate and risk of colorectal cancer and death. N Engl J Med 2014; 370: 2541
• 5 Milluzzo SM, Cesaro P, Grazioli LM. et al. Artificial intelligence in lower gastrointestinal endoscopy: Yhe current status and future perspective. Clin Endosc 2021; 54: 329-339
• 6 Xu H, Tang RSY, Lam TYT. et al. Artificial intelligence-assisted colonoscopy for colorectal cancer screening: A multicenter randomized controlled trial. Clin Gastroenterol Hepatol 2023; 21: 337-346 e3
• 7 Nakashima H, Kitazawa N, Fukuyama C. et al. Clinical evaluation of computer-aided colorectal neoplasia detection using a novel endoscopic artificial intelligence: A single-center randomized controlled trial. Digestion 2023; 104: 193-201
• 8 Wang P, Liu X, Berzin TM. et al. Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study. Lancet Gastroenterol Hepatol 2020; 5: 343-351
• 9 Miyaguchi K, Tsuzuki Y, Hirooka N. et al. Linked-color imaging with or without artificial intelligence for adenoma detection: a randomized trial. Endoscopy 2024; 56: 376-383
• 10 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
• 11 Liu W-N, Zhang Y-Y, Bian X-Q. et al. Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy. Saudi J Gastroenterol 2020; 26: 13-19
• 12 Gimeno-García AZ, Hernández Negrin D, Hernández A. et al. Usefulness of a novel computer-aided detection system for colorectal neoplasia: a randomized controlled trial. Gastrointest Endosc 2023; 97: 528-536 e1
• 13 Kamba S, Tamai N, Saitoh I. et al. Reducing adenoma miss rate of colonoscopy assisted by artificial intelligence: a multicenter randomized controlled trial. J Gastroenterol 2021; 56: 746-757
• 14 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 e7
• 15 Repici A, Spadaccini M, Antonelli G. et al. Artificial intelligence and colonoscopy experience: lessons from two randomised trials. Gut 2022; 71: 757-765
• 16 Wallace MB, Sharma P, Bhandari P. et al. Impact of artificial intelligence on miss rate of colorectal neoplasia. Gastroenterology 2022; 163: 295-304 e5
• 17 Kastenberg D, Bertiger G, Brogadir S. Bowel preparation quality scales for colonoscopy. World J Gastroenterol 2018; 24: 2833-2843
• 18 Dansk Tarmkræftscreeningsdatabase. Årsrapport 2020. 2021.
• 19 Billot L, Copas A, Leyrat C. et al. How should a cluster randomized trial be analyzed?. J Epidemiol Popul Heal 2024; 72: 202196
• 20 Campbell MK, Piaggio G, Elbourne DR. et al. Consort 2010 statement: extension to cluster randomised trials. BMJ 2012; 345: e5661
• 21 Kaminski MF, Thomas-Gibson S, Bugajski M. et al. Performance measures for lower gastrointestinal endoscopy: a European Society of Gastrointestinal Endoscopy (ESGE) quality improvement initiative. United Eur Gastroenterol J 2017; 5: 309-334
• 22 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
• 23 Shiha MG, Oka P, Raju SA. et al. Artificial intelligence–assisted colonoscopy for adenoma and polyp detection: an updated systematic review and meta-analysis. iGIE 2023; 2: 333-343 e8
• 24 Engelke C, Graf M, Maass C. et al. Prospective study of computer-aided detection of colorectal adenomas in hospitalized patients. Scand J Gastroenterol 2023; 58: 1194-1199
• 25 Ashat M, Klair JS, Singh D. et al. Impact of real-time use of artificial intelligence in improving adenoma detection during colonoscopy: A systematic review and meta-analysis. Endosc Int Open 2021; 9: E513-E521
• 26 Dansk Tarmkræftscreeningsdatabase. Årsrapport for 2022. 2024.
• 27 Rondonotti E, Di Paolo D, Rizzotto ER. et al. Efficacy of a computer-Aided detection system in a fecal immunochemical test-based organized colorectal cancer screening program: A randomized controlled trial (AIFIT study). Endoscopy 2022; 54: 1171-1179
• 28 Marcondes FO, Gourevitch RA, Schoen RE. et al. Adenoma detection rate falls at the end of the day in a large multi-site sample. Dig Dis Sci 2018; 63: 856-859
• 29 Pickhardt PJ, Hassan C, Laghi A. et al. Small and diminutive polyps detected at screening CT colonography: a decision analysis for referral to colonoscopy. AJR Am J Roentgenol 2008; 190: 136-144
• 30 Yao L, Zhang L, Liu J. et al. Effect of an artificial intelligence-based quality improvement system on efficacy of a computer-aided detection system in colonoscopy: a four-group parallel study. Endoscopy 2022; 54: 757-768
• 31 Gong D, Wu L, Zhang J. et al. Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study. lancet Gastroenterol Hepatol 2020; 5: 352-361
• 32 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 e4