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Original research
Artificial intelligence and colonoscopy experience: lessons from two randomised trials
  1. Alessandro Repici1,2,
  2. Marco Spadaccini1,2,
  3. Giulio Antonelli3,4,
  4. Loredana Correale2,
  5. Roberta Maselli1,2,
  6. Piera Alessia Galtieri2,
  7. Gaia Pellegatta2,
  8. Antonio Capogreco1,2,
  9. Sebastian Manuel Milluzzo5,
  10. Gianluca Lollo6,
  11. Dhanai Di Paolo7,
  12. Matteo Badalamenti2,
  13. Elisa Ferrara2,
  14. Alessandro Fugazza2,
  15. Silvia Carrara2,
  16. Andrea Anderloni2,
  17. Emanuele Rondonotti7,
  18. Arnaldo Amato7,
  19. Andrea De Gottardi6,
  20. Cristiano Spada5,
  21. Franco Radaelli7,
  22. Victor Savevski8,
  23. Michael B Wallace9,
  24. Prateek Sharma10,11,
  25. Thomas Rösch12,
  26. Cesare Hassan3
  1. 1Department of Biomedical Sciences, Humanitas University, Milan, Italy
  2. 2Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy
  3. 3Gastroenterology and Digestive Endoscopy Unit, Ospedale Nuovo Regina Margherita, Roma, Italy
  4. 4Department of Translational and Precision Medicine, “Sapienza” University of Rome, Rome, Italy
  5. 5Digestive Endoscopy Unit, Poliambulanza Brescia Hospital, Brescia, Lombardia, Italy
  6. 6Department of Gastroenterology and Hepatology, Università della Svizzera Italiana, Lugano, Switzerland
  7. 7Division of Digestive Endoscopy and Gastroenterology, Valduce Hospital, Como, Italy
  8. 8Artificial Intelligence Research, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy
  9. 9Endoscopy unit, Mayo Clinic, Jacksonville, Florida, USA
  10. 10University of Kansas, Kansas City, Kansas, USA
  11. 11Endoscopy unit, University of Kansas city, Kansas city, Kansas, USA
  12. 12Interdisciplinary Endoscopy, University Hospital Hamburg-Eppendorf, Hamburg, Germany
  1. Correspondence to Professor Alessandro Repici, Gastroenerology and endoscopy Unit, IRCCS Humanitas Research Hospital, Rozzano, Lombardia, Italy; alessandro.repici{at}hunimed.eu

Abstract

Background and aims Artificial intelligence has been shown to increase adenoma detection rate (ADR) as the main surrogate outcome parameter of colonoscopy quality. To which extent this effect may be related to physician experience is not known. We performed a randomised trial with colonoscopists in their qualification period (AID-2) and compared these data with a previously published randomised trial in expert endoscopists (AID-1).

Methods In this prospective, randomised controlled non-inferiority trial (AID-2), 10 non-expert endoscopists (<2000 colonoscopies) performed screening/surveillance/diagnostic colonoscopies in consecutive 40–80 year-old subjects using high-definition colonoscopy with or without a real-time deep-learning computer-aided detection (CADe) (GI Genius, Medtronic). The primary outcome was ADR in both groups with histology of resected lesions as reference. In a post-hoc analysis, data from this randomised controlled trial (RCT) were compared with data from the previous AID-1 RCT involving six experienced endoscopists in an otherwise similar setting.

Results In 660 patients (62.3±10 years; men/women: 330/330) with equal distribution of study parameters, overall ADR was higher in the CADe than in the control group (53.3% vs 44.5%; relative risk (RR): 1.22; 95% CI: 1.04 to 1.40; p<0.01 for non-inferiority and p=0.02 for superiority). Similar increases were seen in adenoma numbers per colonoscopy and in small and distal lesions. No differences were observed with regards to detection of non-neoplastic lesions. When pooling these data with those from the AID-1 study, use of CADe (RR 1.29; 95% CI: 1.16 to 1.42) and colonoscopy indication, but not the level of examiner experience (RR 1.02; 95% CI: 0.89 to 1.16) were associated with ADR differences in a multivariate analysis.

Conclusions In less experienced examiners, CADe assistance during colonoscopy increased ADR and a number of related polyp parameters as compared with the control group. Experience appears to play a minor role as determining factor for ADR.

Trial registration number NCT:04260321.

  • colonoscopy
  • adenoma
  • artificial Intelligence
  • colorectal cancer
  • screening

Data availability statement

Data are available upon reasonable request.

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Data availability statement

Data are available upon reasonable request.

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Footnotes

  • Twitter @adegottardi

  • Contributors AR, MS and CH designed the study and drafted the manuscript. LC performed statistical analysis. AR, MS, GA, RM, PAG, AC, SMM, GL, MB, EF, AF, SC, AAn, AAm, ADG, CS, FR and CH recruited patients, performed colonoscopy procedures and/or participated in the data collection. MBW, PS, VS and TR critically revised the draft for important intellectual content. All the authors revised and approved the final manuscript.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests Conflict of interest statement/disclosure(s): All authors for equipment loan by Medtronic. AR and CH received consultancy fee from Medtronic. MBW provides consulting activity to Medtronic and Cosmo on behalf of Mayo Clinic and has equity interest in Virgo.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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