Artificial intelligence and colonoscopy experience: lessons from two randomised trials. Issue 4 (29th June 2021)
- Record Type:
- Journal Article
- Title:
- Artificial intelligence and colonoscopy experience: lessons from two randomised trials. Issue 4 (29th June 2021)
- Main Title:
- Artificial intelligence and colonoscopy experience: lessons from two randomised trials
- Authors:
- Repici, Alessandro
Spadaccini, Marco
Antonelli, Giulio
Correale, Loredana
Maselli, Roberta
Galtieri, Piera Alessia
Pellegatta, Gaia
Capogreco, Antonio
Milluzzo, Sebastian Manuel
Lollo, Gianluca
Di Paolo, Dhanai
Badalamenti, Matteo
Ferrara, Elisa
Fugazza, Alessandro
Carrara, Silvia
Anderloni, Andrea
Rondonotti, Emanuele
Amato, Arnaldo
De Gottardi, Andrea
Spada, Cristiano
Radaelli, Franco
Savevski, Victor
Wallace, Michael B
Sharma, Prateek
Rösch, Thomas
Hassan, Cesare - Abstract:
- 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 differencesAbstract : 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. … (more)
- Is Part Of:
- Gut. Volume 71:Issue 4(2022)
- Journal:
- Gut
- Issue:
- Volume 71:Issue 4(2022)
- Issue Display:
- Volume 71, Issue 4 (2022)
- Year:
- 2022
- Volume:
- 71
- Issue:
- 4
- Issue Sort Value:
- 2022-0071-0004-0000
- Page Start:
- 757
- Page End:
- 765
- Publication Date:
- 2021-06-29
- Subjects:
- colonoscopy -- adenoma -- artificial Intelligence -- colorectal cancer -- screening
Gastroenterology -- Periodicals
616.33 - Journal URLs:
- http://gut.bmjjournals.com ↗
http://www.bmj.com/archive ↗ - DOI:
- 10.1136/gutjnl-2021-324471 ↗
- Languages:
- English
- ISSNs:
- 0017-5749
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 26382.xml