The University of Toledo Translation Journal of Medical Sciences 
Gastroenterology Abstract, Department of Medicine Research Symposium UTJMS 2023 May 05; 11(1):e1-e2 

Comparison of Artificial Intelligence with other 
interventions to improve Adenoma Detection Rate for 
Colonoscopy: A Network Meta-analysis 

Muhammad Aziz, MD1*, Hossein Haghbin1, Wasef Sayeh1, Halah Alfatlawi1, 
Manesh Kumar Gangwani1, Amir Humza Sohail1, Tamer Zahdeh1, Simcha 
Weissman1, Faisal Kamal1, Wade Lee-Smith2, Ali Nawras1, Prateek Sharma1, Aasma 
Shaukat1

1Division of Gastroenterology and Hepatology, Department of Medicine, The 
University of Toledo, Toledo, OH 43614 
2Department of University Libraries, The University of Toledo, Toledo, OH 43614 

*Corresponding author: Muhammad.Aziz@utoledo.edu

Published: 05 May 2023 

Introduction: Recent randomized controlled trials (RCTs) and meta-analysis have demonstrated 
improved adenoma detection rate (ADR) for colonoscopy with artificial intelligence (AI) compared to 
high-definition (HD) colonoscopy without AI. We aimed to perform a systematic review and network 
meta-analysis of all RCTs to assess the impact of AI compared to other endoscopic interventions aimed 
at increasing ADR such as distal attachment devices, dye-based/virtual chromoendoscopy, water-based 
techniques and balloon-assisted devices.   

Methods: A comprehensive literature search of PubMed/Medline, Embase, and Cochrane was 
performed through May 6, 2022 to include RCTs comparing ADR for any endoscopic intervention 
mentioned above. Network meta-analysis was conducted using a frequentist approach and random 
effects model. Relative risk (RR) and 95% confidence interval (CI) were calculated for proportional 
outcome. 

Results:  A total of 94 RCTs with 61172 patients (mean age 59.1±5.2 years, females 45.8%) and 20 
discrete study interventions were included. Network meta-analysis demonstrated significantly improved 
ADR for AI compared to Autofluorescence imaging (RR: 1.33, CI: 1.06-1.66),  dye-based 
chromoendoscopy (RR: 1.22, CI: 1.06-1.40), Endocap (RR: 1.32, CI: 1.17-1.50), Endocuff (RR: 1.19, 
CI: 1.04-1.35), Endocuff-Vision (RR: 1.26, CI: 1.13-1.41), Endoring (RR: 1.30, CI: 1.10-1.52), flexible 
spectral imaging color enhancement (RR: 1.26, CI: 1.09-1.46), Full-spectrum Endoscopy (RR: 1.40, CI: 
1.19-1.65), High-Definition (RR: 1.41, CI: 1.28-1.54), Linked Color Imaging (RR: 1.21, CI: 1.08-1.36), 
Narrow Band Imaging (RR: 1.33, CI: 1.18-1.48), Water-Exchange (RR: 1.22, CI: 1.06-1.42), and Water-
Immersion (RR: 1.47, CI: 1.19-1.82). 

https://dx.doi.org/10.46570/utjms.vol11-2023-652 

https://dx.doi.org/10.46570/utjms.vol11-2023-652
mailto:Muhammad.Aziz@utoledo.edu


https://dx.doi.org/10.46570/utjms.vol11-2023-652 UTJMS 11(1):e1-e2 

https://dx.doi.org/10.46570/utjms.vol11-2023-652 2 ©2023 UTJMS 

Conclusion:  AI demonstrated significantly improved ADR when compared to most endoscopic 
interventions. Future RCTs directly assessing these associations are encouraged. 

https://dx.doi.org/10.46570/utjms.vol11-2023-652
https://dx.doi.org/10.46570/utjms.vol11-2023-652