Journal article
Impact of real-time use of artificial intelligence in improving adenoma detection during colonoscopy: A systematic review and meta-analysis
Endoscopy International Open, Vol.9(4), pp.E513-E521
03/17/2021
DOI: 10.1055/a-1341-0457
PMCID: PMC7969136
PMID: 33816771
Abstract
Background and study aims
With the advent of deep neural networks (DNN) learning, the field of artificial intelligence (AI) is rapidly evolving. Recent randomized controlled trials (RCT) have investigated the influence of integrating AI in colonoscopy and its impact on adenoma detection rates (ADRs) and polyp detection rates (PDRs). We performed a systematic review and meta-analysis to reliably assess if the impact is statistically significant enough to warrant the adoption of AI -assisted colonoscopy (AIAC) in clinical practice.
Methods
We conducted a comprehensive search of multiple electronic databases and conference proceedings to identify RCTs that compared outcomes between AIAC and conventional colonoscopy (CC). The primary outcome was ADR. The secondary outcomes were PDR and total withdrawal time (WT).
Results
Six RCTs (comparing AIAC vs CC) with 5058 individuals undergoing average-risk screening colonoscopy were included in the meta-analysis. ADR was significantly higher with AIAC compared to CC (33.7 % versus 22.9 %; odds ratio (OR) 1.76, 95 % confidence interval (CI) 1.55–2.00; I
2
= 28 %). Similarly, PDR was significantly higher with AIAC (45.6 % versus 30.6 %; OR 1.90, 95 %CI, 1.68–2.15, I
2
= 0 %). The overall WT was higher for AIAC compared to CC (mean difference [MD] 0.46 (0.00–0.92) minutes, I
2
= 94 %).
Conclusions
There is an increase in adenoma and polyp detection with the utilization of AIAC.
Details
- Title: Subtitle
- Impact of real-time use of artificial intelligence in improving adenoma detection during colonoscopy: A systematic review and meta-analysis
- Creators
- Munish Ashat - University of IowaJagpal Singh Klair - Virginia Mason Medical CenterDhruv Singh - Mayo Clinic in ArizonaArvind Rangarajan Murali - University of IowaRajesh Krishnamoorthi - Virginia Mason Medical Center
- Resource Type
- Journal article
- Publication Details
- Endoscopy International Open, Vol.9(4), pp.E513-E521
- DOI
- 10.1055/a-1341-0457
- PMID
- 33816771
- PMCID
- PMC7969136
- NLM abbreviation
- Endosc Int Open
- ISSN
- 2364-3722
- eISSN
- 2196-9736
- Publisher
- Georg Thieme Verlag KG
- Language
- English
- Date published
- 03/17/2021
- Academic Unit
- Gastroenterology and Hepatology; Internal Medicine
- Record Identifier
- 9984361723502771
Metrics
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