Systematic review and meta-analysisAccuracy of artificial intelligence on histology prediction and detection of colorectal polyps: a systematic review and meta-analysis
Section snippets
Definition
In this study, colorectal polyp was defined as any endoscopic lesion that had been removed and examined histologically. We defined adenomatous polyp as a polyp with an adenomas component that was histologically proven. Nonadenomatous polyps were polyps without an adenomatous component, including hyperplastic polyps and serrated lesions. Serrated polyps, when reported separately, were defined as polyps with a serrated component, including traditional serrated adenomas and sessile serrated
Literature search and bias assessment
Among the 8004 studies retrieved, 6763 were selected after removal of duplicates. Eighteen studies were selected for inclusion in the meta-analysis of polyp histology prediction and 6 studies for polyp detection analysis according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses flowchart (Supplementary Fig. 1, available online at www.giejournal.org). The characteristics of these included studies are presented in Tables 1 and 2.
The quality of included studies and the
Discussion
Although a number of studies had reported the performance of AI on colorectal polyp detection and characterization, this is the first systematic review and meta-analysis on the performance of AI on histology prediction and detection of colorectal polyps. Herein, we showed that AI was accurate on histology prediction, with a pooled sensitivity of 92.3%, specificity 89.8%, and AUC .96. For polyp detection, the respective pooled sensitivity and specificity of AI was 95% and 88%, with AUC of .90.
References (46)
- et al.
The American Society for Gastrointestinal Endoscopy PIVI (Preservation and Incorporation of Valuable Endoscopic Innovations) on real-time endoscopic assessment of the histology of diminutive colorectal polyps
Gastrointest Endosc
(2011) - et al.
ASGE technology committee systematic review and meta-analysis assessing the ASGE PIVI thresholds for adopting real-time endoscopic assessment of the histology of diminutive colorectal polyps
Gastrointest Endosc
(2015) - et al.
AGA white paper: training and implementation of endoscopic image enhancement technologies
Clin Gastroenterol Hepatol
(2017) - et al.
Computer-based classification of small colorectal polyps by using narrow-band imaging with optical magnification
Gastrointest Endosc
(2011) - et al.
Computer-aided system for predicting the histology of colorectal tumors by using narrow-band imaging magnifying colonoscopy (with video)
Gastrointest Endosc
(2012) - et al.
Novel computer-aided diagnostic system for colorectal lesions by using endocytoscopy (with videos)
Gastrointest Endosc
(2015) - et al.
Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy
Gastrointest Endosc
(2016) - et al.
Accurate classification of diminutive colorectal polyps using computer-aided analysis
Gastroenterology
(2018) - et al.
Detection and diagnosis of sessile serrated adenoma/polyps using convolutional neural network (artificial intelligence)
Gastrointest Endosc
(2018) - et al.
Artificial intelligence- assisted polyp detection system for colonoscopy, based on the largest available collection of clinical video data for machine learning [abstract]
Gastrointest Endosc
(2019)
Use of artificial intelligence image classifier for real-time detection of colonic polyps
Gastrointest Endosc
Automated insertion time, cecal intubation and withdrawal time during live colonoscopy using convolutional neural networks—a video validation study [abstract]
Gastrointest Endosc
Narrow band imaging to differentiate neoplastic and non-neoplastic colorectal polyps in real time: a meta-analysis of diagnostic operating characteristics
Gut
Usefulness of magnifying narrow-band imaging endoscopy for the diagnosis of gastric and colorectal lesions
Digestion
Proposal of a new “resect and discard” strategy using magnifying narrow band imaging: pilot study of diagnostic accuracy
Dig Endosc
Validation of a simple classification system for endoscopic diagnosis of small colorectal polyps using narrow-band imaging
Gastroenterology
A resect and discard strategy would improve cost-effectiveness of colorectal cancer screening
Clin Gastroenterol Hepatol
Advanced endoscopic imaging: European Society of Gastrointestinal Endoscopy (ESGE) technology review
Endoscopy
Computer-aided classification of colorectal polyps based on vascular patterns: a pilot study
Endoscopy
Software for automated classification of probe-based confocal laser endomicroscopy videos of colorectal polyps
World J Gastroenterol
In vivo real-time assessment of colorectal polyp histology using an optical biopsy forceps system based on laser-induced fluorescence spectroscopy
Endoscopy
Impact of an automated system for endocytoscopic diagnosis of small colorectal lesions: an international web-based study
Endoscopy
Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model
Gut
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DISCLOSURE: The following author disclosed financial relationships: W. K. Leung: Advisory board for NISI (Hong Kong) Company; consultant for Medtronics. All other authors disclosed no financial relationships.
If you would like to chat with an author of this article, you may contact Dr Leung at [email protected].