ArticleiGIE : innovation, investigation and insights2023
Performance of natural language processing in identifying adenomas from colonoscopy reports: a systematic review and meta-analysis.
Article in iGIE : innovation, investigation and insights, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A foundation systematic review of natural language processing applied to gastroenterology & hepatology.BMC gastroenterology · 2025Pooled it
- Accurate and Scalable Classification of Colonoscopy Neoplasia Using Machine Learning and Natural Language Processing.Clinical and translational gastroenterology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Aims: The adenoma detection rate is a key quality metric for colonoscopy and is inversely related to the post-colonoscopy colorectal cancer rate. Natural language processing can be used to automate the generation of such quality metrics from colonoscopy reports. We performed a systematic review and meta-analysis on the performance of natural language processing (NLP) in identifying adenoma detection in colonoscopy and paired pathology reports. Methods: We performed a systematic review and meta-analysis according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses recommendations. A literature query was conducted on MEDLINE, Embase, and Cochrane Database of Systematic Reviews through July 2022. Studies were included if they reported on the operator characteristics of an NLP algorithm in interpreting adenoma detection in colonoscopy and pathology reports. Two authors independently screened studies and abstracted data using an a priori designed data collection form. Performance characteristics were pooled by first using a univariate analysis, followed by a bivariate analysis of sensitivity and specificity. Results: The pooled specificity and sensitivity for identifying adenoma detection were .997 (95% confidence interval [CI], .984-.999) and .978 (95% CI, .938-.992). The pooled positive predictive value, negative predictive value, and F1 score were .997 (95% CI, .979-1.00), .977 (95% CI, .938-.992), and .982 (95% CI, .957-.993), respectively. In the bivariate analysis, the pooled specificity and sensitivity were .992 (95% CI, .978-.997) and .973 (95% CI, .929-.990). The NLP systems performed similarly well in identifying the detection of sessile serrated lesions and advanced adenomas. Conclusions: NLP systems can identify adenoma detection from colonoscopy and pathology reports with strong operator characteristics.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.