Performance of natural language processing in identifying adenomas from colonoscopy reports: a systematic review and...

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.

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.

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.

NLP systems can identify adenoma detection from colonoscopy and pathology reports with strong operator characteristics.