Endoscopist Diagnostic Accuracy in Detecting Upper-GI Neoplasia in the Framework of Artificial Intelligence Studies.

AIMS Estimates on miss-rates for upper gastrointestinal neoplasia (UGIN) rely on registry data or old studies. Quality assurance programs for upper-GI endoscopy are not fully established due to the lack of the infrastructure to measure endoscopists' competence. We aimed at assessing endoscopists' accuracy for the recognition of UGIN exploiting the framework of Artificial Intelligence (AI) validation studies. METHODS Literature search among databases (PubMed/MEDLINE, EMBASE, Scopus) up to August 2020 was performed to identify articles evaluating the accuracy of individual endoscopists for the recognition of UGIN within studies validating AI against a histologically-verified expert-annotated ground-truth. Main outcomes were endoscopists' pooled sensitivity, specificity, positive and negative predictive values (PPV/NPV), Area Under the Curve (AUCs) for all-UGIN, for esophageal squamous-cell neoplasia (ESCN), Barrett-related neoplasia (BERN), and gastric adenocarcinoma (GAC). RESULTS Seven studies with 122 endoscopists were included (2 ESCN, 3 BERN, 1 GAC, 1 UGIN overall). Pooled endoscopists sensitivity and specificity for UGIN was 82% (CI 80-84%) and 79% (CI 76-81%), respectively. Endoscopists' accuracy was higher for GAC detection (AUC 0.95, CI 0.93-0.98) than ESCN detection (AUC 0.90, CI 0.88-0.92) and BERN detection (AUC 0.86, CI0.84-0.88). Sensitivity was higher for Eastern vs. Western endoscopists (87%, CI 84-89% vs. 75%, CI 72-78%), and for experts vs. non-experts endoscopists (85%, CI 83-87% vs. 71%, CI 67-75%). CONCLUSION We show suboptimal endoscopists' accuracy for the recognition of UGIN even within a framework that included higher prevalence and disease awareness. Future AI validation studies represent a framework to assess endoscopist competence.