Endoscopists performance in optical diagnosis of colorectal polyps in artificial intelligence studies. Issue 8 (19th August 2022) - Pecere, Silvia; Antonelli, Giulio; Dinis‐Ribeiro, Mario; Mori, Yuichi; Hassan, Cesare; Fuccio, Lorenzo; Bisschops, Raf; Costamagna, Guido; Jin, Eun Hyo; Lee, Dongheon; Misawa, Masashi; Messmann, Helmut; et al.
Abstract: Widespread adoption of optical diagnosis of colorectal neoplasia is prevented by suboptimal endoscopist performance and lack of standardized training and competence evaluation. We aimed to assess diagnostic accuracy of endoscopists in optical diagnosis of colorectal neoplasia in the framework of artificial intelligence (AI) validation studies. Literature searches of databases (PubMed/MEDLINE, EMBASE, Scopus) up to April 2022 were performed to identify articles evaluating accuracy of individual endoscopists in performing optical diagnosis of colorectal neoplasia within studies validating AI against a histologically verified ground‐truth. The main outcomes were endoscopists' pooled sensitivity, specificity, positive and negative predictive value (PPV/NPV), positive and negative likelihood ratio (LR) and area under the curve (AUC for sROC) for predicting adenomas versus non‐adenomas. Six studies with 67 endoscopists and 2085 (IQR: 115–243, 5) patients were evaluated. Pooled sensitivity and specificity for adenomatous histology was respectively 84.5% (95% CI 80.3%–88%) and 83% (95% CI 79.6%–85.9%), corresponding to a PPV, NPV, LR+, LR− of 89.5% (95% CI 87.1%–91.5%), 75.7% (95% CI 70.1%–80.7%), 5 (95% CI 3.9%–6.2%) and 0.19 (95% CI 0.14%–0.25%). The AUC was 0.82 (CI 0.76–0.90). Expert endoscopists showed a higher sensitivity than non‐experts (90.5%, [95% CI 87.6%–92.7%] vs. 75.5%, [95% CI 66.5%–82.7%], p < 0.001), and Eastern endoscopists showed a higher sensitivity than Western (85%, [95% CI 80.5%–88.6%] vs. 75.8%, [95% CI 70.2%–80.6%]). Quality was graded high for 3 studies and low for 3 studies. We show that human accuracy for diagnosis of colorectal neoplasia in the setting of AI studies is suboptimal. Educational interventions could benefit by AI validation settings which seem a feasible framework for competence assessment.