Desai, Madhav

INTRODUCTION: The performance of a high quality esophagogastroduodenoscopy (EGD) is dependent on the mucosal cleanliness. Recently, the Polprep: Effective Assessment of Cleanliness in EGD (PEACE) scale was created to assess the degree of mucosal cleanliness during EGD. The aim of this study was to validate this scoring system in a cohort of international endoscopists. METHODS: In total, 39 EGD videos, with different degrees of mucosal cleanliness were retrieved from a previously conducted prospective trial. All experts rated the cleanliness of the mucosa on each video using the PEACE scale. To evaluate agreement of all scores (0–3), intraclass correlation coefficient 2.1 was used. The agreement on adequate (scores 2 and 3) and inadequate (scores 0 and 1) cleanliness was assessed using kappa values. RESULTS: Videos evaluating esophagus, stomach, and duodenum cleanliness were reviewed by 16 endoscopists. The PEACE scores demonstrated good agreement (intraclass correlation coefficient 0.82, 95% CI 0.75–0.89), especially for esophagus (0.84; 95% CI 0.71–0.95) and stomach (0.81; 95% CI 0.69–0.91), while agreement was moderate for the duodenum (0.69; 95% CI 0.51–0.87). The agreement was similar between Eastern (0.86; 95% CI 0.79–0.92) and Western experts (0.80; 95% CI 0.72–0.88). Similarly, agreement regarding adequate cleanliness was comparable between Eastern (0.70; 95% CI 0.55–0.85) and Western (0.74; 95% CI 0.64–0.84) endoscopists being overall 0.75 (95% CI 0.65–0.85). DISCUSSION: The PEACE scoring system is a simple and reliable scale to assess the cleanliness during EGD. The score is now validated among international experts with high concordance, justifying its use in clinical practice.

Inflammatory bowel disease is a complex chronic inflammatory disorder with challenges in diagnosis, choosing appropriate therapy, determining individual responsiveness, and prediction of future disease course to guide appropriate management. Artificial intelligence has been examined in the field of inflammatory bowel disease endoscopy with promising data in different domains of inflammatory bowel disease, including diagnosis, assessment of mucosal activity, and prediction of recurrence and complications. Artificial intelligence use during endoscopy could be a step toward precision medicine in inflammatory bowel disease care pathways. We reviewed available data on use of artificial intelligence for diagnosis of inflammatory bowel disease, grading of severity, prediction of recurrence, and dysplasia detection. We examined the potential role of artificial intelligence enhanced endoscopy in various aspects of inflammatory bowel disease care and future perspectives in this review.

AbstractBackground and AimRecently, the BLI Adenoma Serrated International Classification (BASIC) system was developed by European experts to differentiate colorectal polyps. Our aim was to validate the BASIC classification system among the US‐based endoscopy experts.MethodsParticipants utilized a web‐based interactive learning system where the group was asked to characterize polyps using the BASIC criteria: polyp surface (presence of mucus, regular/irregular and [pseudo]depressed), pit appearance (featureless, round/non‐round with/without dark spots; homogeneous/heterogeneous distribution with/without focal loss), and vessels (present/absent, lacy, peri‐cryptal, irregular). The final testing consisted of reviewing BLI images/videos to determine whether the criteria accurately predicted the histology results. Confidence in adenoma identification (rated “1” to “5”) and agreement in polyp (adenoma vs non‐adenoma) identification and characterization per BASIC criteria were derived. Strength of interobserver agreement with kappa (k) value was reported for adenoma identification.ResultsTen endoscopy experts from the United States identified conventional adenoma (vs non‐adenoma) with 94.4% accuracy, 95.0% sensitivity, 93.8% specificity, 93.8% positive predictive value, and 94.9% negative predictive value using BASIC criteria. Overall strength of interobserver agreement was high: kappa 0.89 (0.82–0.96). Agreement for the individual criteria was as follows: surface mucus (93.8%), regularity (65.6%), type of pit (40.6%), pit visibility (66.9%), pit distribution (57%), vessel visibility (73%), and being lacy (46%) and peri‐cryptal (61%). The confidence in diagnosis was rated at high ≥4 in 67% of the cases.ConclusionsA group of US‐based endoscopy experts have validated a simple and easily reproducible BLI classification system to characterize colorectal polyps with >90% accuracy and a high level of interobserver agreement.