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An intensive regimen consisting of a low-fiber diet, bisacodyl, and a split dose of polyethylene glycol can achieve good colon preparation with an improved detection rate for polyps and adenomas in most patients who have had poor bowel cleansing at a previous colonoscopy.

Background and Aims The Seattle protocol for endoscopic Barrett's esophagus surveillance samples a small proportion of the mucosal surface area-risking a potentially high miss rate of early neoplastic lesions. We assess if the new iScan Optical Enhancement system (OE, Pentax) improves the detection of early BE associated neoplasia compared with high definition white light endoscopy (HD-WLE) in both expert and trainee endoscopists to target sampling of suspicious areas. Such a system may both improve early neoplasia detection and reduce the need for random biopsies. Methods 41 patients undergoing endoscopic BE surveillance from Jan 2016-Nov 2017 were recruited from 3 international referral centers. Matched still images in both HD-WLE (n=130) and iScan OE (n=132) were obtained from endoscopic examinations. Two experts, unblinded to the videos and histology, delineated known neoplasia, forming a consensus criterion standard. 7 expert and 7 trainee endoscopists marked one position per image where they would expect a target biopsy to identify dysplastic tissue. The same expert panel then reviewed magnification images and using a previously validated classification system attempted to classify mucosa as dysplastic or non-dysplastic based on the mucosal and vascular patterns observed on magnification endoscopy. Diagnostic accuracy, sensitivity, specificity, NPV, and PPV were calculated. Improvements in dysplasia detection in HD-WLE vs OE and interobserver agreement (IA) were assessed by multilevel logistic regression analysis and Krippendorff's alpha, respectively. Improvements in diagnostic performance were expressed as an odds ratio between the odds of an improvement in OE, compared with the odds of an improvement in WLE Results

Background and aims: Seattle protocol biopsies for Barrett's Esophagus (BE) surveillance are labour intensive with low compliance. Dysplasia detection rates vary, leading to missed lesions. This can potentially be offset with computer aided detection. We have developed convolutional neural networks (CNNs) to identify areas of dysplasia and where to target biopsy.Methods: 119 Videos were collected in high-definition white light and optical chromoendoscopy with i-scan (Pentax Hoya, Japan) imaging in patients with dysplastic and non-dysplastic BE (NDBE). We trained an indirectly supervised CNN to classify images as dysplastic/non-dysplastic using whole video annotations to minimise selection bias and maximise accuracy. The CNN was trained using 148,936 video frames (31 dysplastic patients, 31 NDBE, two normal esophagus), validated on 25,161 images from 11 patient videos and tested on 264 iscan-1 images from 28 dysplastic and 16 NDBE patients which included expert delineations. To localise targeted biopsies/delineations, a second directly supervised CNN was generated based on expert delineations of 94 dysplastic images from 30 patients. This was tested on 86 i-scan one images from 28 dysplastic patients. Findings:The indirectly supervised CNN achieved a per image sensitivity in the test set of 91%, specificity 79%, area under receiver operator curve of 93% to detect dysplasia. Per-lesion sensitivity was 100%. Mean assessment speed was 48 frames per second (fps). 97% of targeted biopsy predictions matched expert and histological assessment at 56 fps. The artificial intelligence system performed better than six endoscopists.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.