Computer-aided diagnosis improves characterization of Barrett's neoplasia by general endoscopists (with video).

Background And Aims: Characterization of visible abnormalities in patients with Barrett's esophagus (BE) can be challenging, especially for inexperienced endoscopists. This results in suboptimal diagnostic accuracy and poor interobserver agreement. Computer-aided diagnosis (CADx) systems may assist endoscopists.

Incidence and Prediction of Unrelated Mortality After Successful Endoscopic Eradication Therapy for Barrett's Neoplasia.

Background & Aims: Follow-up (FU) strategies after endoscopic eradication therapy (EET) for Barrett's neoplasia do not consider the risk of mortality from causes other than esophageal adenocarcinoma (EAC). We aimed to evaluate this risk during long-term FU, and to assess whether the Charlson Comorbidity Index (CCI) can predict mortality.

Methods: We included all patients with successful EET from the nationwide Barrett registry in the Netherlands.

A deep learning system for detection of early Barrett's neoplasia: a model development and validation study.

Background: Computer-aided detection (CADe) systems could assist endoscopists in detecting early neoplasia in Barrett's oesophagus, which could be difficult to detect in endoscopic images. The aim of this study was to develop, test, and benchmark a CADe system for early neoplasia in Barrett's oesophagus.

Methods: The CADe system was first pretrained with ImageNet followed by domain-specific pretraining with GastroNet.

Graft dilatation and Barrett's esophagus in adults after gastric pull-up and jejunal interposition for long-gap esophageal atresia.

Background: Esophageal replacement (ER) with gastric pull-up (GPU) or jejunal interposition (JI) used to be the standard treatment for long-gap esophageal atresia (LGEA). Changes of the ER grafts on a macro- and microscopic level however, are unknown.

Aim: To evaluate long-term clinical symptoms and anatomical and mucosal changes in adolescents and adults after ER for LGEA.

Towards a robust and compact deep learning system for primary detection of early Barrett's neoplasia: Initial image-based results of training on a multi-center retrospectively collected data set.

Introduction: Endoscopic detection of early neoplasia in Barrett's esophagus is difficult. Computer Aided Detection (CADe) systems may assist in neoplasia detection. The aim of this study was to report the first steps in the development of a CADe system for Barrett's neoplasia and to evaluate its performance when compared with endoscopists.