Radiofrequency Ablation in Barrett's Esophagus

Radiofrequency ablation (RFA) is an endoscopic modality used in the treatment of Barrett's esophagus. RFA may be performed using a balloon-based catheter or using one of the probe catheters that attaches to the distal end of the endoscope. Here we demonstrate step-by-step instruction in using radiofrequency ablation in the treatment of Barrett's esophagus and highlight key concepts in the technique. Keywords: Barrett's esophagus, Radiofrequency ablation, Vide

Advanced Imaging and Sampling in Barrett's Esophagus: Artificial Intelligence to the Rescue?

Because the current Barrett's esophagus (BE) surveillance protocol suffers from sampling error of random biopsies and a high miss-rate of early neoplastic lesions, many new endoscopic imaging and sampling techniques have been developed. None of these techniques, however, have significantly increased the diagnostic yield of BE neoplasia. In fact, these techniques have led to an increase in the amount of visible information, yet endoscopists and pathologists inevitably suffer from variations in intra- and interobserver agreement. Artificial intelligence systems have the potential to overcome these endoscopist-dependent limitations

Prospective development and validation of a volumetric laser endomicroscopy computer algorithm for detection of Barrett’s neoplasia

Background and Aims Volumetric laser endomicroscopy (VLE) is an advanced imaging modality used to detect Barrett’s esophagus (BE) dysplasia. However, real-time interpretation of VLE scans is complex and time-consuming. Computer-aided detection (CAD) may aid in the process of VLE image interpretation. Our aim was to train and validate a CAD algorithm for VLE-based detection of BE neoplasia. Methods The multicenter, VLE PREDICT study, prospectively enrolled 47 BE patients. In total, 229 nondysplastic BE, and 89 neoplastic (HGD/EAC) targets were laser marked under VLE guidance and subsequently biopsied for histological diagnosis. Deep convolutional neural networks were used to construct a CAD algorithm for differentiation between nondysplastic and neoplastic BE tissue. The CAD algorithm was trained on a set consisting of the first 22 patients (134 NDBE and 38 neoplastic targets) and validated on a separate test set of patients 23 to 47 (95 NDBE and 51 neoplastic targets). Finally, algorithm performance was benchmarked against the performance of 10 VLE experts. Results Using the Training set to construct the algorithm resulted in an accuracy of 92%, sensitivity of 95% and specificity of 92%. When performance was assessed on the Test set, accuracy, sensitivity, and specificity were 85%, 91%, and 82%, respectively. The algorithm outperformed all 10 VLE experts, who demonstrated an overall accuracy of 77%, sensitivity of 70%, and specificity of 81%. Conclusions We developed, validated, and benchmarked a VLE CAD algorithm for detection of BE neoplasia using prospectively collected and biopsy-correlated VLE targets. The algorithm detected neoplasia with high accuracy and outperformed 10 VLE experts