An interactive web-based educational tool improves detection and delineation of Barrett’s esophagus related neoplasia

Background & Aims: Endoscopic detection of early Barrett’s esophagus-related neoplasia (BORN) is a challenge. We aimed to develop a web-based teaching tool for improving detection and delineation of BORN. Methods: We made high-definition digital videos during endoscopies of patients with BORN and non-dysplastic Barrett’s esophagus (NDBE). Three experts superimposed their delineations of BORN lesions on the videos using special tools. In phase 1, 68 general endoscopists from 4 countries assessed 4 batches of 20 videos. After each batch, mandatory feedback compared assessors interpretations with those from experts . These data informed selection of 25 videos for the phase 2 module, which was completed by 121 new assessors from 5 countries. A 5-video test batch was completed before and after scoring of the four 5-video training batches. Mandatory feedback was as in phase 1. Outcome measures were scores for detection, delineation, agreement delineation, and relative delineation of BORN. Results: A linear mixed-effect model showed significant sequential improvement for all 4 outcomes over successive training batches in both phases. In phase 2, median detection rates of BORN in the test batch increased by 30% (P [less than].001) after training. From baseline to the end of the study, there were relative increases in scores of 46% for detection, 129% for delineation, 105% for agreement delineation, and 106% for relative delineation (all P [less than].001). Scores improved independent of assessors’ country of origin or level of endoscopic experience

Excitation of standing kink oscillations in coronal loops

In this work we review the efforts that have been done to study the excitation of the standing fast kink body mode in coronal loops. We mainly focus on the time-dependent problem, which is appropriate to describe flare or CME induced kink oscillations. The analytical and numerical studies in slab and cylindrical loop geometries are reviewed. We discuss the results from very simple one-dimensional models to more realistic (but still simple) loop configurations. We emphasise how the results of the initial value problem complement the eigenmode calculations. The possible damping mechanisms of the kink oscillations are also discussed

New strategies for endoscopic recognition of Barrett neoplasia

One of the most prominent clinical challenges in Barrett surveillance is the endoscopic recognition of early neoplasia. In this thesis, three different approaches to increase the visualization and recognition of early Barrett neoplasia are investigated. In part 1 (chapters 1-2) of this thesis, two new optical chromoscopy techniques are interrogated in two different clinical settings. Chapter 1 evaluates the additive value of blue light imaging (BLI) for the visualization of Barrett neoplasia when used by expert endoscopists, in particular for delineation of early BE neoplasia prior to endoscopic resection, which is generally performed in tertiary (i.e. expert) setting. Chapter 2 evaluates the additive value of optical chromoscopy techniques BLI and linked color imaging (LCI) when used by non-expert endoscopists. Part 2 (chapter 3) describes the development and validation of an online, interactive training tool that aims to enhance endoscopists’ ability to detect and delineate Barrett neoplasia by the use of high-quality HD-WLE video materials. Part 3 (chapters 4-8) focuses on the use of machine learning techniques for the primary detection and classification of early Barrett neoplasia. These techniques, often referred to as computer aided detection (CADe) or computer aided diagnosis (CADx) can assist endoscopists during endoscopic surveillance, by indicating areas of interest that are missed by the endoscopist or discriminate between neoplastic and non-neoplastic tissue. In chapter 9 the main findings of this thesis are reviewed and recommendations for further research are discussed

Deep-learning system detects neoplasia in patients with Barrett’s Esophagus with higher accuracy than endoscopists in a multi-step training and validation study with benchmarking

Background & Aims: We aimed to develop and validate a deep-learning computer-aided detection (CAD) system, suitable for use in real time in clinical practice, to improve endoscopic detection of early neoplasia in patients with Barrett's esophagus (BE). Methods: We developed a hybrid ResNet-UNet model CAD system using 5 independent endoscopy data sets. We performed pretraining using 494,364 labeled endoscopic images collected from all intestinal segments. Then, we used 1704 unique esophageal high-resolution images of rigorously confirmed early-stage neoplasia in BE and nondysplastic BE, derived from 669 patients. System performance was assessed by using data sets 4 and 5. Data set 5 was also scored by 53 general endoscopists with a wide range of experience from 4 countries to benchmark CAD system performance. Coupled with histopathology findings, scoring of images that contained early-stage neoplasia in data sets 2–5 were delineated in detail for neoplasm position and extent by multiple experts whose evaluations served as the ground truth for segmentation. Results: The CAD system classified images as containing neoplasms or nondysplastic BE with 89% accuracy, 90% sensitivity, and 88% specificity (data set 4, 80 patients and images). In data set 5 (80 patients and images) values for the CAD system vs those of the general endoscopists were 88% vs 73% accuracy, 93% vs 72% sensitivity, and 83% vs 74% specificity. The CAD system achieved higher accuracy than any of the individual 53 nonexpert endoscopists, with comparable delineation performance. CAD delineations of the area of neoplasm overlapped with those from the BE experts in all detected neoplasia in data sets 4 and 5. The CAD system identified the optimal site for biopsy of detected neoplasia in 97% and 92% of cases (data sets 4 and 5, respectively). Conclusions: We developed, validated, and benchmarked a deep-learning computer-aided system for primary detection of neoplasia in patients with BE. The system detected neoplasia with high accuracy and near-perfect delineation performance. The Netherlands National Trials Registry, Number: NTR707