» Articles » PMID: 30610858

An Interactive Web-Based Educational Tool Improves Detection and Delineation of Barrett's Esophagus-Related Neoplasia

Overview
Specialty Gastroenterology
Date 2019 Jan 6
PMID 30610858
Citations 23
Authors
Affiliations
Soon will be listed here.
Abstract

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. Three experts superimposed their delineations of BORN lesions on the videos using special tools. In phase one, 68 general endoscopists from 4 countries assessed 4 batches of 20 videos. After each batch, mandatory feedback compared the assessors' interpretations with those from experts. These data informed the 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 < .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 < .001). Scores improved independent of assessors' country of origin or level of endoscopic experience.

Conclusions: We developed a web-based teaching tool for endoscopic recognition of BORN that is easily accessible, efficient, and increases detection and delineation of neoplastic lesions. Widespread use of this tool might improve management of Barrett's esophagus by general endoscopists.

Citing Articles

Effect of an online educational module incorporating real-time feedback on accuracy of polyp sizing in trainees: a randomized controlled trial.

Mun E, Yen T, Hochheimer C, Tarter W, Kaltenbach T, Keswani R Endoscopy. 2024; 56(6):421-430.

PMID: 38224964 PMC: 11139550. DOI: 10.1055/a-2245-6526.


Deep learning based identification of pituitary adenoma on surgical endoscopic images: a pilot study.

Fuse Y, Takeuchi K, Hashimoto N, Nagata Y, Takagi Y, Nagatani T Neurosurg Rev. 2023; 46(1):291.

PMID: 37910280 DOI: 10.1007/s10143-023-02196-w.


High Proportions of Newly Detected Visible Lesions and Pathology Grade Change Among Patients with Barrett's Esophagus Referred to Expert Centers.

Davis C, Fuller A, Katzka D, Wani S, Sawas T Dig Dis Sci. 2023; 68(9):3584-3595.

PMID: 37402985 DOI: 10.1007/s10620-023-07968-4.


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.

Fockens K, Jukema J, Boers T, Jong M, van der Putten J, Pouw R United European Gastroenterol J. 2023; 11(4):324-336.

PMID: 37095718 PMC: 10165317. DOI: 10.1002/ueg2.12363.


Interactive training versus self-driven training in the prediction of colorectal polyp histology by trainees using the NICE classification.

Wang J, Qiao W, Ren Y, Chen Y, Gong W BMC Gastroenterol. 2023; 23(1):47.

PMID: 36823614 PMC: 9947896. DOI: 10.1186/s12876-023-02680-z.