Literature DB >> 31586576

Development and validation of the international Blue Light Imaging for Barrett's Neoplasia Classification.

Sharmila Subramaniam1, Kesavan Kandiah1, Erik Schoon2, Patrick Aepli3, Bu' Hayee4, Andreas Pischel5, Milan Stefanovic6, Asma Alkandari7, Emmanuel Coron8, Masami Omae9, Francisco Baldaque-Silva9, Roberta Maselli10, Raf Bisschops11, Prateek Sharma12, Alessandro Repici10, Pradeep Bhandari1.   

Abstract

BACKGROUND AND AIMS: Detecting subtle Barrett's neoplasia during surveillance endoscopy can be challenging. Blue-light imaging (BLI) is a novel advanced endoscopic technology with high-intensity contrast imaging that may improve the identification of Barrett's neoplasia. The aim of this study was to develop and validate the first classification to enable characterization of neoplastic and non-neoplastic Barrett's esophagus using BLI.
METHODS: In phase 1, descriptors pertaining to neoplastic and non-neoplastic Barrett's esophagus were identified to form the classification, named the Blue Light Imaging for Barrett's Neoplasia Classification (BLINC). Phase 2 involved validation of these component criteria by 10 expert endoscopists assessing 50 BLI images. In phase 3, a web-based training module was developed to enable 15 general (nonexpert) endoscopists to use BLINC. They then validated the classification with an image assessment exercise in phase 4, and their pre- and post-training results were compared.
RESULTS: In phase 1 the descriptors were grouped into color, pit, and vessel pattern categories to form the classification. In phase 2 the sensitivity of neoplasia identification was 96.0% with a very good level of agreement among the experts (κ = .83). In phase 3, 15 general endoscopists completed the training module. In phase 4 their pretraining sensitivity (85.3%) improved significantly to 95.7% post-training with a good level of agreement (κ = .67).
CONCLUSIONS: We developed and validated a new classification system (BLINC) for the optical diagnosis of Barrett's neoplasia using BLI. Despite the limitations of this image-based study with a high prevalence of neoplasia, we believe it has the potential to improve the optical diagnosis of Barrett's neoplasia given the high degree of sensitivity (96%) noted. It is also a promising tool for training in Barrett's esophagus optical diagnosis using BLI.
Copyright © 2020. Published by Elsevier Inc.

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Year:  2019        PMID: 31586576     DOI: 10.1016/j.gie.2019.09.035

Source DB:  PubMed          Journal:  Gastrointest Endosc        ISSN: 0016-5107            Impact factor:   9.427


  3 in total

1.  A new artificial intelligence system successfully detects and localises early neoplasia in Barrett's esophagus by using convolutional neural networks.

Authors:  Mohamed Hussein; Juana González-Bueno Puyal; David Lines; Vinay Sehgal; Daniel Toth; Omer F Ahmad; Rawen Kader; Martin Everson; Gideon Lipman; Jacobo Ortiz Fernandez-Sordo; Krish Ragunath; Jose Miguel Esteban; Raf Bisschops; Matthew Banks; Michael Haefner; Peter Mountney; Danail Stoyanov; Laurence B Lovat; Rehan Haidry
Journal:  United European Gastroenterol J       Date:  2022-05-06       Impact factor: 6.866

Review 2.  Role of artificial intelligence in the diagnosis of oesophageal neoplasia: 2020 an endoscopic odyssey.

Authors:  Mohamed Hussein; Juana González-Bueno Puyal; Peter Mountney; Laurence B Lovat; Rehan Haidry
Journal:  World J Gastroenterol       Date:  2020-10-14       Impact factor: 5.742

Review 3.  Advanced imaging and artificial intelligence for Barrett's esophagus: What we should and soon will do.

Authors:  Marco Spadaccini; Edoardo Vespa; Viveksandeep Thoguluva Chandrasekar; Madhav Desai; Harsh K Patel; Roberta Maselli; Alessandro Fugazza; Silvia Carrara; Andrea Anderloni; Gianluca Franchellucci; Alessandro De Marco; Cesare Hassan; Pradeep Bhandari; Prateek Sharma; Alessandro Repici
Journal:  World J Gastroenterol       Date:  2022-03-21       Impact factor: 5.742

  3 in total

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