OTH-004 blue light imaging for optical diagnosis of colorectal polyps: the impact of a training intervention
OTH-004 blue light imaging for optical diagnosis of colorectal polyps: the impact of a training intervention
Introduction: the advent of image enhanced endoscopic modalities have paved the way for better optical diagnosis of colorectal polyps. Blue Light Imaging (BLI) is a new technology that utilises powerful light emitting diode technology to enhance mucosal surface and vessel patterns. A specific BLI classification has recently been developed to enable better characterisation of colorectal polyps (BLI Adenoma Serrated International Classification - BASIC) The aim of our study was to investigate the diagnostic ability of BLI before and after training using this classification in experienced and non-experienced endoscopists.
Methods: BLI images from 45 polyps were shown to 10 endoscopists (5 with experience of advanced endoscopic imaging and 5 trainees with limited experience). They independently classified each of the images as adenoma or hyperplastic initially without any focused training on interpretation of BLI images. A training module on BASIC was developed and each endoscopist undertook a face to face training session where direct feedback was given. All endoscopists then repeated the image classification exercise. The sensitivity, specificity, accuracy, negative (NPV) and positive predictive value (PPV) for adenoma detection was calculated.
Results: in both groups of endoscopists, there was a significant improvement in sensitivity and NPV of adenoma detection (p<0.05) following training and utilisation of a dedicated BLI classification system (see table below). This improvement was greater in the experienced endoscopist cohort where overall higher accuracy rates were achieved with no decrease in specificity. [OTH-004 Table 1 not included].
Conclusions: the use of a bespoke BLI classification system with adequate training can significantly improve the sensitivity and NPV of adenoma detection in both experienced and non-experienced endoscopists thereby enabling the full potential of this novel imaging technology to be realised.
A269-A270
Subramaniam, Sharmila
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Alkandari, Asma
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Kandiah, Kesavan
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Smith, Rebecca
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Stammers, Matthew
a4ad3bd5-7323-4a6d-9c00-2c34f8ae5bd3
Thayalasekaran, Sreedhari
faeaf07c-7303-46ae-a400-c06754712695
Aepli, Patrick
a469a90d-3fea-43d3-8ca5-642da381a75a
Hayee, Bu’
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Schoon, Erik
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Stefanovic, Milan
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Bhandari, Pradeep
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Subramaniam, Sharmila
4994d15f-b6cb-4ff5-ae83-ddf429807bfa
Alkandari, Asma
e0500692-ff32-4722-921e-be8e214c66df
Kandiah, Kesavan
65a0a577-cb0b-4b9c-b1b5-d44d4a266285
Smith, Rebecca
3e5e0e0e-08a8-4466-9da2-7ab4296aa753
Stammers, Matthew
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Thayalasekaran, Sreedhari
faeaf07c-7303-46ae-a400-c06754712695
Aepli, Patrick
a469a90d-3fea-43d3-8ca5-642da381a75a
Hayee, Bu’
f40df04c-e9c3-4045-b2d2-873269bdadc8
Schoon, Erik
7a060d27-18f4-4bf9-b965-da6d17d759ad
Stefanovic, Milan
1996d356-ba86-475d-8288-7dd88860db90
Bhandari, Pradeep
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Subramaniam, Sharmila, Alkandari, Asma, Kandiah, Kesavan, Smith, Rebecca, Stammers, Matthew, Thayalasekaran, Sreedhari, Aepli, Patrick, Hayee, Bu’, Schoon, Erik, Stefanovic, Milan and Bhandari, Pradeep
(2018)
OTH-004 blue light imaging for optical diagnosis of colorectal polyps: the impact of a training intervention.
Gut, 67, .
(doi:10.1136/gutjnl-2018-BSGAbstracts.526).
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Meeting abstract
Abstract
Introduction: the advent of image enhanced endoscopic modalities have paved the way for better optical diagnosis of colorectal polyps. Blue Light Imaging (BLI) is a new technology that utilises powerful light emitting diode technology to enhance mucosal surface and vessel patterns. A specific BLI classification has recently been developed to enable better characterisation of colorectal polyps (BLI Adenoma Serrated International Classification - BASIC) The aim of our study was to investigate the diagnostic ability of BLI before and after training using this classification in experienced and non-experienced endoscopists.
Methods: BLI images from 45 polyps were shown to 10 endoscopists (5 with experience of advanced endoscopic imaging and 5 trainees with limited experience). They independently classified each of the images as adenoma or hyperplastic initially without any focused training on interpretation of BLI images. A training module on BASIC was developed and each endoscopist undertook a face to face training session where direct feedback was given. All endoscopists then repeated the image classification exercise. The sensitivity, specificity, accuracy, negative (NPV) and positive predictive value (PPV) for adenoma detection was calculated.
Results: in both groups of endoscopists, there was a significant improvement in sensitivity and NPV of adenoma detection (p<0.05) following training and utilisation of a dedicated BLI classification system (see table below). This improvement was greater in the experienced endoscopist cohort where overall higher accuracy rates were achieved with no decrease in specificity. [OTH-004 Table 1 not included].
Conclusions: the use of a bespoke BLI classification system with adequate training can significantly improve the sensitivity and NPV of adenoma detection in both experienced and non-experienced endoscopists thereby enabling the full potential of this novel imaging technology to be realised.
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e-pub ahead of print date: 8 June 2018
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Local EPrints ID: 477977
URI: http://eprints.soton.ac.uk/id/eprint/477977
ISSN: 1468-3288
PURE UUID: 2a0e9771-0bd7-42fd-9218-d6aa26ec62c1
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Date deposited: 19 Jun 2023 16:37
Last modified: 21 Sep 2024 02:15
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Contributors
Author:
Sharmila Subramaniam
Author:
Asma Alkandari
Author:
Kesavan Kandiah
Author:
Rebecca Smith
Author:
Matthew Stammers
Author:
Sreedhari Thayalasekaran
Author:
Patrick Aepli
Author:
Bu’ Hayee
Author:
Erik Schoon
Author:
Milan Stefanovic
Author:
Pradeep Bhandari
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