Artificial intelligence in luminal endoscopy
Artificial intelligence is a strong focus of interest for global health development. Diagnostic endoscopy is an attractive substrate for artificial intelligence with a real potential to improve patient care through standardisation of endoscopic diagnosis and to serve as an adjunct to enhanced imagin...
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2020-06-01
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Series: | Therapeutic Advances in Gastrointestinal Endoscopy |
Online Access: | https://doi.org/10.1177/2631774520935220 |
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doaj-f55cb791b14d4c0aa5339529c570024c2020-11-25T03:36:29ZengSAGE PublishingTherapeutic Advances in Gastrointestinal Endoscopy2631-77452020-06-011310.1177/2631774520935220Artificial intelligence in luminal endoscopyShraddha GulatiAndrew EmmanuelMehul PatelSophie WilliamsAmyn HajiBu’Hussain HayeeHelmut NeumannArtificial intelligence is a strong focus of interest for global health development. Diagnostic endoscopy is an attractive substrate for artificial intelligence with a real potential to improve patient care through standardisation of endoscopic diagnosis and to serve as an adjunct to enhanced imaging diagnosis. The possibility to amass large data to refine algorithms makes adoption of artificial intelligence into global practice a potential reality. Initial studies in luminal endoscopy involve machine learning and are retrospective. Improvement in diagnostic performance is appreciable through the adoption of deep learning. Research foci in the upper gastrointestinal tract include the diagnosis of neoplasia, including Barrett’s, squamous cell and gastric where prospective and real-time artificial intelligence studies have been completed demonstrating a benefit of artificial intelligence–augmented endoscopy. Deep learning applied to small bowel capsule endoscopy also appears to enhance pathology detection and reduce capsule reading time. Prospective evaluation including the first randomised trial has been performed in the colon, demonstrating improved polyp and adenoma detection rates; however, these appear to be relevant to small polyps. There are potential additional roles of artificial intelligence relevant to improving the quality of endoscopic examinations, training and triaging of referrals. Further large-scale, multicentre and cross-platform validation studies are required for the robust incorporation of artificial intelligence–augmented diagnostic luminal endoscopy into our routine clinical practice.https://doi.org/10.1177/2631774520935220 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shraddha Gulati Andrew Emmanuel Mehul Patel Sophie Williams Amyn Haji Bu’Hussain Hayee Helmut Neumann |
spellingShingle |
Shraddha Gulati Andrew Emmanuel Mehul Patel Sophie Williams Amyn Haji Bu’Hussain Hayee Helmut Neumann Artificial intelligence in luminal endoscopy Therapeutic Advances in Gastrointestinal Endoscopy |
author_facet |
Shraddha Gulati Andrew Emmanuel Mehul Patel Sophie Williams Amyn Haji Bu’Hussain Hayee Helmut Neumann |
author_sort |
Shraddha Gulati |
title |
Artificial intelligence in luminal endoscopy |
title_short |
Artificial intelligence in luminal endoscopy |
title_full |
Artificial intelligence in luminal endoscopy |
title_fullStr |
Artificial intelligence in luminal endoscopy |
title_full_unstemmed |
Artificial intelligence in luminal endoscopy |
title_sort |
artificial intelligence in luminal endoscopy |
publisher |
SAGE Publishing |
series |
Therapeutic Advances in Gastrointestinal Endoscopy |
issn |
2631-7745 |
publishDate |
2020-06-01 |
description |
Artificial intelligence is a strong focus of interest for global health development. Diagnostic endoscopy is an attractive substrate for artificial intelligence with a real potential to improve patient care through standardisation of endoscopic diagnosis and to serve as an adjunct to enhanced imaging diagnosis. The possibility to amass large data to refine algorithms makes adoption of artificial intelligence into global practice a potential reality. Initial studies in luminal endoscopy involve machine learning and are retrospective. Improvement in diagnostic performance is appreciable through the adoption of deep learning. Research foci in the upper gastrointestinal tract include the diagnosis of neoplasia, including Barrett’s, squamous cell and gastric where prospective and real-time artificial intelligence studies have been completed demonstrating a benefit of artificial intelligence–augmented endoscopy. Deep learning applied to small bowel capsule endoscopy also appears to enhance pathology detection and reduce capsule reading time. Prospective evaluation including the first randomised trial has been performed in the colon, demonstrating improved polyp and adenoma detection rates; however, these appear to be relevant to small polyps. There are potential additional roles of artificial intelligence relevant to improving the quality of endoscopic examinations, training and triaging of referrals. Further large-scale, multicentre and cross-platform validation studies are required for the robust incorporation of artificial intelligence–augmented diagnostic luminal endoscopy into our routine clinical practice. |
url |
https://doi.org/10.1177/2631774520935220 |
work_keys_str_mv |
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