Artificial intelligence in gastrointestinal endoscopy: general overview

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Abstract

Artificial intelligence (AI) is now a trendy subject in clinical medicine and especially in gastrointestinal (GI) endoscopy. AI has the potential to improve the quality of GI endoscopy at all levels. It will compensate for humans’ errors and limited capabilities by bringing more accuracy, consistency, and higher speed, making endoscopic procedures more efficient and of higher quality. AI showed great results in diagnostic and therapeutic endoscopy in all parts of the GI tract. More studies are still needed before the introduction of this new technology in our daily practice and clinical guidelines. Furthermore, ethical clearance and new legislations might be needed. In conclusion, the introduction of AI will be a big breakthrough in the field of GI endoscopy in the upcoming years. It has the potential to bring major improvements to GI endoscopy at all levels.

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Most cited references 44

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Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images.

Toshiaki Hirasawa, Kazuharu Aoyama, Tetsuya Tanimoto … (2018)

Image recognition using artificial intelligence with deep learning through convolutional neural networks (CNNs) has dramatically improved and been increasingly applied to medical fields for diagnostic imaging. We developed a CNN that can automatically detect gastric cancer in endoscopic images.

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ACG Clinical Guideline: Diagnosis and Management of Small Bowel Bleeding.

Lauren Gerson, Jeff L Fidler, David R Cave … (2015)

Bleeding from the small intestine remains a relatively uncommon event, accounting for ~5-10% of all patients presenting with gastrointestinal (GI) bleeding. Given advances in small bowel imaging with video capsule endoscopy (VCE), deep enteroscopy, and radiographic imaging, the cause of bleeding in the small bowel can now be identified in most patients. The term small bowel bleeding is therefore proposed as a replacement for the previous classification of obscure GI bleeding (OGIB). We recommend that the term OGIB should be reserved for patients in whom a source of bleeding cannot be identified anywhere in the GI tract. A source of small bowel bleeding should be considered in patients with GI bleeding after performance of a normal upper and lower endoscopic examination. Second-look examinations using upper endoscopy, push enteroscopy, and/or colonoscopy can be performed if indicated before small bowel evaluation. VCE should be considered a first-line procedure for small bowel investigation. Any method of deep enteroscopy can be used when endoscopic evaluation and therapy are required. VCE should be performed before deep enteroscopy if there is no contraindication. Computed tomographic enterography should be performed in patients with suspected obstruction before VCE or after negative VCE examinations. When there is acute overt hemorrhage in the unstable patient, angiography should be performed emergently. In patients with occult hemorrhage or stable patients with active overt bleeding, multiphasic computed tomography should be performed after VCE or CTE to identify the source of bleeding and to guide further management. If a source of bleeding is identified in the small bowel that is associated with significant ongoing anemia and/or active bleeding, the patient should be managed with endoscopic therapy. Conservative management is recommended for patients without a source found after small bowel investigation, whereas repeat diagnostic investigations are recommended for patients with initial negative small bowel evaluations and ongoing overt or occult bleeding.

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Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy

Pu Wang, Xiao Xiao, Jeremy Glissen Brown … (2018)

The detection and removal of precancerous polyps via colonoscopy is the gold standard for the prevention of colon cancer. However, the detection rate of adenomatous polyps can vary significantly among endoscopists. Here, we show that a machine-learning algorithm can detect polyps in clinical colonoscopies, in real time and with high sensitivity and specificity. We developed the deep-learning algorithm by using data from 1,290 patients, and validated it on newly collected 27,113 colonoscopy images from 1,138 patients with at least one detected polyp (per-image-sensitivity, 94.38%; per-image-specificity, 95.92%; area under the receiver operating characteristic curve, 0.984), on a public database of 612 polyp-containing images (per-image-sensitivity, 88.24%), on 138 colonoscopy videos with histologically confirmed polyps (per-image-sensitivity of 91.64%; per-polyp-sensitivity, 100%), and on 54 unaltered full-range colonoscopy videos without polyps (per-image-specificity, 95.40%). By using a multi-threaded processing system, the algorithm can process at least 25 frames per second with a latency of 76.80 ± 5.60 ms in real-time video analysis. The software may aid endoscopists while performing colonoscopies, and help assess differences in polyp and adenoma detection performance among endoscopists.

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Author and article information

Journal

Journal ID (nlm-ta): Chin Med J (Engl)

Journal ID (iso-abbrev): Chin. Med. J

Journal ID (publisher-id): CM9

Title: Chinese Medical Journal

Publisher: Wolters Kluwer Health

ISSN (Print): 0366-6999

ISSN (Electronic): 2542-5641

Publication date (Print): 5 February 2020

Publication date (Electronic): 05 February 2020

Volume: 133

Issue: 3

Pages: 326-334

Affiliations

Department of Gastroenterology and Digestive Endoscopy, Arnault Tzanck Institute, Saint-Laurent du Var 06700, France.

Author notes

Correspondence to: Dr. Ahmad El Hajjar, Department of Gastroenterology and Digestive Endoscopy, Arnault Tzanck Institute, Saint-Laurent du Var 06700, France E-Mail: ahmad-hajjar@ 123456live.com

Article

Publisher ID: CMJ-2019-1624 Accession ID: 00010

DOI: 10.1097/CM9.0000000000000623

PMC ID: 7004609

PubMed ID: 31929362

SO-VID: aa299888-75f0-4c19-b47e-ee40e97506d4

License:

This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0

History

Date received : 20 September 2019

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Keywords: artificial intelligence,computer-assisted diagnosis,deep learning,gastrointestinal endoscopy

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Keywords: artificial intelligence, computer-assisted diagnosis, deep learning, gastrointestinal endoscopy

Artificial intelligence in gastrointestinal endoscopy: general overview

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Artificial Intelligence in Medicine

Most cited references 44

Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images.

ACG Clinical Guideline: Diagnosis and Management of Small Bowel Bleeding.

Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy

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