Artificial intelligence in gastrointestinal endoscopy

Carneiro, Gustavo

doi:10.1111/jgh.15344

Cited by 11 publications

(11 citation statements)

References 12 publications

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“…24,26 The current development of AI in endoscopy mainly focuses on disease detection or classification. 27 There are few studies for using AI for endoscopy quality control for such as photodocumentation as in our study. Takiyama et al 28 from Japan were the first to report deep convolutional neural networks for the anatomical classification of still images into four anatomy sites.…”

Section: Discussionmentioning

confidence: 80%

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Upper endoscopy photodocumentation quality evaluation with novel deep learning system

Chang

Yen

et al. 2021

Digestive Endoscopy

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Objectives Visualization and photodocumentation during endoscopy procedures are suggested to be one indicator for endoscopy performance quality. However, this indicator is difficult to measure and audit manually in clinical practice. Artificial intelligence (AI) is an emerging technology that may solve this problem. Methods A deep learning model with an accuracy of 96.64% was developed from 15,305 images for upper endoscopy anatomy classification in the unit. Endoscopy images for asymptomatic patients receiving screening endoscopy were evaluated with this model to assess the completeness of photodocumentation rate. Results A total of 15,723 images from 472 upper endoscopies performed by 12 endoscopists were enrolled. The complete photodocumentation rate from the pharynx to the duodenum was 53.8% and from the esophagus to the duodenum was 78.0% in this study. Endoscopists with a higher adenoma detection rate had a higher complete examination rate from the pharynx to duodenum (60.0% vs. 38.7%, P < 0.0001) and from esophagus to duodenum (83.0% vs. 65.7%, P < 0.0001) compared with endoscopists with lower adenoma detection rate. The pharynx, gastric angle, gastric retroflex view, gastric antrum, and the first portion of duodenum are likely to be missed by endoscopists with lower adenoma detection rates. Conclusions We report the use of a deep learning model to audit endoscopy photodocumentation quality in our unit. Endoscopists with better performance in colonoscopy had a better performance for this quality indicator. The use of such an AI system may help the endoscopy unit audit endoscopy performance.

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Section: Discussionmentioning

confidence: 80%

“…AI is a promising tool to improve the care in medicine 24,26 . The current development of AI in endoscopy mainly focuses on disease detection or classification 27 . There are few studies for using AI for endoscopy quality control for such as photodocumentation as in our study.…”

Section: Discussionmentioning

confidence: 88%

Upper endoscopy photodocumentation quality evaluation with novel deep learning system

Chang

Yen

et al. 2021

Digestive Endoscopy

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“…AI has also been shown to be useful to differentiate neoplastic from hyperplastic polyps. 4,5 However, such AI systems and outcome data are lacking in the context of IBD.…”

Section: Artificial Intelligence For the Diagnosis Of Dysplasia In In...mentioning

confidence: 99%

“…Artificial intelligence (AI)‐assisted colonoscopy is now commercially available and has been demonstrated to improve detection rates of SA when compared to IEE in non‐IBD patients. AI has also been shown to be useful to differentiate neoplastic from hyperplastic polyps 4,5 . However, such AI systems and outcome data are lacking in the context of IBD.…”

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confidence: 99%

Artificial intelligence for the diagnosis of dysplasia in inflammatory bowel diseases

Wang

2022

J of Gastro and Hepatol

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“…Recent clinical research demonstrated that even experienced physicians could overlook polyps during gastroscopy due to work overload 9 . As a result, physicians face difficulties in making an early diagnosis of polyps via gastroscopy 10 . During gastroscopy, the overlooked polyps could lead to a late diagnosis of gastric cancer.…”

Section: Introductionmentioning

confidence: 99%

An automatic gastric polyp detection technique using deep learning

Mushtaq

Madni

Janjua

et al. 2023

Int J Imaging Syst Tech

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Over the last few years, researchers have focused on computer-aided polyp detection in gastroscopy. Deep learning (DL) has shown great promise for polyps' identification. The most exceptional contribution of DL methods in gastroenterology is their ability to identify polyps quickly and accurately using convolution neural network. Nonetheless, despite significant advancements, automatic detection of small polyps remains a challenging and complex task.

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Artificial intelligence in gastrointestinal endoscopy

Cited by 11 publications

References 12 publications

Upper endoscopy photodocumentation quality evaluation with novel deep learning system

Upper endoscopy photodocumentation quality evaluation with novel deep learning system

Artificial intelligence for the diagnosis of dysplasia in inflammatory bowel diseases

An automatic gastric polyp detection technique using deep learning

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