Application and Prospect of Artificial Intelligence in Digestive Endoscopy

ABSTRACTObjectiveArtificial intelligence (AI) has undeniable values in detection, characterization, and monitoring of tumors during cancer imaging. However, major AI explorations in digestive endoscopy have not been systematically planned, and more important, most AI productions are based on Single-center Studies (ScSs). ScSs result in data scarcity, redundancy as well as island effects, which leads to some limitations in applying it on endoscopy. We investigate the disadvantages of picture processing which may effect the AI detection, and make improvements in AI detection and image recognition accuracy.DesignCurrent investigation aggregates a total of 2,500 gastroenteroscopy samples from various hospitals in multiple regions and carries out deep learning.ResultsIt is found that factors inconducive to AI recognition are common such as: (a) the gastrointestinal tract is not cleaned up completely; (b) shooting angle (from left to right and the top of polyp are unexposed clearly), shooting distance (too close or too far to shoot causes the lump to be unclear), shooting light (insufficient light source or overexposed light source in mass) and unstable shooting lead to poor quality of pictures.ConclusionWe set standards for a multicenter cooperation involving three-level medical institutions from the provincial, municipal and county to improve the recognition accuracy as well as the diagnosis and treatment efficiency meanwhile.

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Research Progress of Artificial Intelligence Technology in Diagnosis and Quality Control of Digestive Endoscopy

Advances in Clinical Medicine ◽

10.12677/acm.2020.1011414 ◽

2020 ◽

Vol 10 (11) ◽

pp. 2720-2726

Author(s):

雯俏华

Keyword(s):

Artificial Intelligence ◽

Quality Control ◽

Research Progress ◽

Digestive Endoscopy ◽

Artificial Intelligence Technology

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The scientific progress and prospects of artificial intelligence in digestive endoscopy: a comprehensive bibliometric analysis (Preprint)

10.2196/preprints.29882 ◽

2021 ◽

Author(s):

Peiling Gan ◽

Shu Huang ◽

Xiao Pan ◽

Shali Tan ◽

Chunyu Zhong ◽

...

Keyword(s):

Artificial Intelligence ◽

Gastrointestinal Endoscopy ◽

Web Of Science ◽

Research Output ◽

Scientific Progress ◽

Digestive Endoscopy ◽

Asian Countries ◽

Microsoft Excel ◽

Number Of Publications ◽

Research Frontiers

UNSTRUCTURED Abstract Background: A growing number of studies have reported artificial intelligence (AI) has been developed for diagnosis and outcome prediction in clinical practice. Furthermore, AI in digestive endoscopy has attracted much attention, which has shown promising and stimulating results. Our study aimed to visualize the articles to determine the trends and hotspots of AI in digestive endoscopy. Methods: Publications on AI in digestive endoscopy research were retrieved from the Web of Science Core Collection (WoSCC) on March 14, 2021. Microsoft Excel 2016, VOSviewer 1.6.11.0, and CiteSpace V were used to assess and plot the research output. Results: The analytic research was based on original articles and reviews. A total of 121 records of AI research in digestive endoscopy published from 2017 to 2021 were retrieved. The citation number for these articles ranged from 0 to 142. The number of published articles increased 68-fold just from 2017 to 2020. All publications were distributed among 31 countries and 296 institutions. Asian countries had the most publications in this field (80.17%). Among the 31 countries, China and Japan were consistently the leading driving force and contributed mostly (31.40% and 28.93%, respectively), with a strong academic reputation in this area. Tada Tomohiro distributed the most related articles (13.22%) and was cited the most frequently. Gastrointestinal endoscopy published the largest number of publications (14.88%), and 4 of the top 10 cited references were in this leading journal. “Barrett’s esophagus” was the leading research hotspot. The keywords “classification,” “polyps,” “risk,” “histology,” and “resection” appeared most recently as research frontiers. Conclusions: Our study provides a systematic elaboration for researchers to obtain a good comprehension of AI development in digestive endoscopy.

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