Literature DB >> 33222330

Artificial intelligence for cancer detection of the upper gastrointestinal tract.

Hideo Suzuki1, Tokai Yoshitaka2, Toshiyuki Yoshio2, Tomohiro Tada3,4,5.   

Abstract

In recent years, artificial intelligence (AI) has been found to be useful to physicians in the field of image recognition due to three elements: deep learning (that is, CNN, convolutional neural network), a high-performance computer, and a large amount of digitized data. In the field of gastrointestinal endoscopy, Japanese endoscopists have produced the world's first achievements of CNN-based AI system for detecting gastric and esophageal cancers. This study reviews papers on CNN-based AI for gastrointestinal cancers, and discusses the future of this technology in clinical practice. Employing AI-based endoscopes would enable early cancer detection. The better diagnostic abilities of AI technology may be beneficial in early gastrointestinal cancers in which endoscopists have variable diagnostic abilities and accuracy. AI coupled with the expertise of endoscopists would increase the accuracy of endoscopic diagnosis.
© 2020 Japan Gastroenterological Endoscopy Society.

Entities:  

Keywords:  artificial intelligence; esophageal squamous cell carcinoma; gastric cancer; helicobacter pylori; pharyngeal cancer

Mesh:

Year:  2020        PMID: 33222330     DOI: 10.1111/den.13897

Source DB:  PubMed          Journal:  Dig Endosc        ISSN: 0915-5635            Impact factor:   7.559


  2 in total

1.  Real-time semantic segmentation of gastric intestinal metaplasia using a deep learning approach.

Authors:  Vitchaya Siripoppohn; Rapat Pittayanon; Kasenee Tiankanon; Natee Faknak; Anapat Sanpavat; Naruemon Klaikaew; Peerapon Vateekul; Rungsun Rerknimitr
Journal:  Clin Endosc       Date:  2022-05-09

2.  Utilization of Ultrasonic Image Characteristics Combined with Endoscopic Detection on the Basis of Artificial Intelligence Algorithm in Diagnosis of Early Upper Gastrointestinal Cancer.

Authors:  Liang Wang; Hui Song; Ming Wang; Hui Wang; Ran Ge; Yan Shen; Yongli Yu
Journal:  J Healthc Eng       Date:  2021-11-29       Impact factor: 2.682

  2 in total

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