Literature DB >> 32209800

[Deep Learning in Upper Gastrointestinal Disorders: Status and Future Perspectives].

Chang Seok Bang1.   

Abstract

Artificial intelligence using deep learning has been applied to gastrointestinal disorders for the detection, classification, and delineation of various lesion images. With the accumulation of enormous medical records, the evolution of computation power with graphic processing units, and the widespread use of open-source libraries in large-scale machine learning processes, medical artificial intelligence is overcoming its traditional limitations. This paper explains the basic concepts of deep learning model establishment and summarizes previous studies on upper gastrointestinal disorders. The limitations and perspectives on future development are also discussed.

Entities:  

Keywords:  Artificial intelligence; Deep learning; Endoscopy; Gastroenterology; Neural networks, computer

Mesh:

Year:  2020        PMID: 32209800     DOI: 10.4166/kjg.2020.75.3.120

Source DB:  PubMed          Journal:  Korean J Gastroenterol        ISSN: 1598-9992


  6 in total

Review 1.  Computer-Aided Diagnosis of Gastrointestinal Protruded Lesions Using Wireless Capsule Endoscopy: A Systematic Review and Diagnostic Test Accuracy Meta-Analysis.

Authors:  Hye Jin Kim; Eun Jeong Gong; Chang Seok Bang; Jae Jun Lee; Ki Tae Suk; Gwang Ho Baik
Journal:  J Pers Med       Date:  2022-04-17

2.  No-Code Platform-Based Deep-Learning Models for Prediction of Colorectal Polyp Histology from White-Light Endoscopy Images: Development and Performance Verification.

Authors:  Eun Jeong Gong; Chang Seok Bang; Jae Jun Lee; Seung In Seo; Young Joo Yang; Gwang Ho Baik; Jong Wook Kim
Journal:  J Pers Med       Date:  2022-06-12

Review 3.  Computer-Aided Diagnosis of Gastrointestinal Ulcer and Hemorrhage Using Wireless Capsule Endoscopy: Systematic Review and Diagnostic Test Accuracy Meta-analysis.

Authors:  Chang Seok Bang; Jae Jun Lee; Gwang Ho Baik
Journal:  J Med Internet Res       Date:  2021-12-14       Impact factor: 5.428

4.  Deep-Learning for the Diagnosis of Esophageal Cancers and Precursor Lesions in Endoscopic Images: A Model Establishment and Nationwide Multicenter Performance Verification Study.

Authors:  Eun Jeong Gong; Chang Seok Bang; Kyoungwon Jung; Su Jin Kim; Jong Wook Kim; Seung In Seo; Uhmyung Lee; You Bin Maeng; Ye Ji Lee; Jae Ick Lee; Gwang Ho Baik; Jae Jun Lee
Journal:  J Pers Med       Date:  2022-06-27

5.  Prediction of Submucosal Invasion for Gastric Neoplasms in Endoscopic Images Using Deep-Learning.

Authors:  Bum-Joo Cho; Chang Seok Bang; Jae Jun Lee; Chang Won Seo; Ju Han Kim
Journal:  J Clin Med       Date:  2020-06-15       Impact factor: 4.241

Review 6.  Usefulness of artificial intelligence in gastric neoplasms.

Authors:  Ji Hyun Kim; Seung-Joo Nam; Sung Chul Park
Journal:  World J Gastroenterol       Date:  2021-06-28       Impact factor: 5.742

  6 in total

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