Literature DB >> 34035738

Artificial Intelligence for Understanding Imaging, Text, and Data in Gastroenterology.

Ryan W Stidham1,2,3.   

Abstract

Artificial intelligence (AI) could change the practice of gastroenterology through its ability to both acquire and analyze information with speed, reproducibility, and, potentially, insight that may exceed that of human medical specialists. AI is powered by computational methods that allow machines to replicate clinical pattern recognition used by gastroenterology specialists to interpret endoscopic or cross-sectional images; understand the meaning and intent of medical documents; and merge different types of data to infer a diagnosis, prognosis, or expected outcome. Ongoing research is studying the use of AI for automated interpretation of text from colonoscopy and clinical documents for improved quality and patient phenotyping as well as enhanced detection and descriptions of polyps and other endoscopic lesions, and for predicting the probability of future therapeutic response early in a treatment course. This article introduces emerging technologies of natural language processing, machine vision, and machine learning for data analytics, and describes current and future applications in gastroenterology.
Copyright © 2020, Gastro-Hep Communications, Inc.

Entities:  

Keywords:  Artificial intelligence, machine learning; computer vision; computer-aided diagnosis; gastroenterology; image classification; image segmentation; natural language processing

Year:  2020        PMID: 34035738      PMCID: PMC8132644     

Source DB:  PubMed          Journal:  Gastroenterol Hepatol (N Y)        ISSN: 1554-7914


  44 in total

1.  Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study.

Authors:  Pu Wang; Xiaogang Liu; Tyler M Berzin; Jeremy R Glissen Brown; Peixi Liu; Chao Zhou; Lei Lei; Liangping Li; Zhenzhen Guo; Shan Lei; Fei Xiong; Han Wang; Yan Song; Yan Pan; Guanyu Zhou
Journal:  Lancet Gastroenterol Hepatol       Date:  2020-01-22

2.  Computer-aided detection of early neoplastic lesions in Barrett's esophagus.

Authors:  Fons van der Sommen; Svitlana Zinger; Wouter L Curvers; Raf Bisschops; Oliver Pech; Bas L A M Weusten; Jacques J G H M Bergman; Peter H N de With; Erik J Schoon
Journal:  Endoscopy       Date:  2016-04-21       Impact factor: 10.093

3.  Machine learning model to predict recurrent ulcer bleeding in patients with history of idiopathic gastroduodenal ulcer bleeding.

Authors:  Grace Lai-Hung Wong; Andy Jinhua Ma; Huiqi Deng; Jessica Yuet-Ling Ching; Vincent Wai-Sun Wong; Yee-Kit Tse; Terry Cheuk-Fung Yip; Louis Ho-Shing Lau; Henry Hin-Wai Liu; Chi-Man Leung; Steven Woon-Choy Tsang; Chun-Wing Chan; James Yun-Wong Lau; Pong-Chi Yuen; Francis Ka-Leung Chan
Journal:  Aliment Pharmacol Ther       Date:  2019-02-13       Impact factor: 8.171

4.  Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy.

Authors:  Gregor Urban; Priyam Tripathi; Talal Alkayali; Mohit Mittal; Farid Jalali; William Karnes; Pierre Baldi
Journal:  Gastroenterology       Date:  2018-06-18       Impact factor: 22.682

5.  Development and Validation of a Deep Neural Network for Accurate Evaluation of Endoscopic Images From Patients With Ulcerative Colitis.

Authors:  Kento Takenaka; Kazuo Ohtsuka; Toshimitsu Fujii; Mariko Negi; Kohei Suzuki; Hiromichi Shimizu; Shiori Oshima; Shintaro Akiyama; Maiko Motobayashi; Masakazu Nagahori; Eiko Saito; Katsuyoshi Matsuoka; Mamoru Watanabe
Journal:  Gastroenterology       Date:  2020-02-12       Impact factor: 22.682

6.  CLAMP - a toolkit for efficiently building customized clinical natural language processing pipelines.

Authors:  Ergin Soysal; Jingqi Wang; Min Jiang; Yonghui Wu; Serguei Pakhomov; Hongfang Liu; Hua Xu
Journal:  J Am Med Inform Assoc       Date:  2018-03-01       Impact factor: 4.497

7.  Feature engineering for sentiment analysis in e-health forums.

Authors:  Jorge Carrillo-de-Albornoz; Javier Rodríguez Vidal; Laura Plaza
Journal:  PLoS One       Date:  2018-11-29       Impact factor: 3.240

8.  Development and Validation of Machine Learning Models in Prediction of Remission in Patients With Moderate to Severe Crohn Disease.

Authors:  Akbar K Waljee; Beth I Wallace; Shirley Cohen-Mekelburg; Yumu Liu; Boang Liu; Kay Sauder; Ryan W Stidham; Ji Zhu; Peter D R Higgins
Journal:  JAMA Netw Open       Date:  2019-05-03

9.  Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study.

Authors:  Pu Wang; Tyler M Berzin; Jeremy Romek Glissen Brown; Shishira Bharadwaj; Aymeric Becq; Xun Xiao; Peixi Liu; Liangping Li; Yan Song; Di Zhang; Yi Li; Guangre Xu; Mengtian Tu; Xiaogang Liu
Journal:  Gut       Date:  2019-02-27       Impact factor: 23.059

10.  Real-time use of artificial intelligence in the evaluation of cancer in Barrett's oesophagus.

Authors:  Alanna Ebigbo; Robert Mendel; Andreas Probst; Johannes Manzeneder; Friederike Prinz; Luis A de Souza; Joao Papa; Christoph Palm; Helmut Messmann
Journal:  Gut       Date:  2019-09-20       Impact factor: 23.059

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  1 in total

1.  Integrated multimodal artificial intelligence framework for healthcare applications.

Authors:  Luis R Soenksen; Yu Ma; Cynthia Zeng; Leonard Boussioux; Kimberly Villalobos Carballo; Liangyuan Na; Holly M Wiberg; Michael L Li; Ignacio Fuentes; Dimitris Bertsimas
Journal:  NPJ Digit Med       Date:  2022-09-20
  1 in total

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