Literature DB >> 33624888

Clinical applications of artificial intelligence and machine learning-based methods in inflammatory bowel disease.

Shirley Cohen-Mekelburg1,2, Sameer Berry2, Ryan W Stidham2,3,4, Ji Zhu4,5, Akbar K Waljee1,2,4.   

Abstract

Our objective was to review and exemplify how selected applications of artificial intelligence (AI) might facilitate and improve inflammatory bowel disease (IBD) care and to identify gaps for future work in this field. IBD is highly complex and associated with significant variation in care and outcomes. The application of AI to IBD has the potential to reduce variation in healthcare delivery and improve quality of care. AI refers to the ability of machines to mimic human intelligence. The range of AI's ability to perform tasks that would normally require human intelligence varies from prediction to complex decision-making that more closely resembles human thought. Clinical applications of AI have been applied to study pathogenesis, diagnosis, and patient prognosis in IBD. Despite these advancements, AI in IBD is in its early development and has tremendous potential to transform future care.
© 2021 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

Entities:  

Keywords:  Gastroenterology; Gastroenterology, IBD: clinical trials; Gastroenterology, IBD: genetics; Gastroenterology, IBD: pre-clinical treatment and novel therapies; Gastroenterology, screening and diagnosis

Mesh:

Year:  2021        PMID: 33624888      PMCID: PMC8917815          DOI: 10.1111/jgh.15405

Source DB:  PubMed          Journal:  J Gastroenterol Hepatol        ISSN: 0815-9319            Impact factor:   4.029


  30 in total

1.  From Local Explanations to Global Understanding with Explainable AI for Trees.

Authors:  Scott M Lundberg; Gabriel Erion; Hugh Chen; Alex DeGrave; Jordan M Prutkin; Bala Nair; Ronit Katz; Jonathan Himmelfarb; Nisha Bansal; Su-In Lee
Journal:  Nat Mach Intell       Date:  2020-01-17

Review 2.  Quality Improvement Initiatives in Inflammatory Bowel Disease.

Authors:  Sameer K Berry; Corey A Siegel; Gil Y Melmed
Journal:  Curr Gastroenterol Rep       Date:  2017-08

Review 3.  Big data in IBD: a look into the future.

Authors:  Pablo Olivera; Silvio Danese; Nicolas Jay; Gioacchino Natoli; Laurent Peyrin-Biroulet
Journal:  Nat Rev Gastroenterol Hepatol       Date:  2019-05       Impact factor: 46.802

4.  Fully automated endoscopic disease activity assessment in ulcerative colitis.

Authors:  Heming Yao; Kayvan Najarian; Jonathan Gryak; Shrinivas Bishu; Michael D Rice; Akbar K Waljee; H Jeffrey Wilkins; Ryan W Stidham
Journal:  Gastrointest Endosc       Date:  2020-08-15       Impact factor: 9.427

5.  Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images.

Authors:  Toshiaki Hirasawa; Kazuharu Aoyama; Tetsuya Tanimoto; Soichiro Ishihara; Satoki Shichijo; Tsuyoshi Ozawa; Tatsuya Ohnishi; Mitsuhiro Fujishiro; Keigo Matsuo; Junko Fujisaki; Tomohiro Tada
Journal:  Gastric Cancer       Date:  2018-01-15       Impact factor: 7.370

6.  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

7.  Evaluation of a convolutional neural network for ovarian tumor differentiation based on magnetic resonance imaging.

Authors:  Robin Wang; Yeyu Cai; Iris K Lee; Rong Hu; Subhanik Purkayastha; Ian Pan; Thomas Yi; Thi My Linh Tran; Shaolei Lu; Tao Liu; Ken Chang; Raymond Y Huang; Paul J Zhang; Zishu Zhang; Enhua Xiao; Jing Wu; Harrison X Bai
Journal:  Eur Radiol       Date:  2020-10-14       Impact factor: 5.315

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.  Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data.

Authors:  Alberto Romagnoni; Simon Jégou; Kristel Van Steen; Gilles Wainrib; Jean-Pierre Hugot
Journal:  Sci Rep       Date:  2019-07-17       Impact factor: 4.379

Review 10.  Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension.

Authors:  Samantha Cruz Rivera; Xiaoxuan Liu; An-Wen Chan; Alastair K Denniston; Melanie J Calvert
Journal:  Nat Med       Date:  2020-09-09       Impact factor: 53.440

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  2 in total

Review 1.  Highlighting the Undetectable - Fluorescence Molecular Imaging in Gastrointestinal Endoscopy.

Authors:  Judith A Stibbe; Petra Hoogland; Friso B Achterberg; Derek R Holman; Raoul S Sojwal; Jacobus Burggraaf; Alexander L Vahrmeijer; Wouter B Nagengast; Stephan Rogalla
Journal:  Mol Imaging Biol       Date:  2022-06-28       Impact factor: 3.488

2.  Application of Artificial Intelligence to Clinical Practice in Inflammatory Bowel Disease - What the Clinician Needs to Know.

Authors:  David Chen; Clifton Fulmer; Ilyssa O Gordon; Sana Syed; Ryan W Stidham; Niels Vande Casteele; Yi Qin; Katherine Falloon; Benjamin L Cohen; Robert Wyllie; Florian Rieder
Journal:  J Crohns Colitis       Date:  2022-03-14       Impact factor: 10.020

  2 in total

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