Machine Learning and Radiomics: Changing the Horizon of Crohn’s Disease Assessment
Author:
Affiliation:
1. Division of Gastroenterology and Hepatology, Mayo Clinic , Rochester, MN , USA
Publisher
Oxford University Press (OUP)
Subject
Gastroenterology,Immunology and Allergy
Link
https://academic.oup.com/ibdjournal/advance-article-pdf/doi/10.1093/ibd/izad284/53843346/izad284.pdf
Reference22 articles.
1. Worldwide incidence and prevalence of inflammatory bowel disease in the 21st century: a systematic review of population-based studies;Ng;Lancet.,2017
2. ECCO-ESGAR Guideline for Diagnostic Assessment in IBD Part 1: Initial diagnosis, monitoring of known IBD, detection of complications;Maaser;J Crohns Colitis,2019
3. Computed tomography enterography for evaluation of inflammatory bowel disease;Park;Clin Endosc,2013
4. Curved planar reformatting and convolutional neural network-based segmentation of the small bowel for visualization and quantitative assessment of pediatric Crohn’s disease from MRI;Lamash;J Magn Reson Imaging.,2019
5. Automatic detection and segmentation of Crohn’s disease tissues from abdominal MRI;Mahapatra;IEEE Trans Med Imaging.,2013
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