Artificial Intelligence in IBD: How Will It Change Patient Management?
Author:
Publisher
Springer Science and Business Media LLC
Subject
Gastroenterology
Link
https://link.springer.com/content/pdf/10.1007/s11938-023-00437-x.pdf
Reference47 articles.
1. Puylaert CAJ, et al. Comparison of MRI activity scoring systems and features for the terminal ileum in patients with Crohn disease. Am J Roentgenol. 2019;212:W25–31.
2. Lamash Y, Kurugol S, Warfield SK. Semi-automated extraction of Crohn’s disease MR imaging markers using a 3D residual CNN with distance prior. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support. 2018;11045:218–26.
3. Enchakalody BE. et al. Machine learning methods to predict presence of intestine damage in patients with Crohn’s disease. in Medical imaging 2020: computer-aided diagnosis vol. 11314 1131436 (Medical Imaging 2020: Computer-Aided Diagnosis, 2020).
4. • Stidham RW et al. Assessing small bowel stricturing and morphology in Crohn’s disease using semi-automated image analysis. Inflamm Bowel Dis 11, 274 (2019). Exemplar methodology for cross sectional imagine segmentation of enterography in Crohn’s disease. Demonstrates ability to automatically collect detailed bowel measurements related to Crohn’s disease severity and phenotype with similar accuracy as IBD-specialist radiologists.
5. Carter D et al. Automatized detection of Crohn’s disease in intestinal ultrasound using convolutional neural network. Inflamm. Bowel Dis. 2023;doi:10.1093/ibd/izad014.
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