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. Author manuscript; available in PMC: 2022 Mar 12.
Published in final edited form as: J Gastroenterol Hepatol. 2021 Feb;36(2):279–285. doi: 10.1111/jgh.15405

Figure 2.

Figure 2

Conceptual application of AI for cross sectional imaging in IBD: Machine learning methods offer the potential for more than replicating expert interpretation, but enhanced disease quantification. CT or MR-enterography can be segmented into anatomic regions relevant for IBD or other disease using artificial intelligence techniques. In this conceptual example, regions of diseased bowel can be predicted using extracted measures of bowel wall thickness, lumen diameter, and total bowel dilation. Of more value is the opportunity to better quantify intestinal disease using direct area and volume measurements, which are expected to aid personalization of care in IBD.17 Outerwall;,Lumen;,Thickness;, Disease.