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Proceedings Paper

Machine learning methods to predict presence of intestine damage in patients with Crohn’s disease
Author(s): Binu E. Enchakalody; Brianna Henderson; Stewart C. Wang; Grace L. Su; Ashish P. Wasnik; Mahmoud M. Al-Hawary; Ryan W. Stidham
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Paper Abstract

The diagnosis of Crohn's disease (CD) can be challenging given variation in anatomic disease distribution, morphology, and proportion of intestine affected. Subsequently, the appearance and presentation of disease on cross-sectional imaging are a heterogeneous combination of shapes and image features, making differentiation of normal vs. diseased small intestine prone to inter-observer variation. Applying machine learning methods to cross-sectional, imaging interpretation may improve the accuracy of CD diagnosis and distinguish normal from diseased intestine by automated approaches. Using a set of 207 CT-enterography (CTE) scans, two independent radiologists labeled the presence of disease vs. non-disease at 7.5mm intervals along the length of the bowel (mini-segments), generating a dataset of 10,552 observations for model training and testing. We introduce two types of classifiers to quantitatively assess CD related intestinal damage for each mini-segment. The sensitivity, specificity and AUC for the best performing ensemble and CNN models are 84.9%, 84.7%, 0.93, and 90.9%, 78.6%, 0.92 respectively. The accuracy for classifying full segments as diseased vs. normal using ensemble and CNN models are 96.3% and 90.7% respectively.

Paper Details

Date Published: 16 March 2020
PDF: 12 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131436 (16 March 2020); doi: 10.1117/12.2549326
Show Author Affiliations
Binu E. Enchakalody, Univ. of Michigan Medical School (United States)
Brianna Henderson, Univ. of Michigan Medical School (United States)
Stewart C. Wang, Univ. of Michigan Medical School (United States)
Grace L. Su, Univ. of Michigan Medical School (United States)
Veterans Affairs Ann Arbor Health Care System (United States)
Ashish P. Wasnik, Univ. of Michigan Medical School (United States)
Mahmoud M. Al-Hawary, Univ. of Michigan Medical School (United States)
Ryan W. Stidham, Univ. of Michigan Medical School (United States)
Michigan Integrated Ctr. for Health Analytics and Medical Prediction (United States)


Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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