Machine learning methods to predict presence of intestine damage in patients with Crohn's disease

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.