Summary

Histologic evaluation of the mucosal changes associated with celiac disease is important for establishing an accurate diagnosis and monitoring the impact of investigational therapies. While the Marsh-Oberhuber classification has been used to categorize the histologic findings into discrete stages (i.e. type 0-3c), significant variability has been documented between observers using this ordinal scoring system. Therefore, a pathologist-trained machine learning classifier was developed to objectively quantitate the pathological changes of villus blunting, intraepithelial lymphocytosis, and crypt hyperplasia in small intestine endoscopic biopsies.

A HALO AI DenseNet2 convolutional neural network (Indica Labs) was trained and combined with a second HALO algorithm to quantitate intraepithelial lymphocytes on CD3 immunohistochemistry whole slide images and used to correlate feature outputs with ground truth modified Marsh scores in 116 samples. Median %CD3 counts (positive cells/enterocytes) from villous epithelium increased with higher Marsh scores. Indicators of villus blunting and crypt hyperplasia were also observed. Using these individual features, a combined feature machine learning score (MLS) was created to evaluate a set of 28 matched pre- and post-intervention biopsies captured before and after dietary gluten restriction. The disposition of the continuous MLS paired biopsy result aligned with the Marsh score in 96.4% (27/28) of the cohort. Machine learning classifiers can objectively quantify histologic features and capture additional data not achievable with manual scoring. Such approaches should be further investigated to improve biopsy evaluation, especially for clinical trials.