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Background The histologic assessment of inflammatory bowel disease (IBD) relies on qualitative grading methods. Although widely accepted, these instruments are time consuming, require specialized training, and suffer from inter-rater disagreement. For these reasons there is a need for more consistent and less biased methods to assess IBD histology. Methods The algorithm was initially developed using hematoxylin and eosin (H&amp;E) stained whole slide images of colon biopsies (238 ulcerative colitis [UC], 30 Crohn’s Disease [CD], and 28 endoscopically normal adjacent [ENA]). The first two stages implement convolutional neural networks (CNN) which segment 11 key anatomical features (Figure 1). The third stage extracts the features and models them for prediction. Results The first stage of the algorithm was validated on an independent test dataset by calculating the intersection-over-union (IoU) for the ground truth and prediction masks, resulting in a value of 0.97. A preliminary validation for stage 2 was performed by randomly selecting 30 unique biopsy sections from the test dataset and applying a 150um x 150um counting frame. An expert gastrointestinal pathologist confirmed correct cell identification by the algorithm for three of the primary inflammatory cell types: plasma cells, eosinophils, and neutrophils which resulted in a sensitivity/specificity of 0.76/0.99, 0.78/1.00, and 1.00/0.98 respectively. The final stage predicts RHI grades which could be directly compared to pathologist reads (Figure 2, 3, and 4). Conclusion This is the first study to demonstrate the value of machine learning to assess histologic activity in IBD. These methods lay the foundations for future work, and we believe stages 1 and 2 can be explored independently to statistically characterize the histologic changes of IBD, enabling the improvement of preexisting grading systems.

P137 Preliminary validation of a multi-stage machine learning algorithm to assess histological inflammation in inflammatory bowel disease