P137 Preliminary validation of a multi-stage machine learning algorithm to assess histological inflammation in inflammatory bowel disease. (27th May 2021)

• Record Type: Journal Article
• Authors: Hagendorn, E; Karsen, S; Pai, R; Jairath, V; Knight, H; Schwartz, A; Laroux, S; Butler, J; Dunstan, R
• Is Part Of: Journal of Crohn's and colitis. Volume 15(2021)Supplement 1
• Page Start: S224
• Page End: S225
• Publication Date: 2021-05-27
• Subjects: Inflammatory bowel diseases -- Periodicals; 616.344005
• DOI: 10.1093/ecco-jcc/jjab076.264
• Languages: English
• ISSNs: 1873-9946

Abstract

Abstract: 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&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.