Aim

Ulcerative colitis (UC) is an idiopathic inflammatory bowel disease (IBD) that may dramatically impact patient quality of life. Introduction in the last decade of a range of biological drugs has reduced the disease burden and may allow us to define histologic remission as an ambitious new treatment target. This has stimulated interest in the development of histological scoring systems to assess disease chronicity and activity. The partially validated DCA score was specifically designed for use in daily clinical practice. This scoring system includes an assessment of the severity of disease chronicity (C), activity (A), and extent of the histologic changes (D). One hurdle for widespread implementation into clinical practice is the reported inter-observer variability.

To tackle the inter-observer variability, we aim to develop a computer algorithm to automatically perform the score on scans of whole slide images. The hematoxylin-eosin (H&E) stained slides from a retrospective cohort of adult patients with UC who received an endoscopy with biopsies at the Inselspital Bern during disease follow-up between 2011 and 2021 will be scanned. Two expert gastrointestinal pathologists blinded to the clinical data will independently perform the DCA-score. Afterward, discrepancies will be discussed in a consensus meeting to determine the consensus DCA score.

The core computer algorithm involves using AlexNet. The pre-trained model vectors will be open-source data trained on the “Image Net” classification dataset. Moreover, some functions from the open-source library “OpenCV” will be incorporated. The scanned slides will be randomly split into train and test sets with an 80:20 ratio. The only available labels for the algorithm will be the consensus DCA score. To enrich our dataset, small segments of the scans will be taken at various magnifications and various image processing transformations shall be applied (including but not limited to Gaussian Blur, Rotation, and Flipping) to make the model more robust to differences in scanning devices and histological slide preparation. While the model ranks individual small fragments of the image, the overall slide score shall be determined by an aggregate vote based on all the fragment rankings. Afterward, scans from the first available follow-up endoscopy will constitute a validation set, which will serve as a proxy of how the algorithm performs in a real-world setting.