Travis, Simon

Abstract Background and Aims Lack of clinical validation and inter-observer variability are two limitations of endoscopic assessment and scoring of disease severity in patients with ulcerative colitis [UC]. We developed a deep learning [DL] model to improve, accelerate and automate UC detection, and predict the Mayo Endoscopic Subscore [MES] and the Ulcerative Colitis Endoscopic Index of Severity [UCEIS]. Methods A total of 134 prospective videos [1550 030 frames] were collected and those with poor quality were excluded. The frames were labelled by experts based on MES and UCEIS scores. The scored frames were used to create a preprocessing pipeline and train multiple convolutional neural networks [CNNs] with proprietary algorithms in order to filter, detect and assess all frames. These frames served as the input for the DL model, with the output being continuous scores for MES and UCEIS [and its components]. A graphical user interface was developed to support both labelling video sections and displaying the predicted disease severity assessment by the artificial intelligence from endoscopic recordings. Results Mean absolute error [MAE] and mean bias were used to evaluate the distance of the continuous model’s predictions from ground truth, and its possible tendency to over/under-predict were excellent for MES and UCEIS. The quadratic weighted kappa used to compare the inter-rater agreement between experts’ labels and the model’s predictions showed strong agreement [0.87, 0.88 at frame-level, 0.88, 0.90 at section-level and 0.90, 0.78 at video-level, for MES and UCEIS, respectively]. Conclusions We present the first fully automated tool that improves the accuracy of the MES and UCEIS, reduces the time between video collection and review, and improves subsequent quality assurance and scoring.

Background and aim Disease activity for Crohn's disease (CD) and UC is typically defined based on symptoms at a moment in time, and ignores the long-term burden of disease. The aims of this study were to select the attributes determining overall disease severity, to rank the importance of and to score these individual attributes for both CD and UC. Methods Using a modified Delphi panel, 14 members of the International Organization for the Study of Inflammatory Bowel Diseases (IOIBD) selected the most important attributes related to IBD. Eighteen IOIBD members then completed a statistical exercise (conjoint analysis) to create a relative ranking of these attributes. Adjusted utilities were developed by creating proportions for each level within an attribute. Results For CD, 15.8% of overall disease severity was attributed to the presence of mucosal lesions, 10.9% to history of a fistula, 9.7% to history of abscess and 7.4% to history of intestinal resection. For UC, 18.1% of overall disease severity was attributed to mucosal lesions, followed by 14.0% for impact on daily activities, 11.2% C reactive protein and 10.1% for prior experience with biologics. Overall disease severity indices were created on a 100-point scale by applying each attribute's average importance to the adjusted utilities. Conclusions Based on specialist opinion, overall CD severity was associated more with intestinal damage, in contrast to overall UC disease severity, which was more dependent on symptoms and impact on daily life. Once validated, disease severity indices may provide a useful tool for consistent assessment of overall disease severity in patients with IBD.