DOP74 Artificial Intelligence Quantifying Endoscopic Severity of Ulcerative Colitis in Gradation Scale

Abstract Background The existing endoscopic scores for Ulcerative Colitis (UC) such as the Mayo Endoscopic Subscore (MES) and the Ulcerative Colitis Endoscopic Index of Severity (UCEIS) are objectively categorize the severity of the disease based on presence or absence of endoscopic findings. Therefore, they may not reflect the range of clinical severity within each category. But, Inflammatory bowel disease expert endoscopists do not only categorize the severity, but rather diagnose the overall impression of degree of inflammation. Therefore, the purpose of this study was to develop an AI that can accurately represent the complexed assessment of endoscopic severity of UC by expert endoscopists, similarly to Visual Analogue Scale (VAS). Methods To enable the AI to perform continuous evaluations of inflammation in line with the strategy used by IBD expert endoscopists, we did not utilize scores determined from images using MES or UCEIS for physician data. Rather, we incorporated data for the relationships identified by IBD expert endoscopists who compared the severity of paired images. This study was conducted using a method that incorporates data on relationships identified by comparing the severity of paired images created from 59595 endoscopic images by an IBD expert endoscopist into a Ranking- Convolutional Neural Network, and then the severity was then expressed on a scale called UC Endoscopic Gradation Scale (UCEGS) rather than a score. Using 4,000 images for which the MES had been assessed beforehand by an IBD expert endoscopist, correlation coefficients were calculated to ensure that there were no inconsistencies in assessments of severity made using results of this novel AI diagnosed UCEGS and the current MES score. The correlation coefficients of the means of the UCEGS results for the 50 test images evaluated by the five IBD expert endoscopists and the novel AI were also calculated. Results Spearman's correlation coefficient between MES and AI-diagnosed UCEGS was approximately 0.89, indicating a strong positive correlation for the order of severity between the AI-diagnosed UCEGS and MES. Correlation coefficient between IBD expert endoscopists and the AI of the evaluation results were all higher than 0.95 (P<0.01). Conclusion In this study, we developed a novel AI that does not rely on conventional scoring methods but instead aims to leverage the intelligence of IBD expert endoscopists when evaluating the disease status of UC. This AI quantifies inflammation as a gradient, allowing for an automated visualization of the expert endoscopist's assessment of mucosal inflammation.