Artificial Intelligence- and Physician-Interpreted Stool Image Characteristics Correlate With C-Reactive Protein Among Inpatients With Acute Severe Ulcerative Colitis: A Pilot Study.

Abstract

Background: Stool characteristics are used as a measure of ulcerative colitis (UC) disease activity, but they have not been validated against objective inflammation. We aimed to determine whether stool characteristics measured by trained artificial intelligence (AI) and physicians correlate with inflammation in UC.

Methods: Patients hospitalized with acute severe UC (ASUC) were asked to capture images of all bowel movements using a smartphone application (Dieta®). Validated AI was used to measure five stool characteristics including the Bristol stool scale. Additionally, four physicians scored each image for blood amount, mucus amount, and whether stool was in a toilet or commode. AI measurements and mean physician scores were rank-normalized and correlated with rank-normalized CRP values using mixed linear regression models. Mann-Whitney tests were used to compare median CRP values of images with and without mucus and with and without blood.

Results: We analyzed 151 stool images collected from 5 patients admitted with ASUC (mean age 42 years, 40% male). Overall, Bristol stool scale and fragmentation positively correlated with CRP, while stool consistency negatively correlated with CRP. The median CRP of images with mucus was higher than that of images without mucus.

Conclusions: Smartphone application AI measurements of Bristol stool scale, stool consistency, and stool fragmentation significantly correlate with CRP values in hospitalized patients with ASUC. Additionally, median CRPs are higher when mucus is seen. Further training of smartphone-based AI algorithms to validate the association of stool characteristics with objective inflammation may yield a novel, noninvasive tool for UC disease monitoring.