Deep Learning Model Using Stool Pictures for Predicting Endoscopic Mucosal Inflammation in Patients With Ulcerative Colitis.

Abstract

Introduction: Stool characteristics may change depending on the endoscopic activity of ulcerative colitis (UC). We developed a deep learning model using stool photographs of patients with UC (DLSUC) to predict endoscopic mucosal inflammation.

Methods: This was a prospective multicenter study conducted in 6 tertiary referral hospitals. Patients scheduled to undergo endoscopy for mucosal inflammation monitoring were asked to take photographs of their stool using smartphones within 1 week before the day of endoscopy. DLSUC was developed using 2,161 stool pictures from 306 patients and tested on 1,047 stool images from 126 patients. The UC endoscopic index of severity was used to define endoscopic activity. The performance of DLSUC in endoscopic activity prediction was compared with that of fecal calprotectin (Fcal).

Results: The area under the receiver operating characteristic curve (AUC) of DLSUC for predicting endoscopic activity was 0.801 (95% confidence interval [CI] 0.717-0.873), which was not statistically different from the AUC of Fcal (0.837 [95% CI, 0.767-0.899, DeLong P = 0.458]). When rectal-sparing cases (23/126, 18.2%) were excluded, the AUC of DLSUC increased to 0.849 (95% CI, 0.760-0.919). The accuracy, sensitivity, and specificity of DLSUC in predicting endoscopic activity were 0.746, 0.662, and 0.877 in all patients and 0.845, 0.745, and 0.958 in patients without rectal sparing, respectively. Active patients classified by DLSUC were more likely to experience disease relapse during a median 8-month follow-up (log-rank test, P = 0.002).

Discussion: DLSUC demonstrated a good discriminating power similar to that of Fcal in predicting endoscopic activity with improved accuracy in patients without rectal sparing. This study implies that stool photographs are a useful monitoring tool for typical UC.