Deep neural network for video colonoscopy of ulcerative colitis: a cross-sectional study

Background A combination of endoscopic and histological evaluation is important in the management of patients with ulcerative colitis. We aimed to adapt our previous deep neural network system (deep neural ulcerative colitis [DNUC]) to full video colonoscopy and evaluate its validity in the real-time detection of histological mucosal inflammation. Methods In this multicentre, cross-sectional study, we prospectively enrolled consecutive patients (>= 15 years) with ulcerative colitis who had an indication for colonoscopy at five hospitals in Japan. Patients in clinical remission were randomly assigned (1:2) to study 1 and study 2. Those with clinically active disease were assigned to study 2 only. Study 1 assessed the validity of real-time histological assessment using DNUC and study 2 validated the consistency of endoscopic scoring between DNUC and experts. The primary endpoint for study 1 was comparison of the results judged by DNUC (healing or active) with biopsy specimens evaluated by pathologists. In study 2, the primary endpoint was the ability of DNUC to determine the Ulcerative Colitis Endoscopic Index of Severity score compared with centrally evaluated scoring by inflammatory bowel disease endoscopy experts. Findings From April 1, 2020, to March 31, 2021, 770 patients (180 in study 1 and 590 in study 2) were enrolled. Using real-time histological evaluation, DNUC was able to evaluate the presence or absence of histological inflammation in 729 (81%) of 900 biopsy specimens. For predicting histological remission, the DNUC had a sensitivity of 97.9% (95% CI 97.0-98.5) and a specificity of 94.6% (91.1-96.9). Moreover, its positive predictive value was 98.6% (97.7-99.2) and negative predictive value was 92.1% (88.7-94.3). The intradass correlation coefficient between DNUC and experts for endoscopic scoring was 0.927 (95% CI 0.915-0.938). Interpretation DNUC provided consistently accurate endoscopic scoring and showed potential for reducing the number of biopsies required. This system is an objective and consistent application for video colonoscopy that has potential for use in various medical situations. Copyright (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This study adapted a deep neural network system (DNUC) to real-time video colonoscopy for the detection of histological mucosal inflammation in ulcerative colitis patients. The DNUC showed high sensitivity and specificity in predicting histological remission, and its endoscopic scoring was consistent with expert evaluations. The system has the potential to reduce the number of biopsies required and can be applied in various medical situations.