Development and Validation of a Deep Neural Network for Accurate Evaluation of Endoscopic Images From Patients With Ulcerative Colitis

There are intra- and interobserver variations in endoscopic assessment of ulcerative colitis (UC) and biopsies are often collected for histologic evaluation. We sought to develop a deep neural network system for consistent, objective, and real-time analysis of endoscopic images from patients with UC. We constructed the deep neural network for evaluation of UC (DNUC) algorithm using 40,758 images of colonoscopies and 6885 biopsy results from 2012 patients with UC who underwent colonoscopy from January 2014 through March 2018 at a single center in Japan (the training set). We validated the accuracy of the DNUC algorithm in a prospective study of 875 patients with UC who underwent colonoscopy from April 2018 through April 2019, with 4187 endoscopic images and 4104 biopsy specimens. Endoscopic remission was defined as a UC endoscopic index of severity score of 0; histologic remission was defined as a Geboes score of 3 points or less. In the prospective study, the DNUC identified patients with endoscopic remission with 90.1% accuracy (95% confidence interval [CI] 89.2%–90.9%) and a kappa coefficient of 0.798 (95% CI 0.780–0.814), using findings reported by endoscopists as the reference standard. The intraclass correlation coefficient between the DNUC and the endoscopists for UC endoscopic index of severity scoring was 0.917 (95% CI 0.911–0.921). The DNUC identified patients in histologic remission with 92.9% accuracy (95% CI 92.1%–93.7%); the kappa coefficient between the DNUC and the biopsy result was 0.859 (95% CI 0.841–0.875). We developed a deep neural network for evaluation of endoscopic images from patients with UC that identified those in endoscopic remission with 90.1% accuracy and histologic remission with 92.9% accuracy. The DNUC can therefore identify patients in remission without the need for mucosal biopsy collection and analysis. Trial number: UMIN000031430 [Display omitted]