Deep Learning Algorithm for Diagnose Endoscopic and Histological Images With Ulcerative Colitis

Abstract

The goal of treatment for ulcerative colitis is to achieve histological and endoscopic remission. Aiming at the problem that the observer will be affected by subjective factors in the endoscopic evaluation of ulcerative colitis and the cumbersome diagnosis process of histological images, this paper aims to develop a computer-assisted diagnosis system for real-time, objective diagnosis of endoscopic images and use the trained CNN model to predict histological images of patients with ulcerative colitis. Diagnosing endoscopic remission of ulcerative colitis, the accuracy of the CNN is 97.04% (95% CI,96.26%:97.62%). Diagnosing the severity of endoscopic inflammation in patients with ulcerative colitis, the accuracy of the CNN is 90.15% (95% CI, 89.49%:90.82%). The accuracy of predicting histological remission was 91.28%. The kappa coefficient between the CNN model and the biopsy results was 82.56%. The proposed computer-aided diagnosis system can effectively evaluate the inflammation of endoscopic images of patients with ulcerative colitis and predict the remission of histological images with high accuracy and consistency.