AUTHOR=Ruan Guangcong , Qi Jing , Cheng Yi , Liu Rongbei , Zhang Bingqiang , Zhi Min , Chen Junrong , Xiao Fang , Shen Xiaochun , Fan Ling , Li Qin , Li Ning , Qiu Zhujing , Xiao Zhifeng , Xu Fenghua , Lv Linling , Chen Minjia , Ying Senhong , Chen Lu , Tian Yuting , Li Guanhu , Zhang Zhou , He Mi , Qiao Liang , Zhang Zhu , Chen Dongfeng , Cao Qian , Nian Yongjian , Wei Yanling 

TITLE=Development and Validation of a Deep Neural Network for Accurate Identification of Endoscopic Images From Patients With Ulcerative Colitis and Crohn's Disease

JOURNAL=Frontiers in Medicine

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2022.854677

DOI=10.3389/fmed.2022.854677

ISSN=2296-858X

ABSTRACT=Background and aim: The identification of ulcerative colitis (UC) and Crohn’s disease (CD) is a key element interfering with therapeutic response, but it is often difficult for less experienced endoscopists to identify UC and CD. Therefore, we aimed to develop and validate a deep learning diagnostic system trained by a large number of colonoscopy images to identify UC and CD.
Methods: This multicenter, diagnostic study was performed in 5 hospitals in China. Normal individuals and active IBD (inflammatory bowel disease) patients were enrolled. A dataset of 1772 participants with 49154 colonoscopy images was obtained between January 2018 and November 2020. We developed a deep learning model based on a deep convolutional neural network (CNN) in the examination. To generalize the applicability of the deep learning model in clinical practice, we compared the deep model with 10 endoscopists and applied it in 3 hospitals across China.
Results: The identification accuracy obtained by the deep model was superior to that of experienced endoscopists per patient (deep model vs. trainee endoscopist, 99.1% vs. 78.0%; deep model vs. competent endoscopist, 99.1% vs. 92.2%, P<0.001) and per lesion (deep model vs. trainee endoscopist, 90.4% vs. 59.7%; deep model vs competent endoscopist 90.4% vs. 69.9%, P<0.001). In addition, the mean reading time was reduced by the deep model (deep model vs. endoscopists, 6.20 s vs. 2425.00 s, P<0.001).
Conclusions: We developed a deep model to assist with the clinical diagnosis of IBD. This provides a diagnostic device for medical education and clinicians to improve the efficiency of diagnosis and treatment.