Development of a Convolutional Neural Network-Based Colonoscopy Image Assessment Model for Differentiating Crohn’s Disease and Ulcerative Colitis

Frontiers in Medicine

article

Lijia Wang¹  Liping Chen¹  Xianyuan Wang²  Kaiyuan Liu²  Ting Li²  Yue Yu²  Jian Han¹  Shuai Xing¹  Jiaxin Xu¹  Dean Tian¹  Ursula Seidler³  Fang Xiao¹ 

[1] Department of Gastroenterology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology;Wuhan United Imaging Healthcare Surgical Technology Co., Ltd.;Department of Gastroenterology of Hannover Medical School

关键词: inflammatory bowel disease;    Crohn’s disease;    ulcerative colitis;    artificial intelligence;    deep learning;    convolutional neural network;    colonoscopy image;    classification;   

DOI  :  10.3389/fmed.2022.789862

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Objective Evaluation of the endoscopic features of Crohn’s disease (CD) and ulcerative colitis (UC) is the key diagnostic approach in distinguishing these two diseases. However, making diagnostic differentiation of endoscopic images requires precise interpretation by experienced clinicians, which remains a challenge to date. Therefore, this study aimed to establish a convolutional neural network (CNN)-based model to facilitate the diagnostic classification among CD, UC, and healthy controls based on colonoscopy images. Methods A total of 15,330 eligible colonoscopy images from 217 CD patients, 279 UC patients, and 100 healthy subjects recorded in the endoscopic database of Tongji Hospital were retrospectively collected. After selecting the ResNeXt-101 network, it was trained to classify endoscopic images either as CD, UC, or normal. We assessed its performance by comparing the per-image and per-patient parameters of the classification task with that of the six clinicians of different seniority. Results In per-image analysis, ResNeXt-101 achieved an overall accuracy of 92.04% for the three-category classification task, which was higher than that of the six clinicians (90.67, 78.33, 86.08, 73.66, 58.30, and 86.21%, respectively). ResNeXt-101 also showed higher differential diagnosis accuracy compared with the best performing clinician (CD 92.39 vs. 91.70%; UC 93.35 vs. 92.39%; normal 98.35 vs. 97.26%). In per-patient analysis, the overall accuracy of the CNN model was 90.91%, compared with 93.94, 78.79, 83.33, 59.09, 56.06, and 90.91% of the clinicians, respectively. Conclusion The ResNeXt-101 model, established in our study, performed superior to most clinicians in classifying the colonoscopy images as CD, UC, or healthy subjects, suggesting its potential applications in clinical settings.

【 授权许可】

CC BY