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

Lijia Wang; Liping Chen; Xianyuan Wang; Kaiyuan Liu; Ting Li; Yue Yu; Jian Han; Shuai Xing; Jiaxin Xu; Dean Tian; Ursula Seidler; Fang Xiao

doi:10.3389/fmed.2022.789862

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

Front Med (Lausanne). 2022 Apr 8:9:789862. doi: 10.3389/fmed.2022.789862. eCollection 2022.

Authors

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

Affiliations

¹ Department of Gastroenterology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
² Wuhan United Imaging Healthcare Surgical Technology Co., Ltd., Wuhan, China.
³ Department of Gastroenterology of Hannover Medical School, Hanover, Germany.

Abstract

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.

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