Author:
Kim Ji Eun,Choi Yoon Ho,Lee Yeong Chan,Seong Gyeol,Song Joo Hye,Kim Tae Jun,Kim Eun Ran,Hong Sung Noh,Chang Dong Kyung,Kim Young-Ho,Shin Soo-Yong
Abstract
AbstractThe aim of this study was to address the issue of differentiating between Mayo endoscopic subscore (MES) 0 and MES 1 using a deep learning model. A dataset of 492 ulcerative colitis (UC) patients who demonstrated MES improvement between January 2018 and December 2019 at Samsung Medical Center was utilized. Specifically, two representative images of the colon and rectum were selected from each patient, resulting in a total of 984 images for analysis. The deep learning model utilized in this study consisted of a convolutional neural network (CNN)-based encoder, with two auxiliary classifiers for the colon and rectum, as well as a final MES classifier that combined image features from both inputs. In the internal test, the model achieved an F1-score of 0.92, surpassing the performance of seven novice classifiers by an average margin of 0.11, and outperforming their consensus by 0.02. The area under the receiver operating characteristic curve (AUROC) was calculated to be 0.97 when considering MES 1 as positive, with an area under the precision-recall curve (AUPRC) of 0.98. In the external test using the Hyperkvasir dataset, the model achieved an F1-score of 0.89, AUROC of 0.86, and AUPRC of 0.97. The results demonstrate that the proposed CNN-based model, which integrates image features from both the colon and rectum, exhibits superior performance in accurately discriminating between MES 0 and MES 1 in patients with UC.
Funder
New Research Fund grant of the Korean Association for the Study of Intestinal Disease
Korean Society of Gastroenterology
National Research Foundation of Korea
Publisher
Springer Science and Business Media LLC
Cited by
3 articles.
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