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Deep-learning system for real-time differentiation between Crohn's disease, intestinal Behçet's disease, and intestinal tuberculosis

Authors
 Jung Min Kim  ;  Jun Gu Kang  ;  Sungwon Kim  ;  Jae Hee Cheon 
Citation
 JOURNAL OF GASTROENTEROLOGY AND HEPATOLOGY, Vol.36(8) : 2141-2148, 2021-08 
Journal Title
JOURNAL OF GASTROENTEROLOGY AND HEPATOLOGY
ISSN
 0815-9319 
Issue Date
2021-08
Keywords
Behçet's disease ; Crohn's disease ; deep learning ; intestinal tuberculosis
Abstract
Background and aim: Pattern analysis of big data can provide a superior direction for the clinical differentiation of diseases with similar endoscopic findings. This study aimed to develop a deep-learning algorithm that performs differential diagnosis between intestinal Behçet's disease (BD), Crohn's disease (CD), and intestinal tuberculosis (ITB) using colonoscopy images.

Methods: The typical pattern for each disease was defined as a typical image. We implemented a convolutional neural network (CNN) using Pytorch and visualized a deep-learning model through Gradient-weighted Class Activation Mapping. The performance of the algorithm was evaluated using the area under the receiver operating characteristic curve (AUROC).

Results: A total of 6617 colonoscopy images of 211 CD, 299 intestinal BD, and 217 ITB patients were used. The accuracy of the algorithm for discriminating the three diseases (all-images: 65.15% vs typical images: 72.01%, P = 0.024) and discriminating between intestinal BD and CD (all-images: 78.15% vs typical images: 85.62%, P = 0.010) was significantly different between all-images and typical images. The CNN clearly differentiated colonoscopy images of the diseases (AUROC from 0.7846 to 0.8586). Algorithmic prediction AUROC for typical images ranged from 0.8211 to 0.9360.

Conclusion: This study found that a deep-learning model can discriminate between colonoscopy images of intestinal BD, CD, and ITB. In particular, the algorithm demonstrated superior discrimination ability for typical images. This approach presents a beneficial method for the differential diagnosis of the diseases.
Full Text
https://onlinelibrary.wiley.com/doi/10.1111/jgh.15433
DOI
10.1111/jgh.15433
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Sungwon(김성원) ORCID logo https://orcid.org/0000-0001-5455-6926
Cheon, Jae Hee(천재희) ORCID logo https://orcid.org/0000-0002-2282-8904
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/185371
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