Deep-learning system for real-time differentiation between Crohn's disease, intestinal Behçet's disease, and intestinal tuberculosis.
J Gastroenterol Hepatol
; 36(8): 2141-2148, 2021 Aug.
Article
in En
| MEDLINE
| ID: mdl-33554375
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.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Tuberculosis, Gastrointestinal
/
Crohn Disease
/
Behcet Syndrome
/
Deep Learning
/
Gastrointestinal Diseases
Type of study:
Diagnostic_studies
/
Prognostic_studies
Limits:
Adolescent
/
Adult
/
Female
/
Humans
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Male
/
Middle aged
Language:
En
Journal:
J Gastroenterol Hepatol
Journal subject:
GASTROENTEROLOGIA
Year:
2021
Type:
Article