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
em En
| MEDLINE
| ID: mdl-33554375
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.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Tuberculose Gastrointestinal
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Doença de Crohn
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Síndrome de Behçet
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Aprendizado Profundo
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Gastroenteropatias
Tipo de estudo:
Diagnostic_studies
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Prognostic_studies
Limite:
Adolescent
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Adult
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
J Gastroenterol Hepatol
Assunto da revista:
GASTROENTEROLOGIA
Ano de publicação:
2021
Tipo de documento:
Article
País de publicação:
Austrália