Identification of Barrett's esophagus in endoscopic images using deep learning.
BMC Gastroenterol
; 21(1): 479, 2021 Dec 17.
Article
em En
| MEDLINE
| ID: mdl-34920705
ABSTRACT
BACKGROUND:
Development of a deep learning method to identify Barrett's esophagus (BE) scopes in endoscopic images.METHODS:
443 endoscopic images from 187 patients of BE were included in this study. The gastroesophageal junction (GEJ) and squamous-columnar junction (SCJ) of BE were manually annotated in endoscopic images by experts. Fully convolutional neural networks (FCN) were developed to automatically identify the BE scopes in endoscopic images. The networks were trained and evaluated in two separate image sets. The performance of segmentation was evaluated by intersection over union (IOU).RESULTS:
The deep learning method was proved to be satisfying in the automated identification of BE in endoscopic images. The values of the IOU were 0.56 (GEJ) and 0.82 (SCJ), respectively.CONCLUSIONS:
Deep learning algorithm is promising with accuracies of concordance with manual human assessment in segmentation of the BE scope in endoscopic images. This automated recognition method helps clinicians to locate and recognize the scopes of BE in endoscopic examinations.Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Esôfago de Barrett
/
Aprendizado Profundo
Tipo de estudo:
Diagnostic_studies
/
Guideline
Limite:
Humans
Idioma:
En
Revista:
BMC Gastroenterol
Assunto da revista:
GASTROENTEROLOGIA
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
China