A neural network algorithm for detection of GI angiectasia during small-bowel capsule endoscopy.
Gastrointest Endosc
; 89(1): 189-194, 2019 01.
Article
em En
| MEDLINE
| ID: mdl-30017868
ABSTRACT
BACKGROUND AND AIMS:
GI angiectasia (GIA) is the most common small-bowel (SB) vascular lesion, with an inherent risk of bleeding. SB capsule endoscopy (SB-CE) is the currently accepted diagnostic procedure. The aim of this study was to develop a computer-assisted diagnosis tool for the detection of GIA.METHODS:
Deidentified SB-CE still frames featuring annotated typical GIA and normal control still frames were selected from a database. A semantic segmentation images approach associated with a convolutional neural network (CNN) was used for deep-feature extractions and classification. Two datasets of still frames were created and used for machine learning and for algorithm testing.RESULTS:
The GIA detection algorithm yielded a sensitivity of 100%, a specificity of 96%, a positive predictive value of 96%, and a negative predictive value of 100%. Reproducibility was optimal. The reading process for an entire SB-CE video would take 39 minutes.CONCLUSIONS:
The developed CNN-based algorithm had high diagnostic performances, allowing detection of GIA in SB-CE still frames. This study paves the way for future automated CNN-based SB-CE reading softwares.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Redes Neurais de Computação
/
Angiodisplasia
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Endoscopia por Cápsula
/
Enteropatias
/
Intestino Delgado
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Aged
/
Aged80
/
Female
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Humans
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Male
/
Middle aged
Idioma:
En
Revista:
Gastrointest Endosc
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
França