A neural network algorithm for detection of GI angiectasia during small-bowel capsule endoscopy.
Journal
Gastrointestinal endoscopy
ISSN: 1097-6779
Titre abrégé: Gastrointest Endosc
Pays: United States
ID NLM: 0010505
Informations de publication
Date de publication:
01 2019
01 2019
Historique:
received:
07
04
2018
accepted:
29
06
2018
pubmed:
19
7
2018
medline:
12
4
2019
entrez:
19
7
2018
Statut:
ppublish
Résumé
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. 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. 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. 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.
Sections du résumé
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.
Identifiants
pubmed: 30017868
pii: S0016-5107(18)32828-1
doi: 10.1016/j.gie.2018.06.036
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
189-194Investigateurs
Sylvie Sacher-Huvelin
(S)
Farida Mesli
(F)
Chloé Leandri
(C)
Isabelle Nion-Larmurier
(I)
Stéphane Lecleire
(S)
Romain Gerard
(R)
Clotilde Duburque
(C)
Geoffroy Vanbiervliet
(G)
Xavier Amiot
(X)
Jean Philippe Le Mouel
(J)
Michel Delvaux
(M)
Pierre Jacob
(P)
Camille Simon-Shane
(C)
Olivier Romain
(O)
Commentaires et corrections
Type : CommentIn
Informations de copyright
Copyright © 2019 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.