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A neural network algorithm for detection of GI angiectasia during small-bowel capsule endoscopy.
- Source :
- Gastrointestinal Endoscopy; Jan2019, Vol. 89 Issue 1, p189-194, 6p
- Publication Year :
- 2019
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00165107
- Volume :
- 89
- Issue :
- 1
- Database :
- Supplemental Index
- Journal :
- Gastrointestinal Endoscopy
- Publication Type :
- Academic Journal
- Accession number :
- 133555779
- Full Text :
- https://doi.org/10.1016/j.gie.2018.06.036