A neural network-based algorithm for assessing the cleanliness of small bowel during capsule endoscopy.
Endoscopy
; 53(9): 932-936, 2021 09.
Article
em En
| MEDLINE
| ID: mdl-33137834
ABSTRACT
BACKGROUND:
Cleanliness scores in small-bowel capsule endoscopy (SBCE) have poor reproducibility. The aim of this study was to evaluate a neural network-based algorithm for automated assessment of small-bowel cleanliness during capsule endoscopy.METHODS:
600 normal third-generation SBCE still frames were categorized as "adequate" or "inadequate" in terms of cleanliness by three expert readers, according to a 10-point scale, and served as a training database. Then, 156 third-generation SBCE recordings were categorized in a consensual manner as "adequate" or "inadequate" in terms of cleanliness; this testing database was split into two independent 78-video subsets for the tuning and evaluation of the algorithm, respectively.RESULTS:
Using a threshold of 79â% "adequate" still frames per video to achieve the best performance, the algorithm yielded a sensitivity of 90.3â%, specificity of 83.3â%, and accuracy of 89.7â%. The reproducibility was perfect. The mean calculation time per video was 3 (standard deviation 1) minutes.CONCLUSION:
This neural network-based algorithm allowing automatic assessment of small-bowel cleanliness during capsule endoscopy was highly sensitive and paves the way for automated, standardized SBCE reports.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Endoscopia por Cápsula
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article