Deep learning for wireless capsule endoscopy: a systematic review and meta-analysis.
Gastrointest Endosc
; 92(4): 831-839.e8, 2020 10.
Article
em En
| MEDLINE
| ID: mdl-32334015
ABSTRACT
BACKGROUND AND AIMS:
Deep learning is an innovative algorithm based on neural networks. Wireless capsule endoscopy (WCE) is considered the criterion standard for detecting small-bowel diseases. Manual examination of WCE is time-consuming and can benefit from automatic detection using artificial intelligence (AI). We aimed to perform a systematic review of the current literature pertaining to deep learning implementation in WCE.METHODS:
We conducted a search in PubMed for all original publications on the subject of deep learning applications in WCE published between January 1, 2016 and December 15, 2019. Evaluation of the risk of bias was performed using tailored Quality Assessment of Diagnostic Accuracy Studies-2. Pooled sensitivity and specificity were calculated. Summary receiver operating characteristic curves were plotted.RESULTS:
Of the 45 studies retrieved, 19 studies were included. All studies were retrospective. Deep learning applications for WCE included detection of ulcers, polyps, celiac disease, bleeding, and hookworm. Detection accuracy was above 90% for most studies and diseases. Pooled sensitivity and specificity for ulcer detection were .95 (95% confidence interval [CI], .89-.98) and .94 (95% CI, .90-.96), respectively. Pooled sensitivity and specificity for bleeding or bleeding source were .98 (95% CI, .96-.99) and .99 (95% CI, .97-.99), respectively.CONCLUSIONS:
Deep learning has achieved excellent performance for the detection of a range of diseases in WCE. Notwithstanding, current research is based on retrospective studies with a high risk of bias. Thus, future prospective, multicenter studies are necessary for this technology to be implemented in the clinical use of WCE.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Endoscopia por Cápsula
/
Aprendizado Profundo
Tipo de estudo:
Guideline
/
Observational_studies
/
Prognostic_studies
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Risk_factors_studies
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Systematic_reviews
Limite:
Humans
Idioma:
En
Revista:
Gastrointest Endosc
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Israel