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.
Abbreviations:
AI (artificial intelligence), CCE (colorectal capsule endoscopy), CCC (convolutional neural network), WCE (wireless capsule endoscopy)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: April 22, 2020
Accepted:
April 13,
2020
Received:
December 21,
2019
Footnotes
DISCLOSURE: The following authors disclosed financial relationships: R. Eliakim: Speaker for Takeda, Jansen, and Medtronic. S. Ben-Horin: Consultant and advisory board for and research support from AbbVie, MSD, Jansen, Takeda, Pfizer, GSK, and CellTrion. U. Kopylov: Research support from Jannsen, Takeda, and Medtronic; advisory fees from Jannsen, Takeda, Medtronic, Abbvie, Dr Falk, and MSD. All other authors disclosed no financial relationships.
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