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Copyright ©The Author(s) 2019.
World J Gastroenterol. Apr 14, 2019; 25(14): 1666-1683
Published online Apr 14, 2019. doi: 10.3748/wjg.v25.i14.1666
Table 5 Summary of clinical studies using artificial intelligence in the capsule endoscopy
Ref.Published yearAim of studyDesign of studyNumber of subjectsType of AIOutcomes
Leenhardt et al[62]2019Detection of gastrointestinal angiectasiaRetrospective600 control images and 600 typical angiectasia images (divided equally into training and test datasets)CNNSensitivity: 100%, specificity: 96%, PPV: 96%, NPV: 100%.
Zhou et al[63]2017Classification of celiac diseaseRetrospectiveTraining set: 6 celiac disease patients, 5 controls. Test set: additional 5 celiac disease patients, 5 controlsCNNSensitivity: 100%, specificity: 100% (for test dataset)
He et al[64]2018Detection of intestinal hookwormsRetrospective440000 imagesCNNSensitivity: 84.6%, specificity: 88.6%
Seguí et al[65]2016Characterization of small intestinal motilityRetrospective120000 images (training set: 100000, test set: 20000)CNNMean classification accuracy: 96%