Application of artificial intelligence in gastroenterology

Table 5 Summary of clinical studies using artificial intelligence in the capsule endoscopy

Ref.	Published year	Aim of study	Design of study	Number of subjects	Type of AI	Outcomes
Leenhardt et al[62]	2019	Detection of gastrointestinal angiectasia	Retrospective	600 control images and 600 typical angiectasia images (divided equally into training and test datasets)	CNN	Sensitivity: 100%, specificity: 96%, PPV: 96%, NPV: 100%.
Zhou et al[63]	2017	Classification of celiac disease	Retrospective	Training set: 6 celiac disease patients, 5 controls. Test set: additional 5 celiac disease patients, 5 controls	CNN	Sensitivity: 100%, specificity: 100% (for test dataset)
He et al[64]	2018	Detection of intestinal hookworms	Retrospective	440000 images	CNN	Sensitivity: 84.6%, specificity: 88.6%
Seguí et al[65]	2016	Characterization of small intestinal motility	Retrospective	120000 images (training set: 100000, test set: 20000)	CNN	Mean classification accuracy: 96%

AI: Artificial intelligence; CNN: Convolutional neural network; PPV: Positive predictive value; NPV: Negative predictive value.