Evolving role of artificial intelligence in gastrointestinal endoscopy

Table 1 Recent clinical studies in artificial intelligence using central neural network and capsule endoscopy for small intestinal imaging

Ref.	Type of study	Aim of study	Images/video	Results
Aoki et al[58], 2020	Retrospective	Detection of mucosal breaks/erosion	20 capsule endoscopy videos	Detection rate, expert 87%; trainee, 55%
Klang et al[59], 2020	Retrospective	Detection of small intestinal ulcers in Crohn’s disease	17640 images from 49 patients	Accuracy, 96.7%; sensitivity, 96.8%; specificity, 96.6% (5-fold)
Tsuboi et al[60], 2020	Retrospective	Detection of small intestinal angiodysplasia	2237 images from 141 patients	Sensitivity, 98.8%; specificity, 98.4%
Ding et al[61], 2019	Retrospective	Detection of small intestinal abnormal images	158235 images from 1970 patients	Sensitivity, 99.9%; reading time, 5.9 min
Saito et al[62], 2020	Retrospective	Detection and classification of protruding lesions	30584 images from 292 patients	Sensitivity, 90.7%; specificity, 79.8%; reading time, 530.462 s