Understanding deep learning in capsule endoscopy: Can artificial intelligence enhance clinical practice?

Table 2 Deep learning applications in wireless capsule endoscopy for classifying non-disease objects

Ref.	Class/outcome variable	Deep network architecture	Device/image resolution	Training and internal validation dataset	Testing/external validation dataset	Accuracy (%)/AUC	Sensitivity (%)/specificity (%)
Seguí et al[38], 2016, Spain	Scenes (turbid, bubbles, clear blob, wrinkles, wall)	CNN	Pillcam SB2 CE/100 × 100 pixels	100000 images from 50 videos	20000 images from 50 videos	96/NA	NA/NA
Zou et al[5], 2015, NA	Organ locations (stomach, small intestine, and colon)	CNN (AlexNet)	NA/480 × 480 pixels	60000 images	15000 images	95.52/NA	NA/NA

AUC: Area under the receiver operating characteristic curve; CE: Capsule endoscopy; CNN: Convolutional neural networks; NA: Not available; SB: Small bowel.