Artificial intelligence in gastrointestinal cancer: Recent advances and future perspectives

Table 2 Previous studies on colonoscopy using artificial intelligence

Ref.	Targets	Sample sizes	Inputs	Tasks	Analysis method	Diagnostic performance
Akbari et al[35]	Screening endoscopy	300 polyp images	Polyp images	Auto segmentation of polyps	CNN	Accuracy: 0.977, Sensitivity: 74.8%
Jin et al[36]	Screening endoscopy	Training: 2150 polyps, test: 300 polyps	NBI images	Differentiation of adenoma and hyperplastic polyps	CNN	The model reduced the time of endoscopy and increased accuracy by novice endoscopists
Urban et al[37]	Screening endoscopy	8641 polyp images and 20 colonoscopy videos	Polyp images	Detection of polyps	CNN	AUC: 0.991, Accuracy: 96.4%
Yamada et al[38]	Screening endoscopy	4840 images, 77 colonoscopy videos	Real-time images	Differentiation of the early signs of CRC	CNN	Sensitivity: 97.3%, Specificity: 99.0%

CNN: Convolutional neural network; NBI: Narrow-band image; AUC: Area under the curve.