Table 2.

Summary of studies for computer aided convolutional neural network polyp detection

	Algorithm and method	Image	Dataset	Processing speed	Outcomes
Misawa et al. 2018 [24]	3D CNN (ex vivo)	Video	73 videos divided into 546 short videos Training: 105 polyps positive, 306 polyps negative; testing: 50 polyps positive, 85 polyps negative		Sensitivity: 90%; specificity: 63.3%; accuracy: 76.5%
Urban et al. 2018 [25]	CNN VGG19 (ex vivo)	Still and video	Training: 8641 images Testing  Set 1: 9 videos  Set 2: 11 videos (missed polyp simulation)	10 ms/frame	Sensitivity at 75% False negative rate: 96.9% Accuracy: 96.4% Frame by frame false positive rate: 5%
Yamada et al. 2019 [26]	Faster R-CNN VGG 16 (ex vivo)	Still and video	Training: 4087 images, and 135,874 video frames Testing: 4840 still images, and 77 videos	21.9 ms/image 30 frames/s	Sensitivity: 97.3%; specificity: 99.0%; AUC: 0.975
Becq et al. 2020 [27]	SegNet CNN (ex vivo)	Video	50 prospectively collected videos		PDR: 81% Sensitivity: 98.8% Positive predictive value: 40.6%
Klare et al. 2019 [28]	CNN	Live	55 live colonoscopies	50 ms of latency	Sensitivity: 75.3%; ADR: 29%
Wang et al. 2019 [29]	CNN Real time RCT	Live	Training: 5545 images Test: 536 routine colonoscopy	25 frames/s 77 ms latency	ADR: 20.3% (conventional) versus 29.1% (CAD), P<0.001 Mean number of polyps per procedure: 0.51 versus 0.97, P<0.001
Liu et al. 2020 [30]	3D CNN Real time RCT	Live	Training: 101 polyp positive; 300 polyp negative Testing: 46 polyp positive; 88 polyp negative		PDR: Control (28%) versus CAD (44%), P<0.001 ADR: Control (24%) versus CAD (39%), P<0.001

3D: Three-dimensional, ADR: Adenoma detection rate, CAD; CADe: Computer-aided detection, CNN: Convolutional neural network, PDR: Polyp detection rate, R-CNN: Region-convoluted neural networks, VGG: Visual geometry group