Scoping out the future: The application of artificial intelligence to gastrointestinal endoscopy

Table 1 Characteristics of randomized trials applying computer-aided detection to colonoscopy

Ref.	Training/validation datasets	Testing datasets	AI system	ADR with AI (%)	ADR without AI (%)	Withdrawal time with AI (min)	Withdrawal time without AI (min)
Wang et al[2], 2019	5545 images from 1290 colonoscopy videos performed in China. Images were labeled by endoscopists. Training: 4495 images. Validation: 1050 images.	CVC-ClinicDb: 612 image frames of polyps from 29 colonoscopy videos performed in Spain. Polyp location manually annotated by endoscopists. 27113 images from 1138 colonoscopy videos performed in China. 20% contained histologically confirmed polyps. Videos of 138 histologically confirmed polyps from 110 patients in China. 54 full-length colonoscopy videos from 54 patients in China.	CNN based on SegNet architecture.	29	20	6.9	6.4
Wang et al[23], 2020				34	28	7.5	7.0
Liu et al[24], 2020				29	21	6.6	6.7
Repici et al[19], 2020	Based on data from previous clinical trial[74]. Videos of 2684 histologically confirmed polyps from 840 patients in Europe and the US. Training and validation: 2346 polyps from 735 patients. Testing: 338 polyps from 105 patients.		GI-Genius, Medtronic; CNN, details not available.	55	40	7.0	7.3
Gong et al[20], 2020	All images were obtained from colonoscopies of > 5000 patients in China. Trained 3 DCNNs on still images: DCNN 1: 3264 in-vitro, 10180 in-vivo, and 4230 unqualified images used to train the system to determine whether a scope was inside or outside the body. 1000 images per category used for testing. DCNN 2: 5189 images of the cecum and 5630 non-cecum images used to train the system to identify the cecum. 500 images per category used for testing. DCNN 3: 2602 clear images, 1877 images in cleansing process, and 1899 blurry images used to train the system to recognize slipping. 200 images per category used for testing. k-fold cross-validation procedure was implemented with k = 10.		DCNN 1-3 trained and tested in four independent convolutional neural networks: VGG16[75], DenseNet-169[76], ResNet-50[77], Inception-v3[78].	16	8	6.4	4.8
Liu et al[21], 2020	151 videos containing endoscopist-confirmed polyps and 384 polyp-negative videos from colonoscopies in China. Training and validation: 101 polyp-positive cases and 300 polyp-negative cases. Testing: 46 polyp-positive cases and 88 polyp-negative cases.		CADe system, Henan Xuanweitang Medical Information Technology; 3-dimensional CNN.	39	24	6.8	6.7
Su et al[22], 2020	23612 images from colonoscopies of > 4000 patients in China. Images were labeled by 2 endoscopists. Training: 15951. Validation: 3681. Testing: 3980. 5 DCNN models were created to time the withdrawal phase, supervise withdrawal stability, evaluate bowel preparation, and detect colorectal polyps in real time.		Model B, based on AlexNet architecture[79]. BP based on ZFNet[80] and Model PD YOLO V2[81]. Model E developed using a DCNN with one fully connected layer.	29	17	7.0	5.7

AI: Artificial intelligence; ADR: Adenoma detection rate; CADe: Computer-aided detection; CNN: Convolutional neural networks; DCNN: Deep convolutional neural network; GI: Gastrointestinal.