Artificial intelligence in colonoscopy

Table 1 Colorectal polyp detection

Ref.	Study design	Algorithm type	Dataset	Results
Karkanis et al[8]	Retrospective	CADe (Wavelet Decomposition)	180 images	Sensitivity: 93.6%
				Specificity: 99.3%
Urban et al[2]	Retrospective	CADe (DCNN)	8461 images &20 colonoscopy videos	Accuracy: 96.4%
				False Positive: 7%
Klare et al[12]	ProspectiveIn vivo	CADe	55 colonoscopies	ADR of: CAD 29.1% and Endoscopist 30.9%
Wang et al[5]	Non-blinded RCT	CADe using Shanghai Wision Al Co. Ltd. (DCNN)	Randomized 522 patients to CADe and 536 to control group	ADR of CAD 29.1% vs control 20.3%
Wang et al[4]	Double blinded RCT	CADe using EndoScreener (DCNN)	Randomized 484 patients to CAD and 478 to sham system	ADR of CAD 34% vs control 28%
Gong et al[13]	Partially blinded RCT	CADe using ENDOANGEL (DCNN)	Randomized 355 patients to CAD and 349 to control	ADR of CAD 16% vs control 8%
Repici et al[14]	Partially-blinded RCT	CADe using GI-Genius (CNN)	Randomized 341 patients to CAD and 344 to control	ADR of CAD 54.8% vs control 40.4%
Liu et al[15]	Non-blinded RCT	CADe using Henan Xuanweitang Medical Information Technology Co. Ltd (convolutional 3D network)	Randomized 508 patients to CAD and 518 control	ADR of CAD 39% vs control 23%
Su et al[16]	Partially blinded RCT	Automatic quality control system (ACQS)(DCNN)	Randomized 308 patients to AQCS and 315 to control	ADR of AQCS 28.9% vs control 16.5%

CADe: Computer-aided detection; CAD: Computer-aided diagnosis; DCNN: Deep convolutional neural network; ADR: Adenoma detection rate.