Artificial intelligence-assisted colonoscopy: A review of current state of practice and research

Table 1 Summary of the randomized controlled trials involving computer-aided detection for colonoscopy

Ref.	Year	Study design	Study aim	CADe system	Image modality	Number of patients in the CADe group	Number of patients in the control group	Number of polyps (CADe vs control group)	Adenoma detection rate (%) (CADe vs control group)	Polyp detection rate (%) (CADe vs control group)	Number of false-positive rate (%) (CADe vs control group)	Withdrawal time (CADe vs control group), min ± SD; minute
Wang et al[36]	2019	Non-blinded prospective randomised controlled study	To investigate whether a high-performance real-time CADe system can increase polyp and adenoma detection rates in the real clinical setting	The real-time automatic polyp detection system (Shanghai Wision AI Co., Ltd.) based on artificial neural network-SegNet architecture	Real-time Video stream	522	536	767 (498 vs 269)	29.1 vs 20.3; P < 0.001; 95%CI = 1.21-2.135	45.0 vs 29.1; P < 0.001; 95%CI = 1.532-2.544	39 vs 0	6.18 ± 1.38 vs 6.07 ± 1.11; P = 0.15
Wang et al[74]	2020	Double-blind Prospective randomised trial	To assess the effectiveness of a CADe system for improving detection of colon adenomas andpolyps; to analyse the characteristics ofpolyps missed by endoscopists	The real-time automatic polyp detection system (Shanghai Wision AI Co., Ltd.) based on artificial neural network-SegNet architecture	Real-time Video stream	484	478	809 (501 vs 308)	34.0 vs 28.0; P = 0.030; OR = 1.36, 95%CI = 1.03–1.79	52.0 vs 37.0; P < 0.0001; OR = 1.86, 95%CI = 1.44–2.41	48 in CADe group (control group not reported)	6.48 ± 1.32 vs 6.37 ± 1.09; P = 0.14
Su et al[75]	2020	Single-blind Prospective randomised trial	To develop an automatic quality control system; to investigate whether the system could increase the detection of polyps and adenomas in real clinical practice	Five deep learning convolutional neural networks (DCNNs) based on AlexNet, ZFNet, and YOLO V2	Real-time Video stream	308	315	273 (177 vs 96)	28.9 vs 16.5; P < 0.001; OR = 2.055, 95%CI = 1.397-3.024	38.3 vs 25.4; P = 0.00; OR = 1.824, 95%CI = 1.296-2.569	62 in CADe system (control group not reported)	7.03 ± 1.01 vs 5.6 ± 1.26; P < 0.001
Gong et al[76]	2020	Single-blind Prospective randomised trial	To evaluate whether the CADe system could improve polyp yield during colonoscopy	ENDOANGEL based on the deep neural networks and perceptual hash algorithms	Real-time video stream	355	349	302 (178 vs 124)	16 vs 8; P = 0.001; OR = 2.30, 95%CI = 1.40-3·77	47 vs 34; P = 0.0016; OR = 1.69, 95%CI = 1.22-2.34	For endoscope being inside = 0.8; For identification of the caecum = 2; for prediction of slipping = 0	6.38 ± 2·48 vs 4.76 ± 254; P < 0.0001
Liu et al[77]	2020	Double-blind Prospective randomised trial	To study the impact of CADe system on the detection rateof polyps and adenomas in colonoscopy	The convolutional threedimensional (3D) neural network	Real-time video stream	508	518	734 (486 vs 248)	39.1 vs 23.9; P < 0.001; OR = 1.637, 95%CI = 1.201‑2.220	43.7 vs 27.8; P < 0.001; OR = 1.57, 95%CI = 1.586‑2.483	36 in CADe system (control group not reported)	6.82 ± 1.78 vs 6.74 ± 1.62; P < 0.001
Luo et al[78]	2021	Non-blinded Prospective randomised trial	To explore whether CADe could improve the polyp detection rate in the actual clinical environment	A CNN algorithm based on a YOLO network architecture	Real-time Video stream	150	150	185 (105 vs 80)	38.7 vs 34.0; P < 0.001	-	52 in CADe system (control group not reported)	6.22 ± 0.55 vs 6.17 ± 0.52; P = 0.102
Repici et al[79]	2020	Singles-blind Prospective randomised trial	To assess the safety and efficacy of a CADe system for the detection of colorectal neoplasia	The CNN (GI-Genius; Medtronic)	Real-time Video stream	341	344	596 (353 vs 243)	54.8 vs 40.4; P < 0.001; RR = 1.30, 95%CI = 1.14-1.45	279/341 (82) 214/344 (62)	-	417 ± 101 seconds for the CADe group vs 435 ± 149 for controls; P = 0.1
Wang1 et al[80]	2020	Singles-blind Prospective randomised trial	To investigate the impact of CADe on adenoma miss and detection rate	The artificial neural network (EndoScreener, Shanghai Wision AI Co,Ltd, Shanghai, Chin)	Real-time Video stream	184 (CADe-routine group)2	185 (Routine-CADe group)3	529 (244 vs 285)	42.39 vs 35.68; P = 0.186; OR = 1.327, 95%CI = 0.872–2.018	63.59 vs 55.14; P = 0.09; OR = 1.421, 95%CI = 0.936–2.157	67 in CADe system (control group not reported)	6.55 (5.34–7.77) vs 6.51 (5.45–7.57); P = 0.7454

¹The total adenoma miss rate by computer-assisted detection system (CADe) [colonoscopy = 13.89%, 95% confidence interval (CI) = 8.24%–19.54%]; by routine colonoscopy = 40.00%, 95%CI=31.23%–48.77%, P < 0.0001. The total polyp miss rate by CADe colonoscopy = 12.98%, 95%CI = 9.08%–16.88%; by routine colonoscopy = 45.90%, 95%CI = 39.65%–52.15%, P < 0.0001). Visible adenoma miss rate: Routine-CADe group = 24.21% vs CADe-routine group = 1.59%, P < 0.001; Visible polyp miss rate: Routine-CADe group = 30.89% vs CADe-routine group = 2.36%; P < 0.001.

²It means that the colonoscopy was performed by the CADe system and then the conventional method.

³It means that the colonoscopy was performed by the conventional method and then the CADe system.

⁴Median (interquartile range).

CADe: Computer-assisted detection system; CNN: Convolutional neural network; DCNN: Deep learning convolutional neural network; SD: Standard deviation; OR: Odds ratio; RR: Relative risk; CI: Confidence interval.