Artificial intelligence technologies for the detection of colorectal lesions: The future is now

Table 1 Ex vivo studies

Ref.	Country	Algorithm	Number of images/videos	Outcomes
Karkanis et al[19], 2003	Greece	Hand-crafted	60 videos	Sensitivity 94%, specificity 99%
Maroulis et al[34], 2003	Greece	Hand-crafted	2809 video frame	Accuracy > 95%
Jerebko et al[35], 2006	United States	Hand-crafted	56 images	Sensitivity 84%
Hwang et al[21], 2007	United States	Hand-crafted	8621 video frame	Per-polyp sensitivity 96%
Park et al[36], 2012	United States	Hand-crafted	35 videos, > 1 million frames	AUROC 0.89
Wang et al[37], 2014	United States	Hand-crafted	46 video file	Sensitivity 81,4%
Bernal et al[38], 2015	Spain	Hand-crafted	612 video frame	PPV 70%
Tajbakhsh et al[39], 2015	United States	Hand-crafted	19400 video frame (property), 300 video frame in CVC-ColonDB	Sensitivity on property database 48%, sensitivity in CVC-ColonDB 88%
Wang et al[20], 2015	United States	Hand-crafted	53 videos	Per-polyp senstivity 97.7%
Geetha et al[40], 2016	India	Hand-crafted	Still images 703 frames	Sensitivity 95%, specificity 97%
Fernández-Esparrach et al[22], 2016	Spain	Hand-crafted	25 videos	Sensitivity 70.4%, specificity 72.4%
Angermann et al[41], 2017	France	Hand-crafted	18 video with 10924 frames	100% per-polyp sensitivity PPV 50%
Park et al[42], 2016	United States	CNN	562 images	Sensitivity 86%, specificity 85%
Billah et al[43], 2017	Bangladesh	CNN	14000 still images	Sensitivity 99%, Specificity 99%
Yu et al[44], 2017	China	CNN	18 videos	Sensitivity 71%, PPV 88%
Zhang et al[23], 2017	China	CNN	150 random + 30 NBI images	Sensitivity 98%, PPV 99%, AUROC 1, Accuracy 86%
Urban et al[25], 2018	United States	CNN	ImageNet 1.2 mil, 53588 imges from videos	90% sensitivity
Misawa et al[24], 2018	Japan	CNN	135 video clips	Per-polyp sensitivity 94%, per-frame sensitivity 90%, specificity 63.3%, accuracy 76.5%
Pogorelov et al[45], 2018	Norway	CNN	1359 to 11954 frame from still images	Sensitivity 75%, specificity 94%
Yamada et al[46], 2018	Japan	CNN	4840 video images	Sensitivity 97%, specificity 99%, AUROC 0.975
Zhu et al[47], 2018	China	CNN	616 still images	Sensitivity 89%, 92% classification accuracy
Hassan et al[26], 2019	Italy	CNN	338 videos, 1.5 milion frames	Sensitivity per lesion 99.7%
Ahmad et al[48], 2019	England	CNN	24596 video frames	Sensitivity 85%, specificity 93%
Eelbode et al[49], 2019	Belgium	CNN	758 frames of still imges	Sensitiviy 92%, specificity 85%
Ka-Luen Lui et al[50], 2019	China	CNN	6 unedited videos	Per-polyp sensitivity 100%, per frame sensitivity 98.3%, specificity 99.7%, AUROC 0.99
Misawa et al[51], 2019	Japan	CNN	64 videos	86% sensitivity
Shichijo et al[52], 2019	Japan	CNN	1233 still images	Per-polyp sensitivity 100%, per-image semsitivity 99%, 76% PPV
Ozawa et al[53], 2020	Japan	CNN	7077 images	92% sensitivity, 86% PPV, accuracy 83%

USA: United States of America; CNN: Convolutional neural network; PPV: Positive predictive value; AUROC: Area under the receiver operating characteristic.

Table 2 Artificial intelligence system country approval

Artificial intelligence system	Country
GI-Genius (Medtronic)	European Union, Australia, Israel, South Arabia
CAD-Eye (Fuji)	European Union
Discovery (Pentax)	European Union
Endobrain-EYE (Olympus)	Japan
Wision-AI	China

Table 3 In vivo randomized control trials characteristics

Ref.	Country	CAD system	CAD system aim	Number of patients		ADR (%)	ADR (%)
				WL	CAD	WL	CAD
Wang et al[15], 2019	China	EndoScreener	Detection	536	522	20.3	28.9
Wang et al[54], 2020	China	EndoScreener	Detection	478	484	28	34.1
Gong et al[30], 2020	China	ENDOANGEL	Quality	318	324	8	16
Repici et al[31], 2020	Italy	GI-Genius	Detection	344	341	40.4	54.8
Liu et al[28], 2020	China	Henan Xuanweitang Medical Information technology Co. Ltd.	Detection	518	508	23.9	39.2
Su et al[29], 2020	China	-	Detection; quality	315	308	16.5	28.9

WL: White light (control group); CAD: Computer aided diagnosis; ADR: Adenoma detection rate.