Artificial intelligence in gastroenterology: A narrative review

Table 3 Overview of findings from studies evaluating the detection accuracy of computer-aided detection for colonic polyps

Ref.	Country	Study design	Lesions	Training dataset	Test dataset	Sensitivity (%)	Specificity (%)	Accuracy (%)	AUROC
Komeda et al[139], 2017	Japan	Retrospective	Adenomas	1200 images	10 images	80	60	70	-
Misawa et al[140], 2018	Japan	Retrospective	Polyps	411 video clips	135 video clips	90	63.3	76.5	0.87
Wang et al[149], 2018	China, United States	Retrospective	Polyps	4495 images	Dataset A: 27113 images; Dataset C: 138 video clips; Dataset D: 54 full-length videos	Dataset A: 94.38; Dataset C: 91.64	Dataset A: 95.92; Dataset D: 95.4	-	Dataset A: 0.984
Horiuchi et al[154], 2019	Japan	Prospective	Diminutive polyps	-	a	80	95.3	91.5	-
Hassan et al[141], 2020	Italy, United States	Retrospective	Polyps	-	338 video clips	99.7	-	-	-
Guo et al[142], 2021	Japan	Retrospective	Polyps	1991 images	100 video clips; 15 full videos	87b	98.3b	-	-
Neumann et al[143], 2021	Germany	Retrospective1	Polyps	> 500 videos	240 polyps within full-length videos	100	0	-	-
Li et al[144], 2021	Singapore	Retrospective	Polyps	6038 images	2571 images	74.1	85.1	-	-
Livovsky et al[151], 2021	Israel	Ambispective	Polyps	3611 h of videos	1393 h of videos	97.1	0	-	-
Pfeifer et al[158], 2021	Germany, Italy, Netherlands	Retrospective	Polyps	10467 images	45 videos	90	80	-	0.92
Ahmad et al[145], 20222	England	Prospective	Polyps	Dataset A: 58849 frames; Dataset B: 10993 videos and still images	Dataset C: 110985 frames; Dataset D: 8950 frames; Dataset E: 542484 frames	Dataset C: 100, 84.1; Dataset D&E: 98.9, 85.2	Dataset C: 79.6; Dataset D&E: 79.3%
Hori et al[146], 2022	Japan	Prospective	Polyps	1456 images	600 images	97	97.7	97.3	-
Pacal et al[152], 2022	Turkey	Retrospective	Polyps	Used images from 3 publicly available datasets (SUN, PICCOLO, Etis-Larib) to create training and test datasets		91.04	-	-	-
Yoon et al[184], 2022	South Korea	Retrospective	SSL	4397 images	Validation Set 2106; SSL Temporal Validation set 133	95.44; 93.89	90.1	92.95	0.96
Nemoto et al[185], 2022	Japan	Retrospective	TA, SSL	1849 images	400 images	72	89	82	0.86
Lux et al[148], 2022	Germany	Retrospective	Polyps	506338 images	41 full-length videos	-	-	95.3	-

^aTested CADe in a cohort of 95 patients.

^bPer-frame analysis from full-length video dataset.

¹Presumed retrospective based on manuscript.

²Sensitivity is reported as per-polyp and per-frame respectively. Specificity is reported as per-frame.

AUROC: Area under the receiver operating characteristic; SSL: Sessile serrate lesion; TA: Tubular adenoma. All studies used a convolutional neural network to classify images.