Artificial intelligence in gastroenterology: A narrative review

Table 2 Overview of findings from studies evaluating the detection accuracy of computer-aided detection for gastric cancer

Ref.	Country	Study design	AI classifier	Lesions	Training dataset	Test dataset	Sensitivity (%)	Specificity (%)	Accuracy (%)	AUROC
Miyaki et al[47], 2013	Japan	Prospectivea	SVM	Gastric cancer	493 FICE-derived magnifying endoscopic images	92 FICE-derived magnifying endoscopic images	84.8	97	85.9	-
Kanesaka et al[48], 2018	Japan, Taiwan	Retrospective	SVM	EGC	126 M-NBI images	81 M-NBI images	96.7	95	96.3	-
Wu et al[50], 2019	China	Retrospective	CNN	EGC	9151 images	200 images	94	91	92.5	-
Cho et al[51], 2019	South Korea	Ambispective	CNN	Advanced gastric cancer, EGC, high grade dysplasia, low grade dysplasia, non-neoplasm	4205 WLE images	812 WLE images; 200 WLE images	-	-	86.6b; 76.4	0.877b
Tang et al[49], 2020	China	Retrospective	CNN	EGC	35823 WLE images	Internal: 9417 WLE images; External: 1514 WLE images1	95.51; 85.9-92.1	81.71; 84.4-90.3	87.81; 85.1-91.2	0.941; 0.887-0.925
Namikawa et al[52], 2020	Japan	Retrospective	CNN	Gastric cancer	18410 images	1459 images	99	93.3	99	-
Horiuchi et al[56], 2020	Japan	Retrospective	CNN	EGC	2570 M-NBI images	258 M-NBI images	95.4	71	85.3	0.852
Horiuchi et al[57], 2020	Japan	Retrospective	CNN	EGC	2570 M-NBI images	174 videos	87.4	82.8	85.1	0.8684
Guo et al[54], 2021	China	Retrospective	CNN	Gastric cancer, erosions/ulcers, polyps, varices	293162 WLE images	33959 WLE images	67.52; 85.1	70.92; 90.3	-	-
Ikenoyama et al[55], 2021	Japan	Retrospective	CNN	EGC	13584 WLE and NBI images	2940 WLE and NBI images	58.4	87.3	-	-
Hu et al[58], 2021	China	Retrospective	CNN	EGC	M-NBI images from 170 patients	Internal: M-NBI from 73 patients External: M-NBI images from 52 patients	79.23; 78.2	74.53; 74.1	773; 76.3	0.8083; 0.813
Ueyama et al[59], 2021	Japan	Retrospective	CNN	EGC	5574 M-NBI images	2300 M-NBI	98	100	98.7	-
Yuan et al[53], 2022	China	Retrospective	CNN	EGC, advanced gastric cancer, submucosal tumor, polyp, peptic ulcer, erosion, and lesion-free gastric mucosa	29809 WLE images	1579 WLE images	59.24; 100	99.34; 98.1	93.54; 98.4	-

^aPresumed prospective based on manuscript.

^bAccuracy of convolutional neural network (CNN) for detecting the five different lesions and detecting gastric cancer respectively. Area under the receiver operating characteristic (AUROC) pertains to detecting gastric cancer.

¹The external dataset was comprised of images from 3 external sites. Sensitivity, specificity, accuracy and AUROC for the internal dataset and external dataset respectively.

²Sensitivity and specificity of CNN for detecting gastric cancers in a dataset comprised of images without annotations and for detecting gastric cancers in a dataset comprised of annotated images respectively.

³Sensitivity, specificity, accuracy and AUROC for the internal dataset and external dataset respectively.

⁴Sensitivity, specificity and accuracy for detecting early gastric cancer and for detecting advanced gastric cancer respectively.

“Internal” and “External” refer to internal and external datasets respectively.

CNN: Convolutional neural network; AUROC: Area under the receiver operating characteristic; EGC: Early gastric cancer; FICE: Flexible spectral imaging color enhancement; M-NBI: Magnifying endoscopy with narrow band imaging; NBI: Narrow band imaging; SVM: Support vector machine; WLE: White light endoscopy.