Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions

Table 4 Summary of artificial intelligence studies on the detection and characterization of early gastric cancer[101-108,110,112-117,119,122]

Ref.	Purpose	Design	Training set	Test set	IEE	AI model	Performance
Zhu et al[122]	ID prediction of EGC	Retrospective	790 i	203 i	WLI	ResNet-50	Acc: 89%; Se: 76%; Sp: 96%; AUC: 0.94
Horiuchi et al[112]	AI vs exp in EGC Det	Retrospective	2570 i	174 v	ME-NBI	GoogLeNet	Acc: 85%; Se: 95%; Sp: 71%; AUC: 0.87
Wu et al[108]	Det of EGC	Multicenter RCT	NA	1050 v	WLI	ENDOANGEL	Acc: 85%; Se: 100%; Sp: 84%
Wu et al[107]	Det of EGC	RCT	NA	1812 v	WLI	ENDOANGEL-LD	(Decrease) miss rate (AI 6% vs endoscopists 27% RR = 0.22)
Wu et al[117]	AI vs exp in ID and DS of EGC	Prospective multicenter	1131 i	100 v	ME-NBI	ENDOANGEL	ID: Acc: 79% vs 64%; DS: Acc: 71% vs 64%
Wu et al[116]	Real time Det of EGC	Prospective single center trial	9824 i	2010 v	WLI	ENDOANGEL-LD	Acc: 92%; Se: 92%; Sp: 92%; PPV: 25%; NPV: 100%
Ueyama et al[103]	Det of EGC	Retrospective	5574 i	2300 i	ME-NBI	ResNet-50	Acc: 99%; Se: 99%; Sp: 98%
He et al[115]	Det of EGC	Retrospective multicenter	4667 i	4702 i; 187 v	ME-NBI	ENDOANGEL-ME	Acc: 90%; Se: 93%; Sp: 94%
Li et al[110]	AI vs exp in EGC Det	Retrospective	1630 i	267 i; 77 v	ME-NBI	ENDOANGEL-LA	i: Acc: 89%; Se: 86%; Sp: 92%; v: Acc: 87%; Se: 84%; Sp: 88%
Tang et al[114]	Det of EGC	Retrospective multicenter	13151 i	1577 i; 20 v	NBI	YOLOv3	Acc: 93%; AUC: 0.95
Jin et al[113]	Det of EGC AI vs exp	Prospective	5708 i	1425 i; 10 v	WLI; NBI	Mask R-CNN	Acc: 90%; Se: 91%; Sp: 89%
Gong et al[106]	Real-time Det and ID prediction of EGC	RCT	5017 i	2524 v	WLI	CDSS	DR: 96%; ID Acc: 86%; lesion class: Acc: 82%
Lee et al[101]	EGC pathological Ch	Retrospective	4336 i; 153 v	436 i; 89 v	WLI	ENAD CAD-G	Acc: UH: 90%; SMI: 88%; LVI: 88%; LNM: 93%
Chang et al[119]	Classification of EGC	Retrospective real-world data	21918 i	6785 i; 296 v	WLI	ENAD CAD-G	i: Acc: EGC: 82%; dysplasia: 88%; v: Acc: EGC: 88%; dysplasia: 91%
Zhao et al[102]	Det of EGC LCI vs WLI	Retrospective	9021 i	116 v	WLI; LCI	CADe	(Increase) Se: LCI: 94% vs WLI: 79%; Sp: 93% in both
Lee et al[101]	Det of EGC	Retrospective	30000 i	500 i	WLI	CADe (ALPHAON®)	Acc: 88%; Se: 93%; Sp: 87%; AUC: 0.96
Soong et al[105]	Raman spectroscopy for EGC risk strat	RCT	NA	25 v	WLI	SPECTRA IMDx™	Acc: 100%; Se: 80%; Sp: 92%
Feng et al[104]	AI vs exp/nonexp in EGC Det	Prospective	12000 i	1289 i; 130 v	WLI	DCNN	Se: 97%; Sp: 89%; AUC: 0.93

EGC: Early-stage gastric cancer; ID: Invasion depth; AI: Artificial intelligence; IEE: Image-enhanced endoscopy; Det: Detection; exp: Expert endoscopists; nonexp: Nonexpert endoscopists; RCT: Randomized controlled trial; i: Images; v: Videos; DS: Differentiation status; Ch: Characterization; LCI: Linked-color imaging; WLI: White light imaging; strat: Stratification; NA: Not applicable; NBI: Narrow-band imaging; ME: Magnified endoscopy; ResNet: Residual network; GoogLeNet: Google network; LD: Lesion detection; LA: Logical anthropomorphic diagnostic system; YOLO: You only look once; R-CNN: Region-based convolutional neural networks; CDSS: Clinical decision support system; ENAD: Endoscopy neural assisted detection; CAD-G: Computer-aided detection for gastric lesions; CADe: Computer aided detection; DCNN: Deep convolutional neural network; Acc: Accuracy; Se: Sensitivity; Sp: Specificity; AUC: Area under the curve; RR: Relative risk; NPV: Negative predictive value; PPV: Positive predictive value; UH: Undifferentiated histology; SMI: Submucosal invasion; LVI: Lympho vascular invasion; LNM: Lymph node metastasis.