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

Table 2 Summary of artificial intelligence studies on the detection and characterization of premalignant and early esophageal squamous cell carcinoma[49-54,66,67,70]

Ref.	Purpose	Design	Training set	Test set	IEE	AI model	Performance
Horie et al[67]	Det of early ESCC	Retrospective	8428 i	1118 i	WLI; NBI	SSD	Acc/Se: 98%; NPV: 95%; PPV: 40%
Ohmori et al[70]	Det of early ESCC	Retrospective	11283 non-ME i; 11279 ME i	523 non-ME i; 204 ME i	WLI; NBI; BLI	SSD	Non-ME Acc: 79%; Se: 95%; Sp: 69%; ME: Acc: 77%; Se: 98%; Sp: 56%
Fukuda et al[66]	AI vs exp in early ESCC Det and Ch	Retrospective	17274 i	144 v	NBI; BLI	SSD	Det: Acc: 63%; Se: 91%; Sp: 51%; Ch: Acc: 88%; Se: 86%; Sp: 89%
Yang et al[52]	AI vs exp in Det of early ESCC	Retrospective	22994 i	222 i; 104 v	WLI; NBI; BLI	YOLOv3; ResNet-v2	Acc: 99%; Se: 100%; Sp: 99%
Yuan et al[51]	Prediction of ID in early ESCC	Retrospective multicenter	7094 i	1589 i	NBI	HRNet; OCRNet	Acc: 91%; ID prediction: 74%
Tani et al[50]	Real-time Det of early ESCC	Prospective single center trial	25048 i	237 i	WLI; NBI	ResNet-101	Acc: 81%; Se: 68%; Sp: 83%
Li et al[49]	Det of early ESCC and precancerous lesions	RCT	26543 i	3117 i	WLI; NBI	ENDOANGEL-ELD	Acc: 98%; Se: 90%; Sp: 98%
Ma et al[54]	Det + optical biopsy for early ESCC	Prospective	25056 i	2442 i; 187 v	WLI; pCLE	iCLE inception-ResNet-v2	Acc: 98%; Se: 95%; Sp: 99%
Aoyama et al[53]	AI vs exp/nonexp in early ESCC Det	Prospective	280 v	115 v	NBI	YOLOv3	Acc: 77%; Se: 76%; Sp: 79%

Det: Detection; Ch: Characterization; ESCC: Esophageal squamous cell carcinoma; AI: Artificial intelligence; ID: Invasion depth; IEE: Image-enhanced endoscopy; exp: Expert endoscopists; nonexp: Nonexpert endoscopists; RCT: Randomized controlled trial; i: Images; v: Videos; ME: Magnified endoscopy; WLI: White light imaging; NBI: Narrow-band imaging; BLI: Blue-laser imaging; pCLE: Probe-based confocal laser endomicroscopy; SSD: Single shot multi-box detector; YOLO: You only look once; ResNet: Residual network; HRNet: High-resolution network; OCRNet: Object-contextual representations network; ELD: Esophageal lesion detection; iCLE: Integrated confocal laser endomicroscopy; Acc: Accuracy; Se: Sensitivity; Sp: Specificity; NPV: Negative predictive value; PPV: Positive predictive value.