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

Table 3 Summary of artificial intelligence studies on the detection and characterization of gastric premalignant conditions[81,83-90,92,94-100]

Ref.	Purpose	Design	Training set	Test set	IEE	AI model	Performance
Zhang et al[92]	Real-time Det of gastric polyps	RCT	708 i	50 i	WLI	SSD-GPNet	mAP: 90%; PDR by > 10%
Zhang et al[99]	Det of AG	Retrospective	3829 i	1641 i	WLI; i-scan	CNN-CAG	Acc: 94%; Se: 95%; Sp: 94%
Xu et al[85]	AI vs exp/nonexp in Det of GPMC	Retrospective multicenter	5198 i	1052 i; 98 v	ME-NBI; ME-BLI	ENDOANGEL	AG: Acc: 86% (i); 88% (v); GIM: Acc: 86% (i); 90% (v)
Lin et al[90]	Det of AG/GIM	Retrospective multicenter	2193 i	273 i	WLI	TResNet	AG: Acc: 96%; AUC: 0.98; Se: 96%; Sp: 96%; GIM: Acc: 98%; AUC: 0.99; Se: 98%; Sp: 97%
Watanabe et al[100]	Det of gastric indefinite for dysplasia lesions	Retrospective	2961 i	248 i	WLI	SSD + miR148a DNA methylation	AUC: 0.93 (exp) > 0.83 (AI + miR148a) > 0.59 (trainees)
Kodaka et al[95]	AG Det and OLGA staging in patients w/Helicobacter pylori infection	Retrospective	11497 i	7724 i	WLI	ResNet-50	AUC: 0.75 (AI + Kyoto) > 0.67 (AI + OLGA) > 0.66 (AI alone)
Zhao and Chi[98]	Severity classification of AGAI vs endoscopists	Prospective	2922 i	268 v	NBI	UNet	(Increase) DR of mod AG (16% vs 8%) and severe AG (7% vs 3%); (decrease) unnecessary Bx
Zhao et al[89]	Det of AGAI vs endoscopists	Prospective case-control	4175 i	676 v	NBI	UNet	Acc: 91% vs 72%; Se: 84% vs 63%; Sp: 97% vs 82%; AUC: 0.91 vs 0.74
Yang et al[81]	Det of AG/GIM	Retrospective	21420 i	5355 i	WLI; LCI	SE-ResNet	AG: Acc: 97%; Se: 99%; Sp: 95%; GIM: Acc: 99%; Se: 99%; Sp: 99%
Li et al[96]	Severity classification of GIM	Retrospective	837 i	278 i	NBI; LCI	CDCN	Acc: 84%
Shi et al[88]	Det of AG	Retrospective	6216 i	600 i; 118 v	WLI	GAM-EfficientNet	i: Acc: 94%; Se: 93%; Sp: 94%; v: Acc: 92%; Se: 96%; Sp: 89%
Iwaya et al[87]	Det of GIM and OLGIM staging	Retrospective	5753 i	1150 i	HE slides	ResNet-50	Se: 98%; Sp: 95%; OLGIM stage III/IV classified in 18%
Fang et al[86]	AI vs pathologists in Det and grading of AG/GIM	Prospective multicenter	1745 i	545 i	Pathology slides	GasMIL	(Increase) pathologists’ performance (AUC: 0.95 vs 0.88)
Tao et al[97]	AG Det and risk stratification vs exp	Retrospective	5856 i	869 i; 119 v	WLI	UNet ++ ResNet-50 ENDOANGEL	(Increase) Se vs exp i: 93% vs 77%; v: 95% vs 86%
Niu et al[94]	GIM grading and OLGIM staging	Retrospective multicenter	470 i	333 i	ME-NBI	Faster R-CNN	Pred high-risk stage Acc: 84%
Zou et al[83]	Det of Helicobacter pylori infection AI vs endoscopists	Multicenter RCT	7377 i	2080 i	WLI	EfficientNet	Acc: 93% vs 76%; Se: 92% vs 79%; Sp: 93% vs 75%
Xu et al[84]	AI vs exp/nonexp in Det of AG/GIM	Single center RCT	NA	1968 v	WLI	ENDOANGEL	(Increase) DR of AG: 23% vs 17%; (Increase) DR of GIM: 14% vs 9%

AI: Artificial intelligence; IEE: Image-enhanced endoscopy; Det: Detection; exp: Expert endoscopists; nonexp: Nonexpert endoscopists; RCT: Randomized controlled trial; i: Images; v: Videos; AG: Atrophic gastritis; GPMC: Gastric premalignant conditions; GIM: Gastric intestinal metaplasia; OLGA: Operative link on atrophic gastritis assessment; AGAI: Atrophic gastritis artificial intelligence; OLGIM: Operative link for gastric intestinal metaplasia assessment; w: With; WLI: White light imaging; NBI: Narrow-band imaging; BLI: Blue-laser imaging; ME: Magnified endoscopy; HE: Hematoxylin-eosin; SSD: Single shot multi-box detector; GPNet: Gated pyramid network; CNN: Convolutional neural networks; CAG: Chronic atrophic gastritis; TResNet: Transformer-residual network; ResNet: Residual network; UNet: U-shaped network; R-CNN: Region-based convolutional neural networks; mAP: Mean average precision; PDR: Polyp detection rate; Acc: Accuracy; Se: Sensitivity; Sp: Specificity; AUC: Area under the curve; DR: Detection rate; Bx: Biopsies; mod: Moderate; LCI: Linked-color imaging; CDCN: Color-guided deformable convolution network; Pred: Prediction; NA: Not applicable.