Synergistic applications of artificial intelligence and organoid technology in gastric precancerous lesion research: Mechanisms, translation, and challenges

Table 1 Diagnostic performance of artificial intelligence systems for gastric cancer and precancerous lesions

No.	Research focus	Ref.	Modality	Study design	Sample size/dataset	AI model/system	Gold standard	Key performance metrics	Results	Comparison with endoscopists
1	EGC diagnosis	Chen et al[20]	WLE, NBI	Systematic review (12 studies)	11685 cases	Various	Pathology	Pooled sensitivity, specificity, AUC	Sensitivity: 0.86 (95%CI: 0.75-0.92); specificity: 0.90 (95%CI: 0.84-0.93); AUC: 0.94	Not compared
2	Upper GI tumors	Arribas et al[21]	NBI	Meta-analysis (19 studies)	Not specified	Various	Pathology	Overall sensitivity, specificity, AUC	Sensitivity: 90%; specificity: 89%; AUC: 0.95	Not compared
3	Gastric neoplasia	Lui et al[22]	WLE, NBI	Systematic review and meta-analysis (23 studies)	969318 images	Various	Pathology	AUC	AUC: 0.96	Superior (AUC 0.98 vs 0.87, P < 0.001)
4	GPLs diagnosis	Dilaghi et al[23]	Not Specified	Systematic review and meta-analysis (4 studies)	Not specified	Various	Pathology	Accuracy	Accuracy: 90.3%	Not compared
5	CAG diagnosis	Shi et al[24]	Not specified	Systematic review and meta-analysis (8 studies)	25216 patients, > 90000 images	Various	Pathology	Pooled sensitivity, specificity, AUC	Sensitivity: 94%; specificity: 96%; AUC: 0.98	Significantly higher accuracy
6	CAG diagnosis	Zhang et al[25]	Not specified	Diagnostic study	5470 antral images	CNN	Pathology	Accuracy, sensitivity, specificity	Accuracy: 0.942; sensitivity: 0.945; specificity: 0.940	Exceeded three experts
7	CAG diagnosis	Shi et al[26]	Not specified	Diagnostic study	Not specified	GAM-efficient net	Pathology	Accuracy	External image: 93.5%; video: 92.37%	Outperformed endoscopists
8	GIM diagnosis	Yan et al[27]	Not specified	Diagnostic study	Not specified	Intelligent diagnostic system	Pathology	AUC, sensitivity, specificity, accuracy	AUC: 0.928; sensitivity: 91.9%; specificity: 86.0%; Accuracy: 88.8%	Not compared
9	Mucosal lesion DDx	Nam et al[28]	Not specified	Diagnostic study	Not specified	AI-DDx	Pathology	AUROC	AUROC: 0.86	Comparable to experts (0.89, P = 0.12); Superior to novices and intermediates
10	CAG and IM diagnosis	Lin et al[29]	WLE	Multicenter diagnostic study	7037 images (14 hospitals)	CNN	Pathology	AUC, Accuracy	CAG: AUC 0.98, Acc 96.4%; IM: AUC 0.99, Acc 97.6%	Not compared
11	Atrophy and IM detection	Yang et al[31]	WLE, LCI	Diagnostic study	21420 images	Novel DL method	Pathology	Accuracy	Atrophy: 97.12%; IM: 99.18%	Not compared
12	GA and IM diagnosis	Xu et al[32]	Image-enhanced endoscopy	Multicenter diagnostic study	6250 images, 98 videos (5 hospitals)	ENDOANGEL (DCNN)	Pathology	Accuracy	GA: 86.4%; IM: 85.9%	Comparable to experts; Superior to non-experts
13	Precursor detection	Xu et al[33]	Not specified	Prospective single-center clinical trial	Not specified	Not specified	Pathology	Detection Rate	IM: 14.23% vs 9.15%; atrophy: 22.76% vs 17.28%	Effect more pronounced in junior physicians
14	Invasion depth	Nam et al[28]	EUS	Diagnostic study	Not specified	AI-ID	Post-operative histology	AUROC	AUROC: 0.73	Superior to EUS experts (0.56, P < 0.001)
15	Cancer vs ulcer	Namikawa et al[36]	Not specified	Diagnostic study	Not specified	A-CNN	Pathology	Sensitivity, specificity, accuracy	Sensitivity: 99.0%; specificity: 93.3%; accuracy: 95.9%	Not compared
16	GISTs vs leiomyomas	Dong et al[37]	EUS	Multicenter diagnostic study	Not specified	Real-time AI-assisted EUS system	Pathology/histology	AUC, accuracy	AUC: 0.948; accuracy: 91.7%	Significantly outperformed
17	Pathology analysis	Yang et al[40]	Macroscopic specimen	Diagnostic study	Gastric cancer surgery specimens	AI algorithm	Histology	mAP, Accuracy	Lesion localization mAP: 95.90%; LN metastasis prediction Acc: 75.00%	Not compared
18	IM scoring	Iwaya et al[41]	Pathology slides	Diagnostic study	Not specified	AI system	Expert pathologist	Scoring difference	Difference with pathologists: 7.6%	Identified missed foci by pathologists

AI: Artificial intelligence; AUC: Area under the curve; CAG: Chronic atrophic gastritis; CNN: Convolutional neural network; DCNN: Deep CNN; DDx: Differential diagnosis; DL: Deep learning; EGC: Early gastric cancer; EUS: Endoscopic ultrasound; GA: Gastric atrophy; GIM: Gastric intestinal metaplasia; GISTs: Gastrointestinal stromal tumors; GPLs: Gastric precancerous lesions; IM: Intestinal metaplasia; LN: Lymph node; mAP: Mean average precision; WLE: White light endoscopy; NBI: Narrow-band imaging; AUROC: Area under the receiver operating characteristic curve; LCI: Linked color imaging endoscopy; GI: Gastrointestinal; CI: Confidence interval; Acc: Accuracy.