Updates on the clinical use of artificial intelligence in upper gastrointestinal diseases: A comprehensive literature review

Table 3 Summary of literature for gastroesophageal varices

Disease	Ref.	AI Architecture	Training/validation cohort	Targeted endpoint	Key AI performance metrics	Performance compared to humans
Varices	[33]	Deep CNN (ENDOANGEL, real-time endoscopic AI system)	8566 variceal images (3021 patients) + 6152 normal images (3168 patients)	Detection and characterization of GEV	EV detection accuracy 97.0%; GV detection accuracy 92.0%	Endoscopists: EV detection 93.94% (P < 0.01); GV detection 84.43% (P < 0.05)
	[34]	Explainable AI platform (ENDOANGEL-GEV; multiple deep learning models for segmentation, RC classification, and variceal grading)	Testing dataset: 1351 EV + 811 GV varices; validation dataset: 2402 EV + 12383 GV varices; prospective dataset: 161 patients	GEV detection, grading, and risk stratification	EV detection sensitivity 90.79% (validation); RC classification EV accuracy 94.62%; EV grading accuracy 94.57%; risk stratification EV accuracy 97.69% (prospective)	Prospective comparison — endoscopists: RC classification EV 66.92% (P < 0.001); EV grading 75.97% (P < 0.001)
	[35]	Deep learning — EfficientNet V2 (best of 6 CNN/transformer-based models; transfer learning from ImageNet; Grad-CAM visualization)	Training: 675 images (439 control, 236 bleeding) augmented to 2000; validation: 400 images; external test: 400 images (200 control, 200 bleeding)	12-month EV bleeding risk prediction	Validation: Accuracy 0.910, F1 0.905; external test: Accuracy 0.893, precision 0.911, F1 0.890	Endoscopist #1: Accuracy 0.800; endoscopist #2: Accuracy 0.763; With AI assistance: Accuracy improved to 0.938 (+17.3%) and 0.908 (+19.0%), respectively
	[36]	Multimodal machine learning: DL image models (EfficientNet best; CNN + transformer-based) combined with clinical structured data via stacking ensemble (H2O AutoML; DL, XGBoost, RF, GBM, GLM)	810 EV images augmented to 2400 (2000 training, 400 validation); 341 patients with clinical data; external test dataset from second center	12-month EV bleeding prediction	Test dataset (stacking model): Accuracy 0.932, sensitivity 0.952, F1 0.879; AUC 0.975	N/A
	[37]	Machine learning (Random Forest algorithm) using clinical variables (EVendo score)	Training: 238 patients; validation: 109 patients (both from 3 hospitals)	Prediction of EV and VNT	EV: AUROC 0.84 (training), 0.82 (validation); VNT: AUROC 0.74 (training), 0.75 (validation)	N/A
	[38]	Machine learning-derived clinical score (EVendo score; external validation)	External validation cohort: 103 patients (66 with GEV, 12 with VNT)	Prediction of GEV and VNT	AUROC for GEV 0.75; AUROC for VNT 0.59; EVendo ≤ 3.90: NPV 94% for VNT, spared 20.4% of EGDs, missed VNT 19%	N/A
	[39]	Deep learning (DeepLabV3+ CNN) for automated CT liver/spleen segmentation and volumetry; spleen volume-to-platelet ratio index	DL segmentation algorithm trained on 813 labeled CT datasets; clinical study: Derivation 239 patients, validation 180 patients; Follow-up endoscopy: 219 patients	Detection of high-risk esophageal varices	Derivation AUC 0.835; validation cutoff > 3.78: Sensitivity 69.4%, specificity 78.5%; cutoff > 1.63: 100% sensitivity	N/A
	[40]	Machine learning-based CT RM using portal venous-phase enhanced CT (liver, spleen, esophagus ROIs)	Training/internal validation: 458 patients (218 mild EV, 240 HREV); external validation: 499 patients (159 mild EV, 340 HREV)	Diagnosis of high-risk esophageal varices	HREV RM — internal validation: AUC 0.834, sensitivity 0.977, specificity 0.969; external validation: AUC 0.736, sensitivity 0.690, specificity 0.762	N/A
	[41]	Machine learning (Random Forest, AdaBoost, SVM, linear regression) using CT morphology + serum biomarkers + clinical features	224 patients (5-fold cross-validation)	Prediction of esophageal variceal bleeding	Test set — random forest AUC 0.818; AdaBoost AUC 0.804; linear regression AUC 0.763	N/A

RM: Radiomics model; AI: Artificial intelligence; CNN: Convolutional neural network; GEV: Gastroesophageal varices; EV: Esophageal variceal; GV: Gastric varices; RC: Red color; AUC: Area under the curve; VNT: Varices needing treatment; AUROC: Areas under the receiver-operating-characteristics curves; NPV: Negative predictive value; EGD: Esophagogastroduodenoscopy; DL: Deep learning; CT: Computed tomography; ROIs: Regions of interest; HREV: High-risk oesophageal varices.