Application of artificial intelligence in portal hypertension and esophagogastric varices

Table 1 Summary of key artificial intelligence applications in portal hypertension and esophagogastric varices management

Applications	Techniques/methods	Research data/performance metrics
Diagnostic tools
Liver and spleen ultrasound elastography	Ultrasound elastography	LSM and SSM values correlated with HVPG
CT/MRI imaging analysis	CT, MRI, deep learning, radiomics	rHVPG model performance (AUC value), virtual HVPG validation, morphological assessment of varices
Deep learning models (DCNN)	Deep learning	Model AUC value (e.g., 0.9), sensitivity, specificity
Prognostic models
HVPG prediction model	Machine learning, CT radiomics	aHVPG model AUC value (e.g., 0.80)
Variceal bleeding risk prediction	Deep learning	Model AUC value (internal: 0.782, external: 0.789), calibration and decision curve analysis
Treatment selection aids
Endoscopic virtual ruler (ENDOAGGEL)	Deep learning	Accuracy for detecting EV and GV (97.00% and 92.00%)
TIPS post-OHE prediction model	Machine learning	Model accuracy in predicting OHE, comparison with traditional models

CT: Computed tomography; MRI: Magnetic resonance imaging; DCNN: Deep convolutional neural networks; HVPG: Hepatic venous pressure gradient; TIPS: Transjugular intrahepatic portosystemic shunt; OHE: Overt hepatic encephalopathy; LSM: Liver stiffness measurement; SSM: Spleen stiffness measurement; rHVPG: Radiomics signature of hepatic venous pressure gradient; AUC: Area under the curve; aHVPG: Automatic machine learning-based quantitative model; EV: Esophageal varices; GV: Gastric varices.