Translational artificial intelligence in gastrointestinal and hepatic disorders: Advancing intelligent clinical decision-making for diagnosis, treatment, and prognosis

Table 5 Summary of artificial intelligence in hepatic disease

Disease	Application	Ref.	Study design	Country/region	Modality	Test set	AI model	Main findings
Hepato-cirrhosis	Diagnosis	Rhyou and Yoo[146]	R	South Korea	US	4950 images	DL	A patch-based DL network for ultrasound cirrhosis diagnosis using synthetic image augmentation, achieving 99.95% accuracy, 100% sensitivity, and 99.9% specificity
	Diagnosis	Luetkens et al[147]	R	Germany	MRI	465 patients	CNN	ResNet50 distinguishes alcoholic from nonalcoholic cirrhosis on MRI: AUC 0.82, 75% accuracy
	Diagnosis	Chang et al[148]	R	United States	Liver biopsy, FibroScan etc.	1370 patients	ML	ML models (especially Random Forest) outperform traditional non-invasive tests (e.g., FibroScan, FIB-4) in identifying significant fibrosis and cirrhosis in NAFLD patients
	Diagnosis	Mazumder et al[149]	R	United States	CT, liver biopsy etc.	351 patients	DL, ML	Combining AI-extracted CT radiomics with routine lab data boosts liver cirrhosis prediction accuracy (AUC 0.84-0.85)
	Diagnosis	Guo et al[150]	P	United Kingdom	NMR spectroscopy	64005 patients	ML	A plasma metabolomics and ML-based nomogram accurately predicts 10-year hepatic cirrhosis complication risk (AUC 0.861), outperforming conventional metrics
Hepatic encephalopathy, HE	Diagnosis	Calvo Córdoba et al[151]	P	Spain	VOG	47 patients	SVM	Automated VOG with SVM detects MHE in 7-10 minutes (93% sensitivity/specificity), outperforming PHES (25-40 minutes)
	Diagnosis	Sparacia et al[152]	R	Italy	MRI	124 patients	ML	MRI radiomics with KNN predicts HE presence (76.5% accuracy); MLP predicts HE severity (≥ stage 2, 94.1%), demonstrating potential for HE diagnosis and staging
	Diagnosis	Chen et al[153]	R	China	MRI	53 patients	SVM	SVM model using gray matter volume discriminates cirrhosis patients with/without MHE at 83.02% accuracy
	Treatment	Liu et al[154]	R	China	TIPS	218 patients	ML	Developed logistic regression model (AUC 0.825) accurately predicts OHE post-TIPS
	Treatment	Zhong et al[155]	R	China	TIPS	207 patients	ANN	ANN model accurately predicts post-TIPS OHE (C-index = 0.863), with 15.9% incidence within 3 months, providing a clinical stratification tool
Liver cancer	Diagnosis	Gao et al[156]	R	China	CECT	723 patients	DL	This model effectively distinguishes malignant liver tumors (HCC, ICC, metastases), achieving 72.6% test-set accuracy and improving physician ICC sensitivity by 26.9%
	Diagnosis	Xu et al[157]	R	China	CT	1049 patients	DL	Swin-Transformer model simplifies LI-RADS classification, effectively distinguishes HCC from non-HCC, and enhances diagnostic performance with clinical data
	Diagnosis	Li et al[158]	R	China	DECT	262 patients	DL	Dual-energy CT deep-learning radiomics nomogram noninvasively predicts HCC MTM subtype, outperforming clinical-radiologic models (AUC 0.87-0.91)
	Diagnosis	Ma et al[159]	R	China	CT, MRI	211 patients	ML	CT/MRI radiomics + clinical features (SVM) achieves highest HCC diagnostic accuracy (82.4%), significantly outperforms single-modality models, and distinguishes HCC vs non-HCC
	Treatment	Hua et al[144]	R	China	CECT	151 patients	ML	Developed and validated a pretreatment CT-based radiomics model predicting response and survival outcomes after triple therapy in unresectable HCC to guide clinical decisions
	Treatment	Xu et al[143]	R	China	CECT	458 patients	DL	The model significantly outperforms single models (externally validated AUC 0.896), effectively distinguishing survival differences (P < 0.001), providing a tool for personalized treatment
	Treatment	An et al[160]	R	China	IATs	2959 patients	ML	Developed MLDSM to risk-stratify unresectable HCC patients undergoing transarterial therapies (alone/combined) for 12-month mortality, guiding clinical treatment decision (e.g. TACE, HAIC)
	Prognosis	Cao et al[161]	R	China	CT, MRI et al	466 patients	DL, ML	Developed pre-/post-op dual-phase DeepSurv model predicting HCC recurrence post-liver transplant; outperformed Milan Criteria (C-index 0.765-0.839), guiding individualized surveillance
	Prognosis	Altaf et al[162]	R	Pakistan	CT, MRI etc.	192 patients	CNN	AI model with tumor size, AFP, and grade predicts post-transplant HCC recurrence risk (validation AUC 0.77)
	Prognosis	Dong et al[163]	R	United States	Clinical data	2038 patients	ML	XGBoost model effectively predicts 1-, 3-, and 5-year survival (AUC > 0.7) in AFP-positive HCC patients, outperforming other algorithms, offering a clinical tool for early intervention
	Prognosis	Yan et al[164]	R	China	MRI	285 patients	CNN	Deep learning-based nomogram integrating imaging, MVI, and tumor number significantly outperforms traditional models in predicting early HCC recurrence (AUC 0.949 vs 0.751)

R: Retrospective; P: Prospective; US: Ultrasound; NMR: Nuclear magnetic resonance; VOG: Video-oculographic; SVM: Support vector machine; PHES: Psychometric hepatic encephalopathy score; MLP: Multilayer perceptron; KNN: K-nearest neighbor; TIPS: Transjugular intrahepatic portosystemic shunt; OHE: Overt hepatic encephalopathy; ANN: Artificial neural network; CT: Computed tomography; CECT: Contrast-enhanced computed tomography; ICC: Intrahepatic cholangiocarcinoma; DECT: Duel-energy computed tomography; IATs: Intra-arterial therapies; TACE: Transarterial chemoembolization; HAIC: Hepatic arterial infusion chemotherapy; MVI: Microvascular invasion; HE: Hepatic encephalopathy; HCC: Hepatocellular carcinoma.