Artificial intelligence in gastroenterology practice: Current perspectives and emerging prospects

Table 7 Studies evaluating applications of artificial intelligence in liver diseases

Ref.	Clinical application	Data type	AI method	Sample size	Study design, center design, and validation design	Major performance metric	Principal limitation
Chang et al[106]	NAFLD fibrosis assessment	Demographic and clinical features	Random forest, logistic regression, and ANN	1370 patients, training 1096 and testing 274	Multicenter cohort, internal validation	AUC 0.89 for cirrhosis	Requires further clinical validation for widespread use
Preechathammawong et al[107]	NAFLD fibrosis and steatosis assessment	Digital histopathological images, H&E biopsy	SMART AI-PATHO, DL algorithms	146 hepatic tissue specimens	Cross-sectional analytic, multicenter observational, comparison with pathologist interpretations	Agreement 68.4%, kappa 0.59 for all staging, 89.7% agreement for advanced fibrosis	Cannot assess lobular inflammation, ballooning, or variations in staining techniques
Zheng et al[108]	Liver fibrosis staging	Portal venous CT imaging	DRCDS, DL segmentation and classification with decision model	819 patients in the development set and 175 patients in external test sets	Retrospective multicenter study, internal test set and 3 external test sets	AUC 0.89-0.92 (internal test), Obuchowski index 0.81-0.85 (external validation), reduced radiologist evaluation requirement by 73.7%-92.0% while maintaining/improving sensitivity and specificity	Retrospective design, potential sampling errors in the biopsy reference standard
Xue et al[109]	Liver fibrosis assessment	Grayscale and elastogram ultrasound images	Transfer learning with Inception-V3	466 patients undergoing partial hepatectomy, training 364 and test 102	Retrospective diagnostic study, single-center, random-split internal test set	AUC 0.950 (95%CI: 0.917-0.972) for Scheuer stage S4	Uneven distribution of fibrosis stages
Ai et al[110]	Liver fibrosis assessment	Ultrasound radiofrequency signals and B-mode images	2D CNN for ROI analysis and 1D CNN for classification	613 participants for ROI, 237 participants for classification	Feasibility study, single-center, internal test sets	AUC 0.957 for ≥ METAVIR F1 (phase-spectrum test set)	Limited sample size and single-scanner diversity
Zha et al[111]	Liver fibrosis assessment	MRI and clinical variables	ResUNet for segmentation and CoRC logistic regression model	595 patients with biopsy-proven liver fibrosis	Retrospective multicenter cohort, internal and external validation	AUC 0.79-0.82 (internal and external test sets)	Retrospective study design
Wang et al[112]	Liver fibrosis in HBV	2D shear-wave elastography	DL radiomics with CNN	398 patients, training 266 and validation 132	Prospective multicenter study, internal and hospital-cross validation	AUC 0.97 (95%CI: 0.94-0.99) for METAVIR F4 (validation set)	Accuracy sensitive to the number of acquisitions
Wei et al[113]	Focal liver lesion characterization	Multiphase enhanced CT imaging	Liver Lesion Network	4039 patients	Multicenter development study, external-center validation, and clinical verification	AUC 97.2% for benign vs malignant lesions (external centers)	Primarily intended to aid areas with limited radiologist availability
Sharma et al[114]	Hepatitis C detection	Standard biochemical blood parameters	Stacked meta-model with random forest and XGBoost, with pre-clustering	869 patients	Hybrid dataset from NHANES and UCI, multi-source, holdout validation	Accuracy 94.82% (holdout set)	Lacks validation on real-world datasets
Ercan et al[115]	Autoimmune hepatitis assessment	H&E and Sirius red biopsy slides	CNN pipeline for segmentation and detection	123 pairs of biopsy slides (H&E and Sirius red) from 116 patients	Diagnostic tool development, single-center, independent 20% test set	Accuracy 99.4% for tissue detection (test set)	Relies solely on images without clinical data
Gerussi et al[116]	Autoimmune hepatitis vs primary biliary cholangitis	H&E digitized biopsy slides	Transformer-based attention-based multiple instance learning with ALNE	446 patients providing a dataset of 561 digitized whole-slide images	Multicenter diagnostic study, internal cross-validation and external validation	AUC 0.81 (external validation)	Limited sample size and focus on Western Europe
Park et al[117]	Alcohol-associated liver disease vs metabolic dysfunction-associated steatotic liver disease	Fecal 16S rRNA sequencing data	CNN with principal component analysis	674 subjects (464 for internal validation and 210 for external validation)	Multicenter screening signature development, internal cross-validation and external validation	AUC > 0.90 for alcohol-associated liver disease (external validation)	Metagenomic signatures are not universally established
Wang et al[118]	Drug-induced liver injury vs autoimmune hepatitis	24 routine laboratory parameters	Gradient boosting decision tree	2554 patients	Multicenter cohort, external and prospective validation	AUROC 0.91 (95%CI: 0.900-0.928) (external validation)	Web-based tool requires clinical implementation
Calderaro et al[119]	HCC vs intrahepatic cholangiocarcinoma and combined hepatocellular-cholangiocarcinoma	H&E whole-slide images	Self-supervised ResNet50 with attention-based multiple instance learning	1356 cases	Multicenter phenotyping and reclassification study, internal cross-validation and external TCGA validation	AUROC 0.94 (external TCGA validation)	No ground truth for biphenotypic reclassification
Huang et al[120]	Metabolic dysfunction-associated steatohepatitis	44 clinical variables and 250 serum metabolites	Multi-step ML	616 patients (311 in the derivation cohort and 305 in the validation cohort)	Multicenter development study across different countries, external validation in Finnish cohort	AUROC 0.87 (derivation cohort)	Requires serum metabolite measurements by NMR
Guo et al[121]	Prediction of cirrhosis complications	168 circulating metabolites and clinical variables	Integrated nomogram	64005 participants from the UK Biobank, training 43734, validation 20271	Multicenter UK Biobank tool-development study, internal and prospective evaluation	AUC 0.930 for 3-year risk (training and validation cohort)	Relies on metabolites not routinely measured
Njei et al[122]	High-risk metabolic dysfunction-associated steatotic liver disease	Demographic, clinical and laboratory data	XGBoost with SHAP	5281 subjects from the NHANES database	Multicenter observational national survey, internal holdout validation	AUC 0.95 (95%CI: 0.91-0.97) (holdout test set)	Lacked liver biopsy data for comparison
Kim et al[123]	Prediction of hepatic decompensation	MRE stiffness and clinical variables	Multivariable Cox regression	1254 participants with NAFLD from an international cohort	Multicenter cohort, internal random-split validation	C-statistic 0.876 for 5-year risk (validation cohort)	Requires MRE and has retrospective design
Nakatsuka et al[124]	HCC prediction in steatotic liver disease	H&E whole-slide images	CNN with EfficientNetB0	685 patients	Retrospective multicenter prediction study, external validation using nonpaired cases	AUC 0.84 (95%CI: 0.72-0.96) (external validation cases)	Retrospective design and focus on a Japanese population
Zhang et al[125]	Early HCC prediction	Gene expression profiles using secreted genes and relative expression ordering	Hybrid model with relative expression ordering, feature selection, and SVM	5586 tissue samples	ML signature development using public databases, independent validation sets	Accuracy 98.34% (independent validation)	Primarily based on public databases
Sharma et al[126]	Graft injury after liver transplantation	Demographic, clinical and laboratory data	GraftIQ with multiclass neural network and data fusion	5759 biopsies	Multicenter diagnostic study, internal test set and external validation	AUC 0.934 (95%CI: 0.909-0.959) (external UHN validation set)	Exclusion of biopsies with dual diagnoses

AI: Artificial intelligence; NAFLD: Nonalcoholic fatty liver disease; ANN: Artificial neural network; AUC: Area under the curve; H&E: Hematoxylin and eosin; DL: Deep learning; CT: Computed tomography; DRCDS: Deep learning-radiologist complementarity decision system; CI: Confidence interval; CNN: Convolutional neural network; ROI: Region of interest; MRI: Magnetic resonance imaging; ResUNet: Residual U-Net; CoRC: Concordance receiver operating characteristic; HBV: Hepatitis B virus; XGBoost: Extreme gradient boosting; NHANES: National Health and Nutrition Examination Survey; UCI: University of California, Irvine; ALNE: Autoimmune liver neural estimator; rRNA: Ribosomal ribonucleic acid; AUROC: Area under the receiver operating characteristic curve; HCC: Hepatocellular carcinoma; TCGA: The Cancer Genome Atlas; ML: Machine learning; NMR: Nuclear magnetic resonance; SHAP: SHapley Additive exPlanations; MRE: Magnetic resonance elastography; SVM: Support vector machine; UHN: University Health Network; ResNet50: Residual network 50-layer.