Artificial intelligence for early prediction of alcohol-related liver disease: Advances, challenges, and clinical applications

Table 1 Key studies on artificial intelligence in alcohol-related liver disease prediction

Study focus	Methodology	Key findings	Citation
Multi-omics integration	Machine learning with transcriptomics and proteomics data	90% accuracy for liver tissue classification, 89% for PBMCs	(Listopad et al[4], 2024) (Listopad et al[3], 2022)
Gut microbiota-based diagnosis	Supervised ML algorithms with feature reduction techniques	AUCs > 0.90 for ALD and NAFLD diagnosis	(Park et al[5], 2024)
Gradient boosting for mortality	Gradient boosting with laboratory and microbiome data	AUC of 0.87 for 30-day mortality prediction, outperforming MELD score	(Gao et al[2], 2022)
Proteomic panels for fibrosis	Machine learning on liver and plasma proteomics data	AUC of 0.90 for detecting F2 or greater fibrosis	(Mezzacappa and Bhat[8], 2023)
Bayesian optimization for classifiers	Bayesian optimization of Random Forest and XGBoost	81.06% accuracy for liver disease prediction	(Kumar and Rani[7], 2024)
Extra Tree for early detection	Extra Tree algorithm with oversampling techniques	92% accuracy for early liver disease detection	(Lima et al[10], 2024)

ALD: Alcoholic-associated liver disease; AUC: Area under the curve; ML: Machine learning; NAFLD: Non-alcoholic fatty liver disease; PBMCs: Peripheral blood mononuclear cells. MELD: Model for end-stage liver disease; XGBoost: eXtreme Gradient Boosting.