Harnessing artificial intelligence for the assessment of liver fibrosis and steatosis via multiparametric ultrasound

Table 3 Comparative summary of the main artificial intelligence models applied to liver ultrasound, outlining their key features, strengths, limitations, and representative clinical applications

Model type	Main features	Strengths	Limitations	Typical clinical applications
Convolutional neural networks	Deep-learning models extracting hierarchical image features from B-mode or SWE data	High accuracy in fibrosis staging; automatic feature extraction; excellent for large datasets	Require large training datasets; limited interpretability (“black box”)	Fibrosis staging, steatosis grading, lesion detection
Support vector machines	Supervised ML classifier using kernel-based separation of data	Robust for small datasets; interpretable decision boundaries	Lower performance for complex, high-dimensional data	Early fibrosis detection, ML radiomics, feature selection
Random forest	Ensemble ML algorithm combining multiple decision trees	Handles mixed data (imaging + clinical); resistant to overfitting	Limited ability to capture image texture; less suitable for pixel-level analysis	Integration of US features with clinical and laboratory data
Generative adversarial networks	DL models using generator-discriminator structure	Effective for data augmentation; improves synthetic image realism and model generalizability	Computationally demanding; risk of instability during training	Image synthesis, dataset expansion, quality enhancement
Hybrid/multimodal models	Combine DL image-based features with ML classifiers or clinical variables	Capture complementary information; improve diagnostic precision	Require harmonized data and complex implementation	Comprehensive multiparametric liver assessment (fibrosis + steatosis)

DL: Deep learning; ML: Machine learning; SWE: Shear wave elastography; UC: Ultrasound.