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

Table 1 Studies that evaluated the artificial intelligence ability to stage liver fibrosis[40,42-57,110,111]

Ref.	Type of study	Population	Number of patients	AI technique employed	Main results
Ruan et al[43]	Retrospective, multi-center	HBV	508	MSTNet DL	High accuracy in detecting both moderate (≥ F2) and advanced (F4) liver fibrosis, outperforming conventional clinical tools (APRI, FIB-4 and Forns) and human sonographers
Song et al[42]	Retrospective, single-center	HBV	93	ANNs DL	Excellent predictive capability to stage liver fibrosis and superior to serum fibrosis tests
Zhang et al[44]	Retrospective, multi-center	HBV	1500	CNNs DL	High-frequency images outperformed low-frequency ones across all trained CNNs models, as well as FIB-4, APRI and SWE in staging liver fibrosis
Duan et al[45]	Retrospective, two-center	CLD	434	GAN model DL	Good performances in staging liver fibrosis. Good predictive accuracy in identifying liver cirrhosis
Miura et al[55]	Retrospective, single-center	CLD	517	CNNs DL	Higher diagnostic accuracy than human scoring for detecting significant fibrosis (≥ F2)
Li et al[40]	Prospective, single-center	Chronic HBV infection	144	Adaptive boosting, random forest, SVM ML	ML algorithms improve the accuracy of liver fibrosis assessment. Combining conventional radiomics, ORF and CEMF data with ML algorithms enhances accuracy in detecting significant liver fibrosis
Durot et al[46]	Retrospective	CLD or elevated liver enzymes	204	SVM ML	SVM ML algorithm demonstrated excellent diagnostic accuracy in distinguishing significant liver fibrosis (≥ F2) when applied to both p-SWE and 2D-SWE data from two different systems, compared with MRE
Gatos et al[47]	Prospective, single-center	54 healthy patients, 31 with CLD	85	ML	Good accuracy in distinguishing healthy individuals from patients with CLD
Gatos et al[48]	Retrospective	56 healthy patients, 70 with CLD	126	ML	Good accuracy in distinguishing healthy individuals from patients with CLD combining different cluster features
Destrempes et al[49]	Retrospective, cross-sectional	CLD (HBV, HCV, NAFLD, AIH)	82	ML	Combining QUS and p-SWE in an ML model enhanced accuracy in staging fibrosis, inflammation and steatosis
Wang et al[51]	Prospective, multi-center	HBV	398	Dlre	Dlre outperformed 2D-SWE in detecting cirrhosis and advanced fibrosis. It was more reliable than biomarkers (FIB-4, APRI) to identify all fibrosis stages
Lu et al[52]	Retrospective, multi-center	CLD	807	Dlre2.0	Dlre2.0 achieved a higher AUC than Dlre for significant fibrosis, but without statistical significance
Kagadis et al[50]	Retrospective	88 healthy individuals, 112 with CLD	200	GoogLeNet, AlexNet, VGG16, ResNet50, DenseNet201 DL	All pre-trained DL networks achieved good to excellent performance in staging liver fibrosis, outperforming radiologists. ResNet50 and DenseNet201 showed high accuracy across all fibrosis stages
Xue et al[54]	Retrospective	Local liver lesions treated by partial hepatectomy	466	Inception-V3 network (DL), TL	Gray scale US images and 2D-SWE images analyzed with Inception-V3 (DL) using the TL achieved excellent performance in staging liver fibrosis
Brattain et al[53]	Retrospective	NAFLD	328	Random forest, SVM ML; CNN DL	CNN demonstrated the highest performance in distinguishing liver fibrosis as significant or not
Zhou et al[57]	Retrospective	94 patients with liver fibrosis; 143 patients with liver fibrosis and liver steatosis	237	iANN, DL	Radiomics with iANN-based homodyned-K US imaging outperformed both the standalone iANN method and radiomics on uncompressed US data for liver fibrosis assessment
Park et al[110]	Retrospective, multi-center	Patients underwent to liver biopsy or hepatectomy	933	DL (VGGNet, ResNet, DenseNet, EfficientNet, ViT)	Deep CNNs accurately staged liver fibrosis by METAVIR score from B-mode US images. EfficientNet showed the best performance among models
Lee et al[111]	Retrospective, multi-center	Healthy individuals and patients with CLD	838	DCNN, DL	DCNN accurately assessed METAVIR score from US images and outperformed radiologists in diagnosing cirrhosis in simulated US examination

AI: Artificial intelligence; HBV: Hepatitis B virus; CLD: Chronic liver disease; HCV: Hepatitis C virus; NAFLD: Nonalcoholic fatty liver disease; AIH: Auto-immune hepatitis; MSTNet: Multi-scale texture network; DL: Deep learning; ML: Machine learning; ANN: Artificial neural network; CNN: Convolutional neural network; GAN: Generative adversarial network; SVM: Support vector machines; Dlre: Deep learning-based radiomics of two dimensional shear wave elastography; GoogLeNet: Google inception network; AlexNet: AlexNet convolutional neural network; VGG: Visual geometry group; ResNet50: Residual network 50 layers; DenseNet201: Densely connected convolutional networks 201; TL: Transfer learning; iANN: Improve artificial neural network; ViT: Vision transformer; DCNN: Deep convolutional neural network; APRI: Aspartate aminotransferase to platelet ratio index; SWE: Shear wave elastography; FIB-4: Fibrosis-4; CEMF: Contrast-enhanced micro-flow; ORF: Original radiofrequency; p-SWE: Point shear wave elastography; 2D-SWE: Two dimensional shear wave elastography; MRE: Magnetic resonance elastography; QUS: Quantitative ultrasound; AUC: Area under the curve; UC: Ultrasound.

Table 2 Studies that evaluated the artificial intelligence ability to stage liver steatosis[49,68,73-75,112-120]

Ref.	Type of study	Population	Number of patients	AI technique employed	Main results
Fujii et al[112]	Prospective, cross-sectional	MASLD	486	DL (U-net)	DL-based segmentation reliably identified the surface irregularity of the liver
Drazinos et al[113]	Retrospective, monocentric	MASLD	112	DL (Inception-V3, MobileNetV2, ResNet50, DenseNet201 and NASNet mobile)	DenseNet201 achieved the highest overall performance, while Inception-V3 showed superior accuracy in the binary classification of steatosis
Chou et al[114]	Retrospective	Healthy patients and patients with liver steatosis	2070	DL	DL models achieved higher 88.7% sensitivity for mild steatosis and consistent accuracy across all grades (normal 91.8%, moderate 77.3% moderate, severe 84.4%)
Vianna et al[115]	Retrospective	Healthy patients and patients with liver steatosis	199	DL (VGG16, ResNet50 and Inception-V3)	DL–based analysis of B-mode US images demonstrated diagnostic performance comparable to expert human readers in both the detection and grading of hepatic steatosis
Vianna et al[116]	Retrospective, multi-center	Patients with suspected hepatic steatosis datasets	Not specified	DL	Diagnostic AUC for steatosis detection increased from 0.78 to 0.97. Test-time adaptation improved DL models robustness and generalizability B-mode US
Cao et al[117]	Prospective, cross-sectional	Healthy patients and patients with liver steatosis	240	DL	The methods showed a good ability (AUC > 0.7) to identify steatosis, particularly in distinguishing moderate from severe (AUC = 0.958)
Han et al[73]	Prospective	Healthy individuals and patients with NAFLD	204	CNN DL	Accurate diagnosis of NAFLD and fat quantification using US radiofrequency signals
Byra et al[74]	Prospective	Steatosis and/or obese patients	55	DL (Inception ResNet-v2)	The AI-based model performed best (AUC = 0.977) outperforming the hepatorenal sonographic index (not significant) and grey-level co-occurrence matrix (significant difference)
Constantinescu et al[75]	Retrospective	Healthy patients and patients with liver steatosis	60	DL (Inception-V3 and VGG-16)	DL algorithms demonstrated excellent diagnostic performance, achieving accuracy rates exceeding 90%
Jeon et al[68]	Prospective	Suspected steatosis	173	DL	DL algorithm combining QUS parametric maps with B-mode imaging accurately estimated hepatic fat fraction and reliably diagnosed hepatic steatosis
Gómez-Gavara et al[118]	Prospective	Livers from brain-dead donors, evaluated during the procurement phase	192 livers	ML	Integrating ML with liver texture and color analysis smartphone images enables highly accurate estimation of hepatic steatosis severity
Santoro et al[119]	Prospective, cross-sectional	Healthy patients and patients with liver steatosis	134	ML	AI application enhances both the diagnostic accuracy and efficiency of US in the assessment of hepatic steatosis
Kaffas et al[120]	Retrospective, single center	Healthy patients and patients with liver steatosis	403	DL	This DL algorithm achieved accurate estimation of hepatic fat fraction and reliable diagnosis of hepatic steatosis
Destrempes et al[49]	Prospective	CLD	82	ML (random forest)	Random Forest integration of QUS and SWE markedly enhanced diagnostic vs SWE alone, particularly for steatosis assessment, increasing AUC by 25%-50%

AI: Artificial intelligence; CLD: Chronic liver disease; NAFLD: Nonalcoholic fatty liver disease; MASLD: Metabolic dysfunction-associated steatotic liver disease; DL: Deep learning; ResNet50: Residual network 50 layers; DenseNet201: Densely connected convolutional networks 201; VGG: Visual geometry group; CNN: Convolutional neural network; ML: Machine learning; QUS: Quantitative ultrasound; AUC: Area under the curve; SWE: Shear wave elastography; UC: 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.