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.