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