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Artificial Intelligence in Ultrasound Diagnosis of Liver Nodules: A Comprehensive Review of B-Mode and Contrast-enhanced Applications
Received date: 2025-10-05
Revised date: 2025-10-25
Accepted date: 2025-11-15
Online published: 2025-11-06
Copyright
Ultrasound is one of the most commonly used imaging modalities for the screening and diagnosis of liver nodules. However, its diagnostic accuracy is highly dependent on operator expertise, and atypical or small lesions are prone to missed diagnosis or misdiagnosis. In recent years, artificial intelligence (AI) has achieved remarkable progress in medical image analysis, offering novel solutions to improve the objectivity, accuracy, and efficiency of liver ultrasound diagnosis. This review systematically summarizes the current status and advances of AI in the ultrasound diagnosis of liver nodules, with a focus on B-mode and contrast-enhanced ultrasound (CEUS). We detail AI applications in automatic nodule detection and localization, benign–malignant differentiation, multi-class classification (e.g., hepatocellular carcinoma [HCC], cholangiocarcinoma [CCA], hemangioma [HH], metastasis [HM]), and prediction of key pathological biomarkers (e.g., microvascular invasion [MVI], pathological grading, Ki-67, vessels encapsulating tumor clusters [VETC]), analyzes the current research status and summarizes the main limitations of existing studies. By reviewing methodological characteristics such as cohort size, validation strategies, and machine learning algorithms, this paper provides insights into future research directions and promotes the development of clinically translatable AI models, with the ultimate goal of advancing standardization and broad clinical adoption of AI-assisted diagnosis in liver ultrasound.
Yu Xiao jie , Song Zheng lai , Chang Xue yong , Yu Jie , Liang Ping . Artificial Intelligence in Ultrasound Diagnosis of Liver Nodules: A Comprehensive Review of B-Mode and Contrast-enhanced Applications[J]. ADVANCED ULTRASOUND IN DIAGNOSIS AND THERAPY, 2025 , 9(4) : 326 -346 . DOI: 10.26599/AUDT.2025.250098
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