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.
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Open Access
Review Article
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Open Access
Original Research
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Drug-induced liver injury (DILI) is one of the most challenging forms of liver disorder. We aimed to use ultrasound dual elastography, by combining strain and shear wave imaging, to noninvasively assess liver inflammation and injury severity of DILI.
291 DILI patients were included in the prospective multicenter study and divided into training and validation cohorts. All patients received liver biopsy and dual elastography examination. Liver inflammation grading (G0-4) and fibrosis staging (F0-4) were considered as the gold standard of liver injury and G+F ≥ 5 was defined as severe liver injury. Indexes of dual elastography and serological indicators (DESI) were selected and analyzed with multivariable logistic regression to build DESI models for evaluating liver inflammation, and the C score model was built with the same method for diagnosing severe liver injury.
Areas under the receiver operating characteristic curve (AUCs) of the DESI model to assess liver inflammation ≥ G2 were 0.887 and 0.868 in training and validation cohorts, respectively. AUCs of the DESI model in diagnosing ≥ G3 were 0.893 and 0.896 in the two cohorts, respectively. The C score accurately assessed severe liver injury with AUCs of 0.909 and 0.885 in two cohorts. Of the 87 patients with mild clinical severity, 10 (11.49%) had severe pathological injury, which could be identified by C score.
Dual elastography demonstrated high performance in diagnosing liver inflammation and identifying severe pathological liver injury of DILI, making up for the deficiency of serological indicators alone for evaluating DILI severity.
Open Access
Review Article
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Interventional ultrasound (IUS) is an important branch of modern minimally invasive medicine that has been widely applied in clinical practice due to its unique techniques and advantages. As a relatively emerging field, IUS has progressed towards standardization, precision, intelligence, and cutting-edge directions alone with more than 40 years of development, which is becoming increasingly important techniques in clinical medicine. This article will briefly review the development and advancement of IUS for diagnosis and treatment in China in the era of precision medicine from the aspects of artificial intelligence, virtual navigation, molecular imaging, and nanotechnology.
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