The integration of artificial intelligence (AI) into ultrasound medicine has revolutionized medical imaging, enhancing diagnostic accuracy and clinical workflows. This review focuses on the applications, challenges, and future directions of AI technologies, particularly machine learning (ML) and its subset, deep learning (DL), in ultrasound diagnostics. By leveraging advanced algorithms such as convolutional neural networks (CNNs), AI has significantly improved image acquisition, quality assessment, and objective disease diagnosis. AI-driven solutions now facilitate automated image analysis, intelligent diagnostic assistance, and medical education, enabling precise lesion detection across various organs while reducing physician workload. AI’s error detection capabilities further enhance diagnostic accuracy. Looking ahead, the integration of AI with ultrasound is expected to deepen, promoting trends in standardization, personalized treatment, and intelligent healthcare, particularly in underserved areas. Despite its potential, comprehensive assessments of AI’s diagnostic accuracy and ethical implications remain limited, necessitating rigorous evaluations to ensure effectiveness in clinical practice. This review provides a systematic evaluation of AI technologies in ultrasound medicine, highlighting their transformative potential to improve global healthcare outcomes.

Cerebrovascular diseases pose a serious threat to human survival and quality of life and represent a major cause of human death and disability. Recently, the incidence of cerebrovascular diseases has increased yearly. Rapid and accurate diagnosis and evaluation of cerebrovascular diseases are of great importance to reduce the incidence, morbidity and mortality of cerebrovascular diseases. With the rapid development of medical ultrasound, the clinical relationship between ultrasound imaging technology and the diagnosis and treatment of cerebrovascular diseases has become increasingly close. Ultrasound techniques such as transcranial acoustic angiography, doppler energy imaging, three-dimensional craniocerebral imaging and ultrasound thrombolysis are novel and valuable techniques in the study of cerebrovascular diseases. In this review, we introduce some of the new ultrasound techniques from both published studies and ongoing trials that have been confirmed to be convenient and effective methods. However, additional evidence from future studies will be required before some of these techniques can be widely applied or recommended as alternatives.

Background: Ultrasound is ideal for displaying intracranial great vessels but not intracranial microvessels and terminal vessels. Even with contrast agents, the imaging effect is still unsatisfactory. In recent years, significant theoretical advances have been achieved in super-resolution imaging. The latest commonly used ultrafast plane-wave ultrasound Doppler imaging of the brain and microbubble-based super-resolution ultrasound imaging have been applied to the imaging of cerebral microvessels and blood flow in small animals such as mice but have not been applied to in vivo imaging of the cerebral microvessels in monkeys and larger animals. In China, preliminary research results have been obtained using super-resolution imaging in certain fields but rarely in fundamental and clinical experiments on large animals. In recent years, we have conducted a joint study with the Xi'an Jiaotong University to explore the application and performance of this new technique in the diagnosis of cerebrovascular diseases in large animals.Objective: To explore the characteristics and advantages of microbubble-based super-resolution ultrasound imaging of intracranial vessels in rhesus monkeys compared with conventional transcranial ultrasound.Methods: First, the effectiveness and feasibility of the super-resolution imaging technique were verified by modular simulation experiments. Then, the imaging parameters were adjusted based on in vitro experiments. Finally, two rhesus monkeys were used for in vivo experiments of intracranial microvessel imaging.Results: Compared with conventional plane-wave imaging, super-resolution imaging could measure the inner diameters of cerebral microvessels at a resolution of 1 mm or even 0.7 mm and extract blood flow information. In addition, it has a better signal-to-noise ratio (5.625 dB higher) and higher resolution (~30-fold higher). The results of the experiments with rhesus monkeys showed that microbubble-based super-resolution ultrasound imaging can achieve an optimal resolution at the micron level and an imaging depth >35 mm.Conclusion: Super-resolution imaging can realize the monitoring imaging of high-resolution and fast calculation of microbubbles in the process of tissue damage, providing an important experimental basis for the clinical application of non-invasive transcranial ultrasound.

Brain tumors have a poor prognosis. Early, accurate diagnosis and treatment are crucial. Although brain surgical biopsy can provide an accurate diagnosis, it is highly invasive and risky and is not suitable for follow-up examination. Blood-based liquid biopsies have a low detection rate of tumor biomarkers and limited evaluation ability due to the existence of the blood-brain barrier (BBB). The BBB is composed of brain capillary endothelial cells through tight junctions, which prevents the release of brain tumor markers to the human peripheral circulation, making it more difficult to diagnose, predict prognosis, and evaluate therapeutic response through brain tumor markers than other tumors. Focused ultrasound (FUS)-enabled liquid biopsy (sonobiopsy) is an emerging technique using FUS to promote the release of tumor markers into the circulatory system and cerebrospinal fluid, thus facilitating tumor detection. The feasibility and safety data from both animal models and clinical trials support sonobiopsy as a great potential in the diagnosis of brain diseases.

Immune Regulatory Genes Are Major Genetic Factors to Behcet Disease: Systematic Review