Diabetic peripheral neuropathy (DPN) is one of the most common chronic complications of diabetes, which can lead to neuropathic pain, foot ulcers, and even disability, and greatly reduces survival. Therefore, early diagnosis and prevention of DPN is of great importance to reduce symptoms and disability rate. Ultrasound elastography is a noninvasive method to evaluate changes in nerve tissue composition by obtaining the elastic modulus of tissue and visually displaying the stiffness in the form of images. This paper summarizes the application progress of ultrasound elastography in the evaluation of peripheral neuropathy in recent years, in order to provide reference for the future clinical application of large samples.

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.

The American College of Radiology has implemented the Liver Imaging Reporting and Data System (LI-RADS) to help detect, interpret, and guide the management of suspected lesions on surveillance imaging for hepatocellular carcinoma (HCC) in patients with cirrhosis. The classification of indeterminate nodules with a grading algorithm can be used for multiple imaging modalities (US, CT, and MRI) and incorporates multiple imaging features to appropriately classify observations with different likelihood of being HCC. Contrast-enhanced ultrasound (CEUS) LI-RADS has been fully implemented since 2017. The aim of this pictorial article is to provide a comprehensive review of CEUS LI-RADS utilization, discuss its advantages, and highlight areas for potential improvement.

Real-time intelligent segmentation of ultrasound video object is a demanding task in the field of medical image processing and serves as an essential and critical step in image-guided clinical procedures. However, obtaining reliable and accurate medical image annotations often necessitates expert guidance, making the acquisition of large-scale annotated datasets challenging and costly. This presents obstacles for traditional supervised learning methods. Consequently, semi-supervised learning (SSL) has emerged as a promising solution, capable of utilizing unlabeled data to enhance model performance and has been widely adopted in medical image segmentation tasks. However, striking a balance between segmentation accuracy and inference speed remains a challenge for real-time segmentation. This paper provides a comprehensive review of research progress in real-time intelligent semi-supervised ultrasound video object segmentation (SUVOS) and offers insights into future developments in this area.

Early detection of vascular disease is fundamental to the prevention and treatment of systemic vascular lesions. The timely identification of vascular damage can be achieved by comprehensively assessing the structural anomaly and/or functional degeneration of the vasculature. The assessment may to some extent indicate the long-term detrimental effects of cardiovascular disease (CVD) risk factors on vascular health. A key aspect in the evaluation of vascular function is the measurement of arterial stiffness. In 2012, the arterial velocity-pulse index (AVI) and arterial pressure-volume index (API) were introduced, which are noninvasively measured with a brachial cuff, and can reflect the status of arterial stiffness in both the aorta and the brachial artery. A large number of relevant studies have demonstrated the strong associations between AVI/API and various CVD risk factors, underlining the substantial relevance of the indices in CVD risk assessment. In this review, we provide a systematic review of the progresses made in brachial cuff-based measurements of arterial stiffness. In addition, we summarize the results of the recent studies focused on exploring the associations of AVI/API with relevant risk factors as well as their roles in CVD assessment.

In the digital age, the miniaturization of portable ultrasound equipment has brought both opportunities and challenges to the healthcare industry. Handheld ultrasound (HHU) devices are tablet or smartphone-sized scanners that are highly portable, have lower costs, produce no harmful side effects, and consume less power, making them suitable for use in different environments. HHU devices are primarily designed for new users of ultrasound scanners with varying backgrounds to evaluate different structures of the human body in various clinical settings. HHU applications based on Fifth-generation (5G) wireless network communication and artificial intelligence (AI) technology provide new healthcare solutions. The main application scenarios for HHU devices currently include in-hospital use, remote medical treatment, emergency rescue, and home monitoring. These scenarios allow for rapid image acquisition and real-time image interpretation, thereby improving the efficiency and quality of healthcare, reducing medical costs, and improving the allocation and utilization of medical resources. However, there remain some technical challenges and weaknesses such as device safety, data privacy, and network stability. With the continuous integration of AI technology, HHU applications will find wider use and promotion, bringing about more opportunities and challenges to the healthcare industry. This article reviews the application experience and insights of 5G technology in the field of HHU, aiming to provide fresh evidence and references for future research and applications.

Objective Inpainting is a technique for fixing or removing undesired areas of an image.

Methods In present scenario, image plays a vital role in every aspect such as business images, satellite images, and medical images and so on.

Results and Conclusion This paper presents a comprehensive review of past traditional image inpainting methods and the present state-of-the-art deep learning methods and also detailed the strengths and weaknesses of each to provide new insights in the field.

The integration of medical imaging and artificial intelligence (AI) has revolutionized interventional therapy of valvular heart diseases (VHD), owing to rapid development in multimodality imaging and healthcare big data. Medical imaging techniques, such as echocardiography, cardiovascular magnetic resonance (CMR) and computed tomography (CT), play an irreplaceable role in the whole process of pre-, intra- and post-procedural intervention of VHD. Different imaging techniques have unique advantages in different stages of interventional therapy. Therefore, single imaging technique can’t fully meet the requirements of complicated clinical scenarios. More importantly, a single intraoperative image provides only limited vision of the surgical field, which could be a potential source for unsatisfactory prognosis. Besides, the non-negligible inter- and intra-observer variability limits the precise quantification of heart valve structure and function in daily clinical practice. With the help of analysis clustered and regressed by big data and exponential growth in computing power, AI broken grounds in the interventional therapy of VHD, including preoperative planning, intraoperative navigation, and postoperative follow-up. This article reviews the state-of-the-art progress and directions in the application of AI for medical imaging in the interventional therapy of VHD.

With the development of network technology and intelligent robot technology, Robot-assisted teleultrasound has played an important role in clinical fields. The application of real-time remote ultrasound technology has made the ultrasonic diagnosis break through the limitation of time and space distance, and solved the problem of shortage of medical resources to a certain extent. This article introduces the development and application basis of robot-assisted teleultrasound, summarizes the clinical application status, and discusses the advantages and limitations of its current application. In addition, we discuss the value in application scenario, interventional therapy and intracavitary ultrasound in the future.

Pelvic floor dysfunction (PFD) is a series of diseases with anatomical and/or functional abnormalities of the pelvic organs, which is common in women and can considerably interfere with their quality of life. Imaging is increasingly being used and can contribute towards better understanding, management, and prediction of long-term outcomes in women who suffer from PFD. Of the available techniques such as X-ray, computed tomography, magnetic resonance imaging, and ultrasound, the latter is generally superior for female pelvic floor imaging, especially in the form of transperineal imaging. This technique is safe, cost-effective, simple, widely available, and can provide an overview of the female pelvic floor. This review will outline the basic methodology, introduce recent researches in the field, and provide an overview of likely future utility of this technique in the evaluation of PFD.

Objective: General anesthesia (GA) can decrease cerebral flow velocities and predispose patients to cerebral hyperperfusion syndrome (CHS) and other perioperative adverse events after carotid endarterectomy (CEA). The aim of this study was to investigate whether decreased pre-operative flow velocity is associated with an increased risk of CHS and perioperative cerebral infarct, and to further identify risk factors if there is any.
Methods: We retrospectively evaluated 920 consecutive patients who received CEA from 2010 to 2020 at a major academic hospital in China. Middle cerebral artery (MCA) blood flow velocities were measured before and after induction of the GA by transcranial Doppler (TCD). Patients were classified into two groups: the NORMAL group if flow velocity decreased<30% and the LOW group if flow velocity decreased ≥30%. The ultrasonographic diagnostic criterion of CHS was defined as the 100% increase in flow velocity by TCD from the baseline to post-CEA. The occurrence of CHS, perioperative cerebral infarction was compared between the two groups.
Results: 399 (43.4%) were classified as LOW measurement, and 521 (56.6%) patients were classified as NORMAL measurement. In the LOW group, there were more patients with diabetes, fewer patients with ipsilateral ICA severe stenosis and the opening of anterior/posterior communicating artery. Although the occurrence of CHS per ultrasonography criteria was higher in the LOW group (21.3% vs 15.7%, P = 0.03), the occurrence of CHS per clinical criteria (3.2%, vs 2.1%, P = 0.28) or the perioperative cerebral infarct between the two groups (5.8% vs 5.0%, P = 0.60) is equivalent.
Conclusion: Patients with decreased flow velocities post-GA were more likely to meet the ultrasonography criteria for CHS, but they are not at risk of developing clinical CHS or perioperative cerebral infarct.

With the increasing utilization of semi-thyroidectomy and rapid advancements in ultrasound-guided thermal ablation therapy for the management of papillary thyroid carcinoma (PTC) and PTC cervical lymph node metastasis, ultrasound-guided fine-needle aspiration biopsy (FNAB) has emerged as the predominant approach for the pre-treatment cytopathologic diagnosis of PTC. Numerous expert consensuses and practice guidelines have delineated the acquisition of sufficient, high-quality cellular specimens for cytological examination. However, new challenges keep emerging in the real-world practice of thyroid FNAB, primarily stemming from the perceptions and expertise of physicians or technicians who perform FNAB. The aim of this study was to delineate the key deficiencies in specimen collection during FNAB, elucidate principles of systematic thinking, and propose preventive measures for these issues, along with a range of innovative concepts and technical approaches. Effectively addressing these concerns will enhance FNAB implementation and facilitate advancements in novel therapeutic modalities, such as thermal ablation, to ameliorate prognosis.

In recent years, ultrasound imaging has become an important means of medical diagnosis because of its safety and radiation-free advantages. With the continuous progress of deep learning, Artificial Intelligence (AI) models can process large amounts of ultrasound data quickly and accurately, providing decision support for clinicians in diagnosis. From the perspective of ultrasound image classification, detection and segmentation, this paper systemically introduces the latest progress of AI technology in ultrasound imaging, and summarizes the recent high-level related work. At the same time, we also discuss the development prospect and bottleneck of AI in ultrasound imaging processing, which provides the future research directions for researchers in related fields.

Ultrasound intelligent diagnosis is an emerging technology that combines artificial intelligence (AI) and medical ultrasonography. It has gained significant attention in recent years due to its potential to improve the accuracy and efficiency of medical diagnosis. The core elements of ultrasound artificial intelligence are the construction of data and algorithm models. Therefore, developing autonomous and controllable models, algorithms, and data platforms is extremely important. In this paper, we provide a comprehensive review of the current state-of-the-art in ultrasound intelligent diagnosis including the aspects of the construction of ultrasonic database, deep learning techniques in ultrasound intelligent diagnosis, and the clinical application of ultrasound-AI products. With continued advancements in AI and ultrasound imaging technology, we believe ultrasound intelligent diagnosis will be a valuable tool in the hands of healthcare professionals, providing them with more accurate and efficient diagnoses and treatment plans in the coming years.

Since the 1990s, researchers have been seeking approaches for applying artificial intelligence (AI) to prenatal ultrasound. With the breakthrough of cloud computing technology and the development of deep learning technology, AI in prenatal ultrasound has already entered the clinical application stage in recent years. How does AI combine with clinical prenatal ultrasound? Is the clinical application of AI in prenatal ultrasound effective? What can we expect from AI in prenatal ultrasound? This review introduces the latest developments in this field and explores the challenges and opportunities brought by AI to prenatal ultrasound.

Ultrasound is a commonly used imaging modality for breast cancer diagnosis and prognosis but suffers from false positives, false negatives and interobserver variability. Deep learning (DL), a subset of artificial intelligence, has the potential to improve the efficiency and accuracy of breast ultrasound. This article provides a comprehensive overview of DL applications for breast cancer diagnosis and treatment in ultrasound, including methodological descriptions of various DL models, and clinical applications on noise reduction, lesion localization, risk assessment, diagnosis, response evaluation and outcome prediction. Furthermore, the review highlights the importance of interpretability and small sample size learning of DL-based systems in clinical practice; specific recommendations for further expanding the clinical impact of DL-based systems are also provided.

Artificial intelligence-based pelvic floor ultrasound helps the diagnosis, preoperative assessment, and postoperative monitoring of female pelvic floor dysfunction (FPFD). The application of artificial intelligence in pelvic floor ultrasound mainly includes automatic segmentation and measurement, the diagnosis of muscle injury, childbirth prediction and postoperational evaluation. It can not only overcome the problem of operator experience dependence but also improve work efficiency and simplify the workflow, which has popularized the application of pelvic floor ultrasound. However, most of the current research is still limited to the automatic segmentation of three-dimensional axial plane levator hiatus (LH). The automatic reconstruction, real-time tracking of 3D/4D images and the imaging navigation of pelvic floor surgery remain major challenges for researchers.

Neuromuscular disease includes a wide range of muscular disorders, but it lacks convenient and effective tools for clinical diagnosis and therapeutic monitoring. As a widely used imaging tool, ultrasound can clearly display muscle structure and create basic conditions for accurate image analysis. At present, many studies have tried to obtain information on muscle function and pathological changes by analyzing the features of muscle ultrasound images, and have shown reliable results. However, the minimal changes in muscle structure and image texture are easy to be neglected, and manual segmentation and data analysis are time-consuming tasks. Artificial intelligence (AI) can accurately identify image changes and improve the efficiency of image analysis, and the muscle ultrasonic image analysis model developed based on AI has shown advantages in a large number of research results. This review summarizes the relevant studies of muscle ultrasound imaging and AI in the field of it, including a variety of research based on traditional AI methods or deep learning methods, as well as discusses the clinical significance of ultrasound analysis assisted by AI and the future exploration directions in this field.

Breast cancer is the most common malignancy and the leading cause of death for women. Ultrasound is the main tool for breast cancer screening, but it can be influenced by the subjective factors of sonographers. With the continuous development of medical technology and artificial intelligence (AI), the application of breast ultrasound imaging technology is becoming increasingly widespread. Among them, the application of AI and automated breast volume scanning (ABVS) brings new opportunities and challenges for ultrasound diagnosis of breast diseases, while making breast ultrasound diagnosis more accurate and efficient. This article explores the application and prospects of AI and ABVS in ultrasound diagnosis of breast diseases.

Artificial intelligent (AI) based on deep learning has been used in medical imaging analysis for years. Improvements have been made in the diagnosis of various diseases with the help of deep learning. Multimodal medical imaging combines two or more imaging modalities, providing comprehensive diagnostic information of the diseases. However, some modality problems always exist in clinical practice. Recently, AI-based deep learning technologies have realized the modality conversion. Investigations on modality conversion have gradually been reported in order to acquire multimodal information. MRI images could be generated from CT images while ultrasound elastography could be generated from B mode ultrasonography. Continuous researches and development of new technologies around deep learning are still under investigation and provide huge clinical potentials in the future. The purpose of this review is to summarize an overview of the current applications and prospects of deep learning-based modality conversion of medical imaging.

Cardiovascular disease (CVD) is one of the ten leading causes of death worldwide. Atherosclerotic disease, which can lead to myocardial infarction and stroke, is the main cause of CVD. The two main ultrasound image phenotypes used to monitor atherosclerotic load are carotid intima-media thickness (IMT) and plaque area (PA). Early segmentation and measurement methods were based on manual or threshold segmentation, snake models, etc. Usually, these methods are semi-automatic and have poor repeatability and accuracy. Segmentation of the carotid intima-media complex (IMC) and plaque in ultrasound based on artificial intelligence can achieve good accuracy. Compared with two-dimensional ultrasound, three-dimensional/four-dimensional ultrasound can provide spatial dynamic vascular information, which is helpful for doctors to evaluate. This study reviews the progress of artificial intelligence (AI) segmentation methods based on machine learning (ML) and deep learning (DL) used in the segmentation of the IMC and plaque as well as the 3D / 4D reconstruction of carotid ultrasound.

ChatGPT is attracting widespread attention from all walks of life with its excellent multi-round dialogue ability and strong user intent understanding ability, triggering a new wave of artificial intelligence. From the perspective of technical analysis, this article sorts out the various related technologies used in the GPT (Generative Pre-training Transformer) series models as well as large-scale multimodal models, which are more powerful and perform better in multiple downstream tasks. Meanwhile, we guide users to use LLM (Large Language Model) along with GPT more scientifically to maximize their potential. Finally, we analyze the application prospect of the GPT as well as the large-scale multimodal models in the medical field, and the problems are discussed from the perspectives of the risks and limitations of large-scale models applied into the medical field.

In 2023, the capabilities of text communication, language translation, image generation, and code writing demonstrated by ChatGPT and GPT-4 have received widespread attention in and out of China. With years of data and technology accumulation, many Chinese companies and research teams continue to make efforts in the field of large models, and their models cover many industries and have a number of characteristic functions. This paper will introduce the type and development trends of large models, and sort out the methods and characteristics of domestic large models. Finally, we will explain the advantages and disadvantages of the domestic models, and analyze the application prospect and challenges of them in the medical field.

ChatGPT, an artificial intelligence (AI) software developed by OpenAI, is a powerful language model. ChatGPT is expected to perform a variety of tasks in the field of medical writing and publishing, including writing drafts, extracting article abstracts, and embellishing language. At the same time, ChatGPT has technical shortcomings and ethical challenges that have raised concerns. This review summarizes the issues faced by ChatGPT in the field of medical writing and publishing, and provides a reference for the development of standards and systems for the use of AI products such as ChatGPT.

This article discusses the potential benefits of using virtual reality (VR) technology in the teaching of ultrasonography. VR technology can provide an immersive learning experience, enabling students to interact with simulated environments and practice various tasks. Ultrasonography has the characteristics of convenient, rapid, real-time feedback, and dynamic, and is indispensable in practical clinical disease diagnosis applications. Combining VR and ultrasound technology can provide a unique and effective teaching method for medical students and medical professionals. This article mainly discusses the current situation, advantages, and challenges of virtual reality technology in the teaching of ultrasonography to ensure their successful implementation in an educational environment.

The rapid advancement of 5G technology has opened new possibilities for remote ultrasound education, offering the potential to enhance training, real-time consultation, and quality control for primary ultrasound doctors. The 5G remote ultrasound education has the potential to revolutionize the way primary ultrasound doctors are trained and supported, ultimately leading to improved patient care and outcomes. By understanding the current status and development trends of this cutting-edge educational approach, the medical community can better prepare for and contribute to its ongoing evolution. Looking towards the future, the development trends in 5G remote ultrasound education are expected to revolve around continuous improvement and innovation in educational methods and technologies. This includes the exploration of artificial intelligence and machine learning applications, the expansion of telemedicine and telementoring programs, and the development of personalized learning plans tailored to individual learners' needs. This article aims to offer an overview of the current status and applications of 5G remote ultrasound education, including the development of theoretical courses and network construction within our institutes, and to discuss future trends in this field.

The sentinel lymph node (SLN) concept hypothesizes that metastatic cancer cells will spread through the lymphatic system to the SLN being the first one in the lymphatic chain to receive the metastatic cells, indicating that if the SLN is free of cancer cells the rest of the lymphatic chain is also without metastatic disease. Diagnostic ultrasound imaging (US) has been used to evaluate lymph nodes (LN) to determine level of suspicion and to guide LN biopsies. However, conventional US cannot be used for lymphatic mapping, which requires administration of a tracer. This has been changed with the use of contrast-enhanced US (CEUS) to detect lymphatic channels and SLNs after subcutaneous injections of microbubble-based US contrast agents (UCAs). The aim of this review is to examine the clinical evidence on the role of subcutaneous injection of UCA, known as lymphosonography, to be used as preoperative identification of SLNs in patients with breast and other cancers.

Atherosclerosis (AS) is the main pathophysiological process behind CVD. Cardiovascular diseases caused by AS (such as stroke, coronary heart disease, etc.) are characterized by a high prevalence, high disability rate and high mortality, which greatly increase the burden on families and society. Thus, it is important to find AS as early as possible. When atherosclerosis occurs, the earliest sign of its structural and functional changes is arterial stiffness, and endothelial dysfunction is the key link. Arteriosclerosis is caused by the imbalance between collagen and elastin in the arteries. Elastin fibers gradually decrease, and stiffer collagen fibers increase, making the vessels less elastic. Endothelial dysfunction also affects the regulatory function of arterial vascular tension, and accelerates the remodeling of the vascular wall, to make blood vessels more rigid. Quantitative measurement of arterial stiffness and carotid intima-media thickness (CIMT) is an important tool for early detection of vascular lesions. At present, ultrasound technology including echo tracking (ET), wave intensity (WI), real-time shear wave elastography (SWE), ultrafast pulse wave velocity (ufPWV), shear wave dispersion (SWD), cardio-ankle vascular index (CAVI), arterial velocity pulse index and arterial pressure volume index (AVI and API) has the advantages of being noninvasive, real-time, economical and reproducible in the assessment of atherosclerosis, and is widely used in the early diagnosis of AS. Most importantly, the change in arterial hardness detected by traditional ultrasound technology precedes the change in intima-media thickness, suggesting that ultrasonic elasticity monitoring may be a more sensitive method of predicting AS and can find and prevent cardiovascular diseases earlier.

Contrast-enhanced ultrasound (CEUS) is an imaging modality that has achieved considerable relevance in various clinical settings including the assessment of renal disease. CEUS is performed by injecting microbubble-based ultrasound contrast agents (UCAs) that create signals to display the microvasculature, allowing quantitative and qualitative assessment of parenchymal perfusion and real-time visualization of the renal anatomy. In recent years, CEUS has been widely accepted and applied for the assessment of kidney perfusion and the characterization of indeterminate renal masses, primarily due to its diagnostic efficacy, availability, low cost, reproducibility, and absence of nephrotoxicity. CEUS provides a higher spatial and temporal resolution than other cross-sectional imaging, resulting in high sensitivity and specificity for its applications in a variety of renal conditions including cancer monitoring following ablation, detection of transplant complications, hypoperfusion, acute traumatic injury, renal artery stenosis, parenchymal infection, and kidney intervention guidance. Additionally, the continuous investigation and development of new technologies surrounding this imaging technique have shown encouraging preliminary results for the use of CEUS in the evaluation of molecular expression in several disease processes, the dynamic analysis of blood flow kinetics, and the implementation of super-resolution imaging systems. The purpose of this article is to provide an overview of the current and potential clinical applications of renal CEUS.

The cholinergic anti-inflammatory pathway is a neuro-immune regulatory pathway that mediates anti-inflammatory effects based on the vagus nerve, acetylcholine and α7 nicotinic acetylcholine receptors. In recent years, the effect of nerve stimulation by ultrasound has attracted much attention and has been widely studied. Ultrasound can stimulate the vagus nerve or spleen nerve and activate the cholinergic anti-inflammatory pathway, exerting anti-inflammatory and organ protection effects, which is expected to provide a new treatment for many inflammatory diseases. The purpose of this review is to introduce the composition, mechanisms and regulation methods of cholinergic anti-inflammatory pathway, and discuss its therapeutic implications.

The exploration of human brain function has always been a research hotspot in the field of neuroscience. The concept of a neurovascular unit suggests that cerebral microcirculation can be used as a reliable signal to reflect neural function. Accordingly, functional imaging techniques based on the neurovascular unit are very promising because of their ability to reveal neurovascular coupling and evaluate the functional rehabilitation of cerebrovascular diseases represented by stroke. In the present review, we first describe the role of the neurovascular unit in the injury and repair processes after stroke. We then briefly introduce the relative characteristics and advantages of representative neurovascular unit-based functional imaging. Finally, we summarized the value of these techniques in the evaluation of neural networks after stroke.

Objective: CT-based Bosniak classification system has been routinely used to assess complex renal cystic lesions and also been applied to contrast-enhanced magnetic resonance imaging (CEMRI) and contrast-enhanced ultrasound (CEUS). Besides, the 2019 new version incorporated MRI into the Bosniak system. However, the role of US in the Bosniak system has not been clearly established. The aim of this study was to compare the diagnostic ability of CEUS, CECT and CEMRI for renal cystic lesions based on the current Bosniak classification.

Methods: Related studies were searched in PubMed, EMBASE, and the Cochrane Library databases from January 1, 2010 to December 14, 2020. QUADAS-2 was used to assess the study quality. Meta-analysis was performed by “midas modules” of Stata SE 15.0 software. The bivariate mixed-effect model was used. The pooled sensitivity and specificity of these three modalities were calculated and compared. Meta-regression and subgroup analyses were conducted to reveal the source of heterogeneity.

Results: CEUS showed highest pooled sensitivity and specificity, which were 98% (95% CI: 91%, 100%) and 80% (95% CI: 64%, 90%) respectively. Pooled estimates of CEMRI were slightly lower than those of CECT with the sensitivity 85% (95% CI: 77%, 91%) versus 88% (95% CI: 77%, 94%) and specificity 71% (95% CI: 52%, 85%) versus 79% (95% CI: 70%, 86%), respectively.

Conclusions: Based on the current Bosniak classification, CEUS seemed superior to CECT and CEMRI for the diagnosis of complex renal cystic masses, and could serve as a valuable alternative for CECT and CEMRI.

This article provides a review of pathology, clinical features, imaging diagnosis and treatment of lipomatosis of nerve (LN), aiming to improve the comprehensive understanding of the disease and achieve early diagnosis and treatment. By searching for the relevant literature, we systematically summarized the pathology, clinical features, imaging diagnosis and treatment of LN in peripheral nerve. Lipomatosis of nerve is a rare benign tumor-like lesion in the peripheral nerve. LN can present primarily as painless neoplasm, macrodactyly, or neurological dysfunction. Magnetic resonance imaging and ultrasonography are commonly used imaging methods before operation, which can characterize and locate the scope of the disease. There are few ultrasonographic studies on LN of peripheral nerve, mostly in the form of case report or small sample analysis. Treatment of lipomatosis of nerve varies with clinical symptoms and lacks consensus. Although LN is benign lesion, it can be invasive, progressive, and recurrent. It is very important to understand the problems of neurological dysfunction caused by LN. More studies are needed to provide more detailed imaging information for clinicians and to achieve the effective treatment.

Chronic liver disease is common in China and worldwide, with liver fibrosis as the primary pathological finding. Any chronic liver disease can lead to hepatic fibrosis and gradually develop into cirrhosis. Complications, such as portal hypertension, gastrointestinal bleeding, and liver failure can occur. Some patients even develop hepatocellular carcinoma. Thus, timely diagnosis of hepatic fibrosis is vital for the assessment of etiology, treatment, and prognosis. Conventional ultrasound imaging shows low sensitivity with its subjectivity for preliminary diagnosis of liver fibrosis, creating limitations in qualitative and quantitative evaluation. Ultrasound elastography is a recently developed technique that can help overcome these limitations. The elastic imaging method combines conventional ultrasound to assess liver stiffness along with routine examination. This "one-stop" check for liver disease opens new prospects for clinical and scientific research and improves the accuracy of disease diagnosis for broad clinical application. This article will review the current status of ultrasound elastography for its applications in chronic liver diseases.

Objectives: To determine the efficacy of Doppler-based renal resistive index (RRI) in the prediction of acute kidney injury (AKI) in critically ill patients.
Methods: A systematic review and meta-analysis of cohort studies was conducted. Relevant studies were identified in PubMed, Embase and Cochrane Library from inception to November 1, 2020, and reference lists of identified primary studies. Prospective studies that examined the diagnostic accuracy of RRI in AKI were included.
Results: Among the 126 articles identified, 18 were included, with a total of 1656 patients. Bivariate analysis yielded pooled sensitivity and specificity of 0.81 (95% CI 0.74-0.86) and 0.75 (95% CI 0.65-0.83), respectively. The summary positive likelihood ratio was 3.2 (95% CI 2.2-4.6), and negative likelihood ratio was 0.26 (95% CI 0.19-0.36).
Conclusion: Elevated RRI may be an early predictor of AKI in critically ill patients. Further large-scale prospective studies are needed to confirm the predictive efficacy and determine the performance and optimal cutoff value of RRI among the included studies.

Gas therapy is a new therapeutic method that has been developed in recent years and shows great clinical prospects for the treatment of tumours and cardiovascular, nerve, and immune system diseases. Therapeutic gases, including oxygen, hydrogen, nitric oxide, hydrogen sulfide, xenon, and other bioactive gases are involved in modulating cell signaling pathways and have important physiological functions with substantial therapeutic potential. However, their precise delivery remains a major challenge. Recently, researchers began to use ultrasound to trigger microbubbles that have encapsulated these gases for intravenous administration. This not only enhances the contrast of ultrasound imaging, but also precisely releases gases in the targeted area using ultrasound-targeted microbubble destruction. This article reviews the latest advances in the use of microbubbles to load therapeutic gases for the treatment of diseases.

Ultrasound imaging has attracted great interest of researchers due to their application in cancer diagnosis and treatment. Ultrasound contrast agents, microbubbles and nanobubbles are widely explored as a multifunctional platform, not only carrying other contrast agents for multimodal imaging to complement the disadvantages of each imaging modality, but also carrying drug/gene for cancer theragnostic. In this article, the characteristics and differences of microbubbles and nanobubbles are briefly introduced and reviewed. Besides, the microbubbles and nanobubbles driven multimodal imaging and theragnostic of cancer are summarized.

As an emerging technology, radiomics has shown potential values in the field of healthcare. CT/MRI was preferred in previous radiomics researches because its images are easy to be standardized. And only until recently, an increasing number of studies focusing on the application of ultrasound radiomics in predicting molecular subtypes, identifying of malignant lesions, reactions to neoadjuvant chemotherapy, and axillary lymph node metastasis of breast cancer have been published. The purpose of this review is to summarize the steps of radiomics used in the field of breast cancer. In conclusion, ultrasound radiomics is a promising technology in diagnosing and monitoring breast cancer and further assisting physicians in patient management.

It is particularly important to evaluate the efficacy of neoadjuvant chemotherapy (NAC) for breast cancer. This article reviews the current status and progress of imaging evaluations regarding the efficacy of NAC in women with breast cancer, including mammography, ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET-CT) and radiomics. Each imaging method has value for evaluating the efficacy of NAC for breast cancer, but certain defects and limitations. Therefore, the optimal selection will employ a combination of multiple imaging methods that will not only benefit patients but also avoid the unnecessary waste of medical resources.

The ultrasound contrast agents currently used in clinics are microbubbles with a large particle size and short circulation time, and their approved clinical applications are limited to endovascular diagnosis and therapy only. The development of ultrasound-responsive nanodroplets (NDs) provides a new approach for extravascular diagnosis and therapy, especially for molecular imaging and targeted therapy of tumors. The NDs with a nano-scaled particle size and a liquid core can maintain their shape and initial diameter during injection, enhancing their EPR effects and facilitating the accumulation of NDs at the tumor site. When exposed to ultrasound, NDs can vaporize and exhibit contrast enhancement at the sites of interest. In addition, the destruction of microbubbles can provide a driving force to facilitate the release of drugs or genes from the microbubbles into target cells, allowing the NDs to act as drug carriers. The development of ultrasound-responsive NDs has shown rapid progress in recent years, while a variety of NDs with excellent properties have been fabricated for targeted diagnosis and drug delivery. In this article, the development of ultrasound-responsive NDs was reviewed in terms of their structure, phase transition properties, and applications in targeted tumor diagnosis and therapy.