Atrioventricular Coupling (AV-Coupling) refers to the functional coordination between atrial and ventricular systole and diastole in the heart. Currently, the primary method for evaluating AV-Coupling is through the left atrioventricular coupling index (LACI), measured using imaging techniques. A higher LACI indicates a greater mismatch between the volumes of left atrium and left ventricle at the end of ventricular diastole, reflecting a more significant impairment of left AV-Coupling. AV-Coupling plays a vital role in the pathophysiology and progression of cardiovascular diseases. Therefore, early and accurate assessment of AV-Coupling is essential for evaluating a patient’s condition, guiding clinical decisions, stratifying risk, and determining prognosis. This review aims to summarize the physiological mechanisms and evaluation methods of AV-Coupling, as well as its clinical significance in various cardiovascular diseases.

Ultrasound radiogenomics, an emerging field at the intersection of radiology and genomics, employs high-throughput methods to convert radiological images into high-dimensional data, which are then processed to extract and analyze radiomic features. These features, including shape, texture, and intensity variations, are correlated with specific genetic mutations such as TP53 and PIK3CA, critical for cancer progression and treatment response. By integrating clinical data with ultrasonic features, predictive models are developed using machine learning techniques, aiming to refine the capability to diagnose and personalize treatment plans for breast cancer patients. This approach reduces the need for invasive biopsies and medical costs for patients through a better understanding of the tumor’s biological behavior using ultrasound images. This review focuses on the application of ultrasound radiogenomics for predicting gene mutations in breast cancer, highlighting its transformative potential in clinical practice and discussing ongoing challenges and future directions in this field.

Since 2020, breast cancer has held the highest incidence rate among cancers worldwide. Breast ultrasound (US) imaging technology plays a crucial role in the early diagnosis and intervention treatment of breast cancer patients. Deep learning (DL), as one of the most powerful machine learning techniques in the field of artificial intelligence (AI), has the ability to automatically select features from raw data, achieving remarkable advancements in breast US imaging. This review focuses on the application of convolutional neural networks (CNNs) within DL technology in the field of breast US. It summarizes the use of DL models in breast cancer screening and in preoperative prediction of molecular subtypes, response to neoadjuvant chemotherapy (NAC), and axillary lymph node (ALN) metastasis status. The review also identifies the data limitations of using CNN models in breast US and describes the development history and current applications of DL in breast cancer screening, diagnostic guidance, and prognostic prediction. Furthermore, it discusses the future research directions and potential challenges. Advancing the development of CNN technology in breast US, and improving the generalizability and reproducibility of these models, will significantly promote their translational application in clinical settings.

Lymphoma is a common hematological malignancy with markedly increasing incidence. Its pathological types are complex and heterogeneous, and there are significant differences in treatment options and efficacy. Therefore, early and precise diagnosis, assessment of efficacy, and judgment of prognosis are key clinical problems. Ultrasound (US) has important clinical value in the diagnosis and treatment of lymphoma. This article reviews the progress made with new US technologies in improving the accuracy of diagnosis and staging of lymphoma, predicting the course of lymphoma, monitoring the progression of lesions during treatment, and assisting clinics in formulating accurate and effective treatment plans. In addition, we review the biological basis of US prediction of lymphoma and provide an outlook for future research directions.

Papillary thyroid microcarcinoma (PTMC) is a subtype of papillary thyroid carcinoma (PTC) characterized by a diameter of less than 10 mm. While its incidence is on the rise, PTMC generally carries a favorable prognosis. Traditional surgical intervention remains the primary treatment method, widely recognized for its effectiveness. However, surgical procedures can lead to postoperative scarring and complications, posing challenges for patients. For some low-risk PTMC cases that exhibit long periods of non-progression, active surveillance has emerged as a viable treatment option. Thermal ablation technology, guided by ultrasound, has demonstrated comparable short-term efficacy to surgery but with smaller incisions and reduced costs, offering a new alternative for PTMC patients. Currently, the management strategies for PTMC exhibit considerable diversity, contributing to ongoing debates in treatment approaches. This paper provides a comprehensive summary and review of the primary therapies available today.

Preoperative imaging is crucial for patients diagnosed with renal cell carcinoma presenting with thrombus. These individuals frequently exhibit a hypercoagulable state, raising the risk of thrombus progression or the formation of a new bland thrombus post-imaging and pre-surgery. Intraoperative ultrasound, employed under direct visualization, offers real-time, dynamic detection of thrombi, potentially influencing surgical decisions. This short review explores the utility of intraoperative ultrasound in robot-assisted thrombectomy for renal cell carcinoma, detailing its primary applications and added value in mitigating surgical risks for urologists.

Alzheimer’s disease (AD) is a common neurodegenerative disease in clinical practice. The pathogenesis is still unclear, and there is no specific method. According to the current known pathological studies, AD biomarker TAU protein, phosphorylated tau and amyloid-β (Aβ) play an important role in the pathophysiological changes of AD. For pathological research, the development of low-intensity ultrasound (LIUS) provides another idea for the mechanism of AD treatment, which can better treat AD, regulate various factors specifically, and effectively treat AD by stimulating synapses and improving neurons. Based on this research background, this paper summarizes the role of AD biomarkers TAU protein, phosphorylated tau and amyloid protein in the occurrence and development of AD and the mechanism of pathological changes in the treatment of AD by low-intensity ultrasound, aiming to provide new insights into clarifying the pathological changes of AD biomarkers and the mechanism of LIUS in the treatment of AD. Given that the treatment for AD based on LIUS is still far from a complete cure, we will discuss the prospects for future development of LIUS to guide the treatment of AD.

Barium titanate (BaTiO₃), as an emerging inorganic piezoelectric material with excellent piezoelectric catalytic effects, has showing advantages in tumor therapy. To achieve ultrasound-regulated tumor treatment using BaTiO₃, researchers have developed strategies including utilizing BaTiO₃ combined with ultrasound for tumor therapy, enhancing reactive oxygen species (ROS) generation through chemical modification of BaTiO₃, and employing combined therapy with other treatment methods. These strategies provide new insights and approaches for non-invasive and precision treatment of tumors. In this review, we first explain the principle of piezoelectric effect based on BaTiO₃. Subsequently, we introduce the application of BaTiO₃ as a piezoelectric material in tumor therapy and its combined therapy with other treatment modalities in tumor treatment. Finally, we summarize the current status and limitations of BaTiO₃ in ultrasound‐triggered piezoelectric therapy for tumors and propose future prospects.

Multiparametric MRI (mpMRI) is currently the standard imaging modality for the diagnosis of prostate cancer; however, studies have reported that targeted biopsy based on mpMRI may miss approximately 30% of clinically significant cases. Recent advances in ultrasound imaging have improved its accuracy for detection of prostate cancer. Newer techniques such as MicroUS, elastography, contrast-enhanced ultrasound (CEUS), and contrast ultrasound dispersion imaging (CUDI) have enabled a comprehensive, real-time, and relatively inexpensive approach to evaluate the prostate gland. Multiparametric ultrasound (mpUS) integrates multiple parameters from these techniques to generate multiparametric maps akin to those produced by mpMRI, to localize prostate cancer. This review aims to explore the performance of modern ultrasound techniques and mpUS for diagnosis of prostate cancer, comparing them with mpMRI.

Right ventricular-pulmonary artery coupling (RV-PAC) serves as an indicator of the efficiency of energy transfer from the right ventricle to the pulmonary circulation. It plays a critical role in the diagnosis, clinical treatment, and prognosis of conditions such as pulmonary hypertension, heart valve disease, and heart failure. Various non-invasive evaluation methods have recently been proposed for assessing RV contractility and arterial afterload, based on the end-systolic elastance to arterial elastance ratio (Ees/Ea), which is derived from invasive pressure-volume loops. In this review, we summarize the fundamental concepts, physiological mechanisms, examination methods, influencing factors, and clinical significance of RV-PAC to provide a valuable reference for clinical practice.

The left ventricle (LV) and right ventricle (RV) are interdependent, as both structures are nestled within the pericardium, share a common septum, and are encircled by interconnected myocardial fibers. Interventricular interaction refers to the dynamic relationship between LV and RV, particularly how changes in one ventricle influence the geometry and function of the other. Imaging, particularly echocardiography, is vital for characterizing interventricular interactions by assessing geometric indices, septal motion, Doppler flow patterns, and changes in strain, remodeling, and diastolic filling associated with the loading conditions of the contralateral ventricle. In this review, we summarized the physiological and anatomical basis of ventricular interaction, echocardiographic imaging indices, and their clinical utilities and limitations. The goal is to systematically review the research advancements in echocardiographic assessment of LV-RV coupling and to provide guidance for clinical practice.

Right ventricular-pulmonary arterial coupling refers to the interaction and functional matching between the right ventricle and the pulmonary artery. When the coupling is disrupted, it can lead to a series of cardiovascular diseases, such as pulmonary hypertension, congenital heart disease, heart failure and so on. Therefore, it is important to evaluate cardiovascular structure and function. Cardiac magnetic resonance has the advantage of multi-parameter, multi-sequence, and high-resolution imaging, which can be used to comprehensively evaluate the cardiovascular system through cardiac magnetic resonance feature tracking technology, cardiac magnetic resonance cine imaging technology, T1 mapping, and T2 mapping imaging, and so on. This review summarizes the application and research progress of cardiac magnetic resonance technology in the assessment of the right ventricle and the pulmonary artery (RV-PA) coupling.

Arterial stiffness (AS) represents a pathological process characterized by reduced arterial elasticity and compliance, closely linked to aging and cardiovascular diseases, including hypertension, atherosclerosis, diabetes, and chronic kidney disease. As an important predictor of cardiovascular risk, AS evaluation plays a crucial role in early detection, disease monitoring, and therapeutic guidance. This review aims to systematically summarize current advancements in AS evaluation, focusing on non-invasive techniques such as pulse wave velocity, ultrasound-based methods, and arterial pressure waveform analysis. We discuss the advantages, limitations, and clinical applications of these methods, highlighting the recent integration of artificial intelligence and machine learning to enhance diagnostic accuracy and automation. The review also explores emerging biomarkers and novel imaging techniques, such as shear wave elastography and ultrafast ultrasound imaging, which offer promising insights for early AS detection and risk stratification. Despite significant progress, challenges remain in standardizing measurement protocols and improving sensitivity across various populations. Future research directions emphasize the development of wearable technologies, artificial intelligence-based diagnostic tools, and standardized methodologies to advance AS evaluation and improve cardiovascular outcomes.

Left ventricular-arterial coupling (LVAC) represents a critical physiological mechanism that characterizes the interaction between left ventricular (LV) contractility and the arterial system's resistance and elasticity. The balance within LVAC is essential for efficient energy transfer from the heart, which underpins optimal cardiovascular function. In a healthy state, the balance between LV contractility and arterial elasticity and resistance allows the heart to maintain normal circulation with minimal energy expenditure. However, with the progression of age and diseases such as atherosclerosis and hypertension, arterial stiffness increases, LV function decreases, and the LVAC balance is disrupted, leading to a significantly increased risk of cardiovascular events. This imbalance is particularly significant in patients with heart failure (HF) and coronary artery disease (CAD), where LVAC imbalance is strongly associated with increased cardiac load and decreased energy efficiency. Thus, understanding and evaluating LVAC are crucial for elucidating cardiovascular physiology and guiding therapeutic strategies for diseases such as HF, hypertension, and CAD. Methods for assessing LVAC include invasive pressure-volume loops and cardiac catheterization, as well as non-invasive techniques such as echocardiography and arterial pulse wave analysis (PWA). Despite the higher accuracy of invasive methods, non-invasive methods are commonly used in clinical practice to assess LVAC because of their lower risk. With cardiac magnetic resonance imaging (CMR) and 3D/4D imaging techniques advancing, more precise structural and functional analysis of the heart and arterial system will be possible in the future. In this review, we describe the physiological mechanisms, assessment methods, influencing factors, and clinical significance of LVAC, as well as interdisciplinary studies with biomechanics and metabolism, which provide new ideas for personalized treatment of LVAC.

Left ventricular-arterial coupling (VAC) is essential for understanding both cardiovascular physiology and pathophysiology. Traditionally assessed through invasive techniques, recent advancements have introduced noninvasive methods that employ imaging modalities and physiological parameters to evaluate ventricular pressure, volume, and arterial load characteristics. This review examines commonly used noninvasive VAC assessment methods, including echocardiographic single-beat method, myocardial work, wave intensity, the ratio of pulse wave velocity to global longitudinal strain, and imaging-based pressure-volume loops. These methodologies have demonstrated potential in clinical applications, such as evaluating cardiac function, personalizing treatment plans, monitoring therapeutic effects, and assessing prognosis. The incorporation of advanced imaging and computational techniques is anticipated to further enhance the accuracy and clinical relevance of VAC assessment in the management of cardiovascular diseases.

A heterozygous microdeletion of chromosome 7q11.23 causes the rare neuropsychiatric developmental disorder Williams-Beuren Syndrome. The syndrome is more difficult to diagnose before birth than after, even though the syndrome often manifests prenatally as intrauterine growth restriction and cardiovascular defects on prenatal ultrasonography. The potentially poor prognosis of affected individuals highlights the need to improve prenatal diagnosis of the syndrome. This review summarizes recent advances in our understanding of the genetics of Williams-Beuren Syndrome and its manifestations on prenatal ultrasonography, which may facilitate its early detection and inform prenatal genetic counseling.

Ultrasound imaging holds a significant position in medical diagnostics due to its non-invasive and real-time capabilities. However, traditional ultrasound is constrained by the diffraction limit, making it challenging to capture fine blood vessels. Ultrasound localization microscopy (ULM) overcomes this limitation by achieving super-resolution imaging through tracking the trajectories of microbubbles (MBs) within microvasculature. This review summarizes the applications of deep learning (DL) techniques in ULM post-processing algorithms, including key steps such as beamforming, clutter filtering and denoising, localization, and tracking. Although DL shows great potential in improving ULM imaging quality and efficiency, current research mainly focuses on imaging algorithmic improvements rather than in-depth image analysis. In the future, with the accumulation of ULM image data, the powerful feature extraction capability of DL is expected to further advance ULM applications in disease prediction and diagnosis.

Integrating machine learning into medical diagnostics has revolutionized the field, particularly enhancing Computer-aided Diagnosis (CAD) systems. These systems assist healthcare professionals by leveraging medical data and machine learning algorithms for more accurate diagnosis and treatment plans. Mammography, an X-ray-based imaging technique, is pivotal in early breast cancer detection, enabling the differentiation between benign and malignant lesions. Recent studies have focused on developing deep learning-enabled mammography CAD systems, which have shown promising results in detecting, segmenting, and classifying anomalies in mammogram images. This comprehensive review presents an innovative system architecture for breast cancer detection, segmentation, and classification using deep learning within mammography CAD systems. It also explores publicly available mammogram datasets and the critical parameters for assessing deep learning system performance. The literature review is meticulously conducted using the PRISMA methodology to evaluate and synthesise novel research findings in this domain. This survey highlights the technological advancements and underlines the potential of deep learning in transforming mammographic analysis for breast cancer detection.

Throughout pregnancy, maternal thyroid-related hormones are transported to the fetus via the placenta to allow normal fetal growth and development and are particularly important in the first and second trimesters of pregnancy. During maternal-fetal transport, in addition to thyroid-related hormones, thyroid-stimulating hormone receptor antibodies and antithyroid drugs can enter the fetus and interfere with development of the fetal thyroid gland and endocrine function, potentially leading to hyperthyroidism or hypothyroidism in the fetus or newborn. Several basic studies have been performed to demonstrate the important role of thyroid-related hormones in fetal and neonatal bone development. Ultrasound can assess neonatal skeletal maturity and bone development safely, rapidly, and effectively. This review aims to communicate the latest knowledge about maternal and fetal thyroid function in both normal and pathological pregnancies and summarize the latest advances in the potential effects of abnormal maternal thyroid function on bone development in the fetus and neonate. Finally, it discusses recent advances in research on ultrasound in the assessment of fetal and neonatal bone development.

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.