Irreversible electroporation (IRE) is an innovative non-thermal ablation technique increasingly utilized in cancer treatment due to its unique operational principles and clinical advantages. As a novel interventional ultrasound technology, IRE has undergone extensive research, development, and practical application over the years. IRE ablation, particularly in conjunction with immunotherapy, has emerged as a significant modality in cancer treatment and related fields. This article aims to present the advancements in irreversible electroporation ablation for cancer through an examination of both basic research and clinical applications.

Cancer, one of the leading causes of global mortality, arises from dysregulated cellular processes that create an immunosuppressive tumor microenvironment (TME), promoting immune evasion and therapeutic resistance. While immunotherapy activates the immune system to combat tumors and provide durable benefits, its efficacy is often constrained by the hostile TME. Focused ultrasound (FUS) has emerged as a non-invasive, and precise therapeutic modality capable of mechanically or thermally ablating solid tumors. This review discusses the dual mechanisms of FUS—thermal ablation (T-HIFU) and mechanical disruption (M-HIFU, e.g., histotripsy)—and their role in modulating the TME. Specifically, it examines how FUS-induced immune activation can overcome immunosuppressive barriers, promote T-cell infiltration, and synergize with immunotherapy to improve outcomes in solid tumors, while also highlighting current challenges and future directions.

Objective Plantar fascia (PF) is a thick connective tissue on the plantar surface of the foot that plays a crucial role in maintaining the longitudinal arch. Plantar fasciitis, characterized by inflammation of the medial tuberosity of the calcaneus, is one of the most common causes of heel pain. Imaging is essential for accurate diagnosis, with ultrasonography widely applied to evaluate PF thickness, monitor therapeutic outcomes, and guide interventions. However, its application is limited by operator dependency. This study aimed to investigate the inter- and intra-rater reliability of musculoskeletal ultrasound in measuring PF thickness in patients with plantar fasciitis.
Methods In this cross-sectional analytical study, 40 participants were enrolled, including 26 females and 14 males. The reliability of PF measurements on different sides was assessed using Cronbach’s alpha and intraclass correlation coefficients (ICC).
Results A total of 40 participants (age range: 20-60 years) were included in the study. PF thickness in patients with plantar fasciitis measured by Observer 1 ranged from 3.8-6.9 mm (left) and 3.2-6.0 mm (right), whereas measurements by Observer 2 ranged from 2.9-7.1 mm (left) and 3.2-6.0 mm (right). Intra-rater reliability showed ICCs ranging from 0.618-0.857 for Observer 1 and 0.76-0.92 for Observer 2, indicating moderate (> 0.60) to excellent reliability.
Conclusion PF is a deep structure, and its visualization may be influenced by operator technique. Ultrasound measurement of tendon thickness shows good reliability in patients with established plantar fasciitis.

Objective It is always a clinical challenge to identify neonatal pulmonary hypertension (NPH). Although the diagnostic gold standard of pulmonary hypertension (PH) is the true measurement of resting pulmonary arterial pressure (PAP) through cardiac catheterization, it is inappropriate for delicate newborns. Hence, echocardiography examination has become the most common inspection tool for NPH despite its limitations.
Methods After outlining the conventional echocardiographic parameters for detecting NPH and their drawbacks in newborns, this review mainly discussed the roles of two-dimensional speckle tracking echocardiography, including RV global longitudinal strain and segmental longitudinal strain, in the evaluation of NPH, hoping to provide more information for detecting NPH.
Results When combined with conventional echocardiographic parameters, RV longitudinal strain would be a great help for the evaluation of NPH. Furthermore, based on the preliminary research, our finding revealed that the magnitude of the apical segmental strain of RVFW was significantly lower, and the basal-to-apical strain ratio (Ratio bas/api) of RVFW was remarkably higher in infants with PH than those without PH.
Conclusion Based on the particularity of newborns, neonatal echocardiography is the preferred inspection method for NPH. It provides hemodynamic, morphological and functional information for evaluating NPH. RV longitudinal strain is sensitive to subtle changes of RV function and closely related to PH. It could be considered not only as the key factor affecting the prognosis of NPH but also as a potential index to detect and identify NPH.

Objectives Conventional ultrasound (US) elastography lacks specificity in distinguishing benign from malignant breast lesions. This study employed US to assess breast tissue viscosity and elasticity. The primary objective was to evaluate the diagnostic performance of US-derived viscoelasticity parameters. Secondary objectives included investigating the consistency of parameters in the mechanical properties of breast tissue.
Materials and methods Two doctors independently measured the viscosity and elasticity of specific positions in the breasts of 20 health females for consistency assessment. Then the doctors selected region of interest (ROI) to measure viscoelasticity. ROI-1, ROI-2, and ROI-3 represent the tumor, peritumoral, and peripheral areas, respectively. The viscosity modulus and elasticity modulus of 3 ROIs were analyzed. The viscosity and elasticity parameters with the highest area under the curve (AUC) were selected as the optimal ones. Finally, elasticity and viscosity parameters were combined to assess their diagnostic performance in differentiating breast lesions.
Results US viscoelasticity parameters can be measured with high consistency. Among conventional US elasticity parameters, 1-Emax demonstrated the highest AUC (0.746) for differentiating benign and malignant breast lesions. In US viscoelasticity parameters, 2-Emax achieved the highest AUC of 0.801, while 2-Vmax showed the highest AUC of 0.835. The highest specificity (0.903) was observed when both 2-Emax and 2-Vmax exceeded their respective cutoff values (P < 0.05 for all).
Conclusion Quantitative ultrasound viscoelasticity parameters play a crucial role in breast cancer diagnosis, with tumor boundary parameters being particularly significant for cancer screening and prevention strategies.

Objective To improve infant outcomes and guide treatment decisions, early and accurate diagnosis of congenital abnormalities during pregnancy is crucial. Despite its excellent accuracy, chorionic villus sampling (CVS) has procedural dangers; sonography offers a non-invasive, safer substitute. With an emphasis on clinical value, safety, and diagnostic accuracy, this evaluation assesses how well sonography performs in identifying congenital diseases prior to 12 weeks of gestation when compared to CVS.
Methods A comprehensive review of the literature was conducted using databases such as Web of Science, PubMed, and Scopus. Studies published between 2015 and 2024 that examined the diagnostic sensitivity, specificity, and accuracy of sonography and CVS for congenital illness identification were included.
Results With a sensitivity of 85-90%, sonography shows excellent accuracy in identifying anatomical abnormalities such organ malformations and nuchal translucency. Although CVS has a 0.5-1% chance of miscarriage, it is still the gold standard for identifying chromosomal abnormalities with an accuracy of around 99%. Combining the two modalities reduces hazards while improving diagnostic accuracy.
Conclusion For low-risk populations in particular, sonography provides a dependable, non-invasive screening method for congenital abnormalities prior to 12 weeks. For high-risk instances that need genetic investigation, CVS is advised. Integration of both approaches could optimize prenatal diagnostic protocols.

Objective Aortic stenosis (AS), a prevalent valvular disease, demands accurate diagnosis. Current methods, notably Doppler echocardiography, face limitations like dynamic image challenges and reliance on cardiologist experience. To assess aortic stenosis, measuring the LVOT diameter is critical, as a 1 mm difference can result in a 10% variation in stroke volume. Accurate Doppler beam alignment and LVOT VTI measurement are also essential to avoid errors. Our study, utilizing the TMED 2 dataset, introduces a novel artificial intelligence program for precise aortic stenosis diagnosis. By leveraging AI, we aim to overcome existing constraints and significantly enhance diagnostic accuracy.
Methods a novel method that involves using convolutional neural networks (CNNs), were used to grade AS based on various views of transthoracic echocardiography (TTE) images from the TMED 2 dataset. This innovative method aimed to take advantage of CNN’s abilities to recognize detailed patterns in echocardiographic data, making AS diagnosis more accurate. We evaluated the performance of our CNN models using confusion metrics and the area under the receiver operator curve (AUROC).
Results Our CNN networks were trained on a dataset comprising view_and_diagnosis_labeled_set, which included 599 studies from 577 unique patients (some with multiple studies on distinct days). For classification, we chose three classes: no aortic stenosis, aortic stenosis, and mild aortic stenosis. The detection of aortic stenosis achieved an accuracy of 85.74%. External validation using three views (PLAX, PSAX, and A4C) of outpatient transthoracic echocardiograms demonstrated effective screening for AS, yielding respective AUROCs of 0.81, 0.88, and 0.48.
Conclusion Our novel CNN-based approach achieved an 85,74% accuracy in AS detection using diverse views from the TMED 2 dataset. External validation on outpatient echocardiograms demonstrated robust screening capabilities, with AUROCs of 0.81, 0.88, and 0.48 for PLAX, PSAX, and A4C views, respectively. These promising results suggest the potential of AI in improving AS diagnosis for clinical applications. Moving forward, our future endeavors will focus on addressing data imbalances and detecting the view of images, in addition to assessing the severity of aortic stenosis, to further refine and optimize our diagnostic approach.

Objective To compare fetal cardiac morphology and function between small-for-gestational-age (SGA) and appropriate-for-gestational-age (AGA) fetuses using two-dimensional speckle-tracking echocardiography (2D-STE), and to evaluate global longitudinal strain (GLS), global sphericity index (GSI), and fractional area change (FAC) in both ventricles with FetalHQ software.
Methods This cross-sectional observational study included 101 pregnant women, comprising 36 with SGA and 65 with AGA fetuses. Five- to fifteen-second four-chamber view (4CV) cine loops of the fetal heart were acquired and analysed using fetal heart quantification and speckle tracking (FetalHQ) software. GLS, GSI, and FAC of both left ventricle (LV) and right ventricle (RV) were measured.
Results SGA fetuses demonstrated significantly lower GSI values, consistent with a more globular cardiac shape. LV-FAC and RV-FAC were significantly lower in SGA compared with AGA fetuses, reflecting impaired systolic function. Both LV-GLS and RV-GLS values were significantly higher (less negative) in the SGA group, indicating early biventricular systolic dysfunction. These findings align with previously reported adaptive responses of the fetal myocardium to chronic hypoxia.
Conclusion The study highlights distinct alterations in fetal cardiac morphology and function between SGA and AGA groups. FetalHQ-based deformation analysis may potentially detect subclinical biventricular dysfunction in SGA fetuses before Doppler abnormalities become apparent, offering potential for earlier clinical intervention and closer monitoring.

Objective Uterine and ovarian development is influenced by both age and hormonal milieu. Sonographic assessment of normal pubertal and pre-puberty girls provides critical insights into the physiological trajectory of female gonadal maturation and its potential pathological deviations.
Methods This was a cross-sectional study conducted at Gilani Ultrasound Center, Lahore, Pakistan. The duration of the study was 9 months. Uterus length, width, height, volume, right/left ovary volume and Fundo/Cervical ratio were measured.
Results A total of 384 subjects were included in our study, categorizing them into pre-puberty (age 2-6 years), early puberty (age 7-11 years), and late puberty (age 12-18 years) groups. In the pre-puberty group (n = 111, mean age 4.29 ± 1.20 years), uterine measurements revealed a mean length of 2.95 cm, height of 0.97 cm, width of 1.41 cm, and volume of 4.82 cm³. The mean volumes of the right and left ovaries were 1.24 cm³ and 1.10 cm³, respectively, with a mean F/C ratio of 1.33. For the early puberty group (n = 99, mean age 9.15 ± 1.45 years), uterine measurements included a mean length of 4.03 cm, height of 1.22 cm, width of 1.80 cm, and volume of 12.37 cm³. In the late puberty group (n = 31, mean age 12.63 ± 1.21 years), uterine measurements showed a mean length of 5.29 cm, height of 1.82 cm, width of 2.65 cm, and volume of 31.11 cm³. The mean volumes of the right and left ovaries were 4.70 cm³ and 5.26 cm³, respectively, with a mean F/C ratio of 1.26.
Conclusion Uterine and ovarian dimensions, including volumes, correlate directly with age and pubertal status, except for the (Fundus/Cervical) ratio, which shows individual variability. This normative data could serve as a basis for the evaluation of Uterine and ovarian dimensions and volume in the local population.

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 medicine is an interdisciplinary field that integrates ultrasonics and medicine, encompassing the applications of ultrasound in medical diagnosis, therapy, and basic research. While classical acoustic theories and technologies have reached a developmental bottleneck, their convergence with physics, artificial intelligence (AI), and related advanced technologies has spawned a dynamic research landscape defined by ultra-microscale precision and extreme interdisciplinarity. This paper presents a comprehensive systematic review of sound field modulation theories and their cutting-edge advances in ultra-microscale and highly interdisciplinary biological research. Leveraging acoustic metamaterials, microbubble dynamics, and acoustic streaming coupling effects, breakthroughs have been achieved in deep subwavelength diffraction imaging and precise nanoscale/microscale manipulation at extreme deep subwavelength resolutions. These innovations are fueling biophysical revolutions—including mechanical loading of biomolecules and regulation of ion channel proteins—while enabling breakthroughs in emerging technologies such as sonogenetics and non-invasive ultrasound-based brain-computer interfaces (BCIs). In the future, acoustics is poised to generate disruptive technologies in areas such as artificial structures and devices, non-invasive BCIs, cell and molecular regulation, micro- and nano-imaging/manipulation, and targeted drug delivery. Its unique characteristics—wavelength tunability and cross-scale integration—will continue to drive the deep fusion of physics, biology, and information science, fostering unexploited interdisciplinary synergy.

Ultrasound localization microscopy (ULM) is an ultrasound technique capable of overcoming the acoustic diffraction limit to achieve super resolution imaging of microvasculature, simultaneously balancing imaging depth and resolution. Abdominal organs are rich in microvasculature, and pathological processes in these organs are often accompanied by microvascular alterations, such as in tumors, chronic liver and kidney diseases, and allograft. Therefore, for abdominal organs, ULM represents a promising tool for aiding disease diagnosis and monitoring. Currently, an increasing number of studies are exploring the preclinical and clinical applications of ULM in both healthy and diseased abdominal organs. This paper aims to provide a systematic review of ULM applications in abdominal organs, while briefly discussing its limitations and future prospects.

This pictorial review summarizes the Contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) Treatment Response Algorithm (LR-TR, v2024) for response assessment after nonradiation locoregional therapies (NLT). The NLT covered by LR-TR v2024 includes embolization procedures such as conventional transarterial chemoembolization (cTACE), drug-eluting bead TACE (DEB-TACE), and bland transarterial embolization (TAE), as well as ablation techniques such as radiofrequency ablation (RFA), microwave ablation (MWA), and percutaneous ethanol injection (PEI). The algorithm independently evaluates intralesional and perilesional viability, using arterial-phase enhancement as the dominant criteria for intralesional evaluation and multiphasic enhancement (arterial, portal, and late phases) for perilesional evaluation. The results are then integrated into three standardized response categories, including LR-TR Viable, Equivocal, or Nonviable. Evidence from multicenter studies in hepatocellular carcinoma (HCC) indicates that CEUS LR-TR v2024 provides high reliability and strong reproducibility in detecting residual viable tumor following NLT. This review provides representative imaging features and interpretation tips to familiarize physicians with CEUS LR-TR v2024, aiming to improve accuracy in treatment response assessment (TRA) in HCC and facilitate timely therapeutic adjustments that ultimately benefits patients.

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.

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.

Early detection of hepatocellular carcinoma (HCC) is crucial, as patient outcomes depend largely on clinical stage and treatment options at diagnosis. Currently most clinical guidelines emphasize non-invasive cross-sectional imaging for HCC diagnosis and evaluation, yet the full potential of ultrasound is often underexplored. With the development of emerging ultrasound techniques and diagnostic algorithms in recent years, the diagnostic ability of ultrasound for HCC smaller than 3 cm has been substantially improved and is increasingly emphasized in the management of small HCC. Following sequential clinical scenarios, this review discusses the state-of-the-art of ultrasound-related techniques and algorithms for preoperative screening and diagnosis, intraoperative guidance and monitoring as well as posttreatment efficacy evaluation of small HCC. By highlighting how advanced ultrasound complements and enhances standard practices, this review aims to promote ultrasound as an indispensable, versatile, and dynamic tool in the precision management of small HCC.

Multimodal ultrasound, including B-mode imaging, contrast-enhanced ultrasound (CEUS), and ultrasound-based elastography, has demonstrated significant value in evaluating both diffuse liver diseases such as fibrosis and steatosis, and focal liver lesions such as hepatocellular carcinoma (HCC). Radiomics, including both handcrafted radiomics and deep learning approaches, has emerged as a promising strategy to enhance ultrasound-based liver disease assessment. Recent studies have applied radiomics across multimodal ultrasound, achieving notable success in grading fatty liver disease, staging fibrosis, and improving diagnosis, risk stratification, and prognostic prediction in HCC. Multimodal ultrasound provides complementary information on liver morphology, perfusion, and stiffness, while fusion strategies further enhance diagnostic accuracy and robustness. Future efforts should focus on standardized, large-scale multicenter validation, methodological improvements in multimodal integration, and the incorporation of explainable artificial intelligence to support clinical translation. Ultimately, despite ongoing challenges related to data heterogeneity, reproducibility, interpretability, and clinical validation, multimodal ultrasound radiomics holds strong promise for noninvasive, individualized, and clinically meaningful liver disease management.

Recent advancements in ultrasound technology have revolutionized both medical imaging and therapeutic applications. Among these, volumetric imaging using two-dimensional (2D) array ultrasound transducers has emerged as a powerful tool, enabling real-time three-dimensional (3D) visualization, which is also referred to as four-dimensional (4D) imaging. 4D ultrasound imaging represents the most advanced diagnostic technique in ultrasound and is considered one of the most essential tools for medical diagnostics, particularly for assessing blood flow in micro-sized blood vessels. Due to its real-time and volumetric imaging capabilities, 4D imaging offers a unique advantage for the early diagnosis of cardiovascular and cerebrovascular diseases. The row-column-addressed (RCA) array is a novel 2D ultrasound transducer designed for ultrafast 3D ultrasonic imaging. Compared to traditional fully-sampled 2D matrix arrays, RCA transducers reduce the number of electronic channels from M×N to M+N, thereby significantly lowering hardware costs and manufacturing complexity. This review explores the design, fabrication, and clinical applications of 2D arrays, including both fully-sampled 2D arrays and RCA arrays. We discuss their roles in cardiology, brain imaging, and interventional procedures, while also addressing current challenges and future developments in the field.

Recent advancements in artificial intelligence (AI) have generated novel opportunities and challenges in ultrasound imaging. Deep learning algorithms exhibit significant potential in analyzing echocardiographic images, encompassing tasks such as view classification, quantification of cardiac function, and the diagnosis and risk assessment of cardiac diseases. The “black box” nature of AI models limits their clinical applications. Adopting explainable artificial intelligence (XAI) methods is crucial for improving the transparency and understanding of model predictions. This paper reviews the progress of AI applications in echocardiography, with a particular emphasis on XAI as a technical solution to enhance the transparency of model decision-making and its benefits compared to traditional AI models. This review outlines recent advancements in XAI applications for echocardiography and their clinical implications.

Artificial Intelligence (AI) technology has made remarkable progress in fetal ultrasound examinations, particularly excelling in fetal growth monitoring, organ function assessment, and early disease diagnosis. By automating the analysis of fetal ultrasound images, AI can accurately measure fetal biometric parameters and assist in diagnosing issues such as fetal growth restriction and organ developmental abnormalities. It demonstrates significant application potential in evaluating multiple organ systems including the fetal lungs, nervous system, cardiovascular system, and placenta, substantially enhancing the efficiency and accuracy of prenatal screening. This paper aims to review the current status of AI applications in obstetric ultrasound, while also exploring its limitations and future prospects.

Pulsed field ablation (PFA), a non-thermal ablation technique that induces cell death via irreversible electroporation, has emerged as a promising therapeutic strategy for treating tumors located in anatomically complex regions. This review comprehensively examines recent developments in PFA, including its mechanisms foundations, established clinical applications in pancreatic, prostate and liver cancer, and expanding utility with other tumors, etc. Furthermore, we discuss synergistic approaches combining PFA with chemotherapy, immunotherapy, surgery and radiotherapy to augment therapeutic outcomes. In addition, technological innovations—such as robotic assistance, magnetic anchoring, and artificial intelligence—that improve precision and reproducibility are also explored. Despite encouraging clinical results, broader implementation of PFA necessitates higher-quality evidence from large-scale randomized trials and standardized treatment protocols. This review highlights the transformative potential of PFA in oncology while addressing contemporary challenges and future research directions to facilitate clinical translation.

Biomedical ultrasound imaging, as one of the most common, safe, and cost-effective modalities in clinical diagnosis, witnesses remarkable progress with the integration of artificial intelligence (AI). Early studies based on traditional machine learning (ML) rely on handcrafted features and classical classifiers to achieve automatic recognition and quantitative analysis of ultrasound images. However, such methods are limited in feature representation capacity and generalizability. With the advent of deep learning (DL), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention-based architectures are widely applied to tasks such as segmentation, detection, and lesion classification, significantly improving diagnostic accuracy and robustness. More recently, large language models (LLMs) and multimodal foundation models open new avenues for intelligent ultrasound analysis. These models not only integrate imaging and textual information to support automated report generation and cross-modal reasoning but also offer enhanced interpretability and greater potential for clinical adoption. In this review, we provide a systematic review of the evolution of AI in ultrasound image analysis, spanning from traditional ML to deep learning and LLMs, outlining a complete trajectory of methodological advances.

In recent years, dermatological ultrasound has made significant breakthroughs in the research and application of dermatology by virtue of its advantages of non-invasive, real-time imaging and high resolution. The increase of ultrasonic probe frequency makes it possible to visualize the skin structure. Different frequencies of high frequency or ultra-high-frequency ultrasound can realize different degrees of presentation of the fine structure of epidermis, dermis and subcutaneous layer. The wide range of application of shear wave elastography provides a new ultrasonic evaluation index for skin diseases represented by scleroderma. This review provides a detailed summary of the latest application progress and research frontiers of ultrasound in different skin diseases. It includes the diagnosis and efficacy evaluation of scleroderma, the diagnosis and activity evaluation of psoriasis, the diagnosis and follow-up of nevus punctatum, the differentiation of benign and malignant skin tumors, the evaluation of pathological scar, the application in the field of aesthetic medicine, and the evaluation of other common skin diseases. In the future, dermatological ultrasound is expected to play a more important role in accurate classification of diseases and prognosis prediction with the further innovation of equipment and the deep exploration of skin diseases.

Photoacoustic-ultrasound (PA/US) fusion imaging is an emerging dual-modality technique that integrates the high optical contrast of photoacoustic imaging (PAI, also referred to as optoacoustic imaging or photoacoustic tomography) with the anatomical resolution of US. This review summarizes the principles, technical advances, and clinical applications of PA/US in breast cancer diagnosis and therapy. Recent studies demonstrate that PA/US markedly improves diagnostic specificity while maintaining high sensitivity, particularly in differentiating benign from malignant lesions, predicting molecular subtypes, and monitoring therapeutic response. Radiomics and artificial intelligence further enhance the interpretive power of PA/US, enabling functional phenotyping, Ki-67 expression prediction, and axillary lymph node (ALN) metastasis risk assessment. Moreover, multicenter clinical trials, such as the PIONEER study, have validated the clinical feasibility of PA/US, reducing unnecessary biopsies and refining BI-RADS categorization. Despite challenges in system standardization, quantitative accuracy, and large-scale validation, PA/US holds promise as the “fourth major breast imaging modality,” complementing mammography, US, and MRI. With continued progress in AI integration, standardized protocols, and policy recognition, PA/US is expected to achieve routine clinical implementation in the next 5-10 years, supporting individualized breast cancer diagnosis and precision oncology.

Recent advancements in medical imaging have greatly enhanced our understanding of tissue structure and disease mechanisms. Habitat imaging, which segments imaging data into distinct spatial subregions or "habitats," offers valuable insights into the heterogeneous nature of tumors, challenging traditional treatment strategies and supporting precision medicine. Super-resolution ultrasound (SRUS) has emerged as a promising tool for habitat imaging by exceeding the diffraction limits of conventional ultrasound, thus enabling visualization of microcirculation at the micron scale. Unlike MRI, CT, and PET, SRUS offers superior resolution in depicting microvascular structures, providing complementary information that enhances our understanding of tissue perfusion and microcirculatory heterogeneity. SRUS-based habitat imaging can delineate vascular habitats with high precision, supporting dynamic analysis and offering potential benefits in oncology, such as assessing tumor aggressiveness and monitoring therapeutic responses. As SRUS technology continues to mature, it is poised to become an integral part of personalized medicine, with future studies focusing on standardizing protocols and validating biomarkers to integrate SRUS into routine clinical practice.

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.

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.

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.

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.

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.

Background Intraplaque neovascularization is a biomarker of vulnerable plaque. However, no data are available whether the increase in neovascularization within carotid plaques is a result of ischemia or an increase in adventitial vasa vasorum (VV).

Objective To evaluate the VV signal in carotid vulnerable plaques.

Methods Contrast-enhanced ultrasound (CEUS) examination was performed to examine changes in VV density in 47 patients with carotid plaque, and 21 patients received CT angiography (CTA) examination to assess the VV signal. In addition, a single-channel flow tissue model was fabricated for use in vitro studies to exclude pseudo-enhancement interferences in the distal wall of arteries by CEUS.

Results The intensities of adventitial VV behind carotid plaque were lower than that of adventitial VV at the same level adjacent to the plaque in both CEUS and CTA examinations (P < 0.05). In vitro study, the intensities of far wall increased as the microbubble concentration increased (P < 0.05). However, no significant differences of intensities of far wall were found between different thicknesses tubes at the concentration of microbubble concentrations of 0.3% and 0.5% (P ≥ 0.05).

Conclusion The formation of intraplaque neovascularization in carotid arteries is associated with the adventitial VV, and ischemia of VV may be a potential mechanism for intraplaque neovascularization.

Objective Antenatal vaginal bleeding, particularly during the first trimester, is worrisome for obstetricians. The common causes are all types of abortions, including molar and ectopic pregnancies. The aim is to evaluate the obstetric causes of vaginal bleeding during the first trimester.

Methods The study population comprises 100 pregnant women with complaints of vaginal bleeding during their first trimester period. These patients were subjected to ultrasound examination to diagnose the causes of bleeding. Patients with 12 completed weeks of gestation and non-obstetrical causes of vaginal bleeding were excluded.

Results The study population of 18-34 years had complained of vaginal bleeding during their first trimester of pregnancy. Most 57% were in the age group of 20-24 years. Forty-two percent of the study population presented at ten weeks of amenorrhea. Out of 100 cases, the majority (58%) were diagnosed as threatened abortion, 31% cases were diagnosed as incomplete abortion, 4% cases were diagnosed as complete abortion, 2 (2%) cases each were diagnosed as ectopic gestation, inevitable and missed abortions, and 1 (1%) case diagnosed as Hydatidiform mole. Out of 100 patients, the gestational sac was seen in 75 (75%).

Conclusion Antenatal ultrasonography is helpful in accurate and early diagnosis of the causes of vaginal bleeding during the first trimester. This aids the obstetrician in selecting the best treatment planning and helps with prognosis prediction, establishing an accurate diagnosis in a few clinically misdiagnosed cases.

Photoacoustic imaging (PAI) is a functional optical imaging modality that utilizes ultrasound as a medium. Owing to its high contrast in optical imaging and deep tissue penetration capabilities inherent in ultrasound, PAI can generate images that integrate both structural and functional information. It has emerged as a novel medical imaging tool, with ongoing research continually expanding its applications within the medical field. The integration of photoacoustic imaging with other modalities to create multimodal imaging systems allows for the synergistic advantages of various technologies, thereby providing more comprehensive diagnostic information. PAI facilitates early and precise diagnosis as well as treatment monitoring for diverse conditions such as tumors, inflammation, and skeletal muscle injuries through real-time quantitative analysis of deoxyhemoglobin levels and molecular markers. This article elucidates the principles of PAI, its various modes of operation, and clinical applications while also anticipating future developmental prospects.

Objective Biliary atresia is a significant cause of neonatal pathological jaundice, demanding effective interventions such as the Kasai procedure to impede its advancement. Previous research highlights the potential of shear wave elastography for assessing liver fibrosis and the subsequent necessity for liver transplantation following Kasai procedure. This underlines the significance of our study in investigating shear wave elastography as a predictive tool for the success of Kasai procedure in biliary atresia patients.

Methods This retrospective case-control comparative study analyzed data from biliary atresia patients who underwent shear wave elastography ultrasound and the Kasai procedure at our center from 2020 to 2022. Successful Kasai outcomes formed the case group; unsuccessful, the control. We calculated the mean shear wave elastography values for each group and established a predictive Kasai success cut-off using SPSS for statistical analysis.

Results Twenty-one subjects, with 8 males and 13 females (median age: 82 days), were evaluated. Of the 21 subjects, 9 (42.9%) had successful Kasai outcomes, while 12 (57.1%) were unsuccessful. There are statistically different values between two groups, such as the shear wave elastography value (P = 0.001). The optimal cut-off point of shear wave elastography value to predict the success of Kasai procedure is 2.21 m/s or 14.4 kPa (sensitivity 88.9%, specificity 83.3%, accuracy 85.7%, PPV 87.65%, NPV 84.91%), with an AUC of 0.889 (95%CI = 0.75-1.00), OR = 10.50 (1.360-81.053).

Conclusion This study demonstrates shear wave elastography’s potential utility in predicting Kasai procedure success for biliary atresia patients, suggesting its role as a valuable prognostic tool.

Lymphadenopathy is a common clinical disease, and ultrasonography is its primary preliminary diagnostic screening strategy. With an increase in house-raised pets, the annual incidence of cat scratch lymphadenitis is rising. After infection, the characteristics of the disease include abnormal lymph node enlargement in the local drainage, accompanied by low heat sweats, similar to clinical symptoms of malignant disease. The two-dimensional ultrasound results lack specificity. However, garland-like variation can be observed in the enhanced images, which can be used for the differential diagnosis of cat scratch lymphadenitis. In this case, we obtained the ultrasound and computed tomography images of a patient with cat scratch lymphadenitis and compared and analyzed them with the pathological data.

Objective: Simple hepatic steatosis can no longer be ignored as a "benign finding", and the management and evaluation of the clinical interventions depend on the degree of hepatic steatosis. Here, we aimed to investigate the feasibility and diagnostic performance of thermoacoustic imaging (TAI) for assessing hepatic steatosis grades in a rabbit model.

Methods: High-fat diet was used for the rabbits. To collect various degrees of hepatic steatosis, the diet duration was different (4, 8, 12, 16, 20, and 24 weeks). An in-vivo liver TAI imaging system was developed. At the end of the feed point, rabbits underwent the TAI and laparotomy for liver histopathology.

Results: We performed TAI and histopathologic examinations for 33 rabbits developing none (n = 4), mild (n = 16), moderate (n = 6), and severe (n = 7) steatosis with/without hepatic fibrosis. A strong correlation was found between the thermoacoustic fat coefficient (TAFC) derived from TAI and liver fat percentage (Pearson correlation coefficient, 0.865; P < 0.001). Besides, TAFC showed significant differences between the consecutive grades of steatosis. TAI potentially provided a good diagnostic performance, with 83% sensitivity and 100% specificity for mild steatosis, 92% sensitivity and 95% specificity for moderated steatosis, and 100% sensitivity and 92% specificity for severe steatosis. The fibrosis stage did not significantly affect the TAFC.

Conclusion: Our findings demonstrate that TAI is a promising way to evaluate hepatic steatosis grades.

Stroke significantly impacts national health due to its high incidence, disability, mortality, and recurrence rates, resulting in a substantial economic burden. Risk prediction models for ischemic stroke help identify high-risk populations for early prevention, diagnosis, and treatment. Various risk-scoring models have been developed for primary and secondary prevention of ischemic stroke, estimating the probability of cardiovascular events over a specified timeframe based on the presence of known risk factors. However, these risk-scoring models often lack precision for cardiovascular disease risk assessments across diverse baseline risk conditions. Integrating image-based biomarkers into existing risk-prediction models may enhance risk stratification accuracy. This review presents the most used models for ischemic stroke prediction and underscores the clinical utility of biomarkers in the management of ischemic stroke.

Heart transplantation (HT) is a definitive treatment for end-stage heart failure, significantly improving both the quality of life and survival rates of HT recipients (HTx). Speckle tracking echocardiography (STE), a key non-invasive diagnostic method, has become indispensable for providing an in-depth analysis of myocardial mechanics and function. This review focuses on the clinical utility of STE in both pre- and post-transplant settings. The ability of STE to identify subtle cardiac abnormalities and predict post-transplant outcomes underscores its critical role in the clinical management of HTx.

Stroke is a critical condition marked by the death of brain cells due to inadequate blood flow, necessitating improved predictive models for stroke lesions. The accuracy and flexibility required to forecast and classify stroke lesions is lacking in current approaches, which compromise patient outcomes. To solve these issues, Bille-Viper-Segmentation with the Tandem-MU-Net Model is suggested as a solution for tissue damage detection problems. This study improves blood flow detection in stroke images by introducing the Bille-Viper-Segmentation method to overcome difficulties in recognizing tissue injury. This novel method effectively samples pixel data and analyzes fogging phases related to stroke lesions by utilizing a Deep Luxe Gauging Tree. Existing methods struggle with flexibility in varying conditions; thus, the Trans-Lucent-Rich Reprise Pattern recognition algorithm for precise identification of infected areas is introduced. Furthermore, the Focus View Algorithm is suggested, which incorporates features from infarcted regions to improve early detection of emerging lesions. Furthermore, the Tandem-MU-Net model is used to extract essential morphological features and categorize stroke types, including Hemorrhagic and Acute strokes, through an investigation of their neutral and ionic forms. The results show that the suggested model performs substantially better than existing methods, achieving an amazing accuracy rate of 75%, recall rate of 83%, F1 score of 98%, Dice score of 98%, and precision of 73%, all while operating effectively in a time frame of 250 seconds.