Objective To explore the quality control value of 5G remote ultrasound robot in normal breast imaging. Methods Fifty female volunteers from Xizang were selected to undergo routine breast examinations using 5G remote ultrasound robot (robot group) and on-site ultrasound (control group). Longitudinal and transverse continuous scanning methods were used to collect and save images respectively. The two groups of images were scored for breast imaging quality by five senior attending physicians or above using a blind method according to the Likert scale grading standard. The paired t-test was used to analyze the differences in breast imaging quality scores obtained by five physicians between the two examination methods and the age differences between the two groups of volunteers. The Kappa test was used to analyze the consistency of the image scoring results between the two groups. The u03C72 test was used to analyze the ethnic differences between the two groups of volunteers. Results There is no significant age difference between the two groups of volunteers (t = u22120.850, P = 0.101, P u0026gt; 0.05). There is no significant difference in the ethnic proportions between the two groups of volunteers (P = 1.00, P u0026gt; 0.05). There was no statistically significant difference in the scores of the five physicians between the two groups of images (P u0026gt; 0.05), Doctor A (t = u22121.647, P = 0.101), Doctor B (t = u22120.943, P = 0.347), Doctor C (t = 0.582, P = 0.561), Doctor D (t = 0.040, P = 0.958), Doctor E (t = 0.144, P = 0.886). The consistency of the scoring results of the five doctors on the two groups of images was good (Kappa = 0.789, 0.753, 0.807, 0.778, 0.823, P u0026lt; 0.001). Conclusion The remote ultrasound robot has good consistency with the on-site ultrasound imaging quality. The remote ultrasound robot can be used for breast cancer screening in special environments such as high altitude and cold weather.
Ultrasound has become the primary modality for fetal central nervous system examination and diagnosing malformations. However, the effectiveness of fetal brain examinations remains highly operator-dependent. Deep learning, a key branch of artificial intelligence (AI), has demonstrated significant advantages in image recognition, proving particularly valuable in medical imaging. Consequently, several studies have proposed the use of deep learning models as tools for fetal brain ultrasound examinations. AI has achieved clinical applications in fetal brain ultrasonography, encompassing standard plane recognition, biometric measurements, structural identification, and malformation diagnosis. This review systematically analyzes the applications of AI in fetal brain ultrasound examination and discusses unmet clinical needs and future development.
Axillary lymph node (ALN) metastasis is a critical factor influencing prognosis and treatment strategies in breast cancer patients. However, traditional methodsu2014ranging from physical examination to ultrasoundu2014often lack the precision required for clinical decision-making. In recent years, ultrasound radiomics and deep learning have emerged as promising solutions, leveraging high-throughput quantitative features from ultrasound images to enhance detection accuracy. This review explores the development and application of radiomics and deep learning across multiple ultrasound modalities (grayscale, elastography, and contrast-enhanced ultrasound), as well as in multimodal imaging approaches that integrate ultrasound with MRI and PET/CT, underscoring the benefits of incorporating clinicopathological variables to boost predictive performance. These studies provide a vital foundation for personalized treatment and precision medicine in breast cancer management.
Background US-guided tube thoracostomy (US-TT) is widely employed for pleural effusion management, however bleeding complications remain a clinical concern.Objective To identify risk factors for bleeding during US-TT, focusing on doctor experience, coagulation status, malignancy, and anticoagulant/antiplatelet medication use.Methods In this retrospective cohort study, we analyzed 4,073 patients undergoing US-TT between January 2019 and January 2024. Using univariate analysis and multivariate binary logistic regression, we evaluated associations between post-procedural bleeding and the following factors: coagulation parameters (PLT, INR, APTT), inflammatory markers, use of anticoagulant/antiplatelet medications, malignancy status, and doctor experience (junior vs. senior). Demographic and clinical data were extracted from electronic medical records.Results Bleeding occurred in 24 patients (0.6%). Univariate analysis identified procedure performance by junior physicians and presence of malignancy as significant risk factors (P < 0.05). No significant associations were found with coagulation parameters, inflammatory markers, or medication use. Factors with P < 0.1 (junior doctor, malignancy, INR > 2.0, elevated leukocyte count) were included in multivariate analysis. This confirmed junior doctor status (OR, 3.333; P = 0.033) and malignancy (OR, 3.960; P = 0.016) as independent predictors of bleeding.Conclusions US-TT carries a low overall bleeding risk (0.6%). In this study, bleeding was associated with malignancy and less experienced doctors, but not significantly with routine coagulation indices. A trend toward increased risk with INR > 2.0 warrants caution.
Objective To evaluate the feasibility and safety of water–air sequential infusion assisted Ultrasound-Guided Percutaneous Gastrostomy (UGPG) in a patient with complete esophageal obstruction.Methods A 72-year-old male patient with esophageal squamous cell carcinoma presented with complete esophageal obstruction and severe malnutrition. Endoscopic and fluoroscopic gastrostomy were not feasible, and the patient was unable to tolerate general anesthesia. A purely ultrasound-guided percutaneous gastrostomy was performed. The procedure consisted of initial infusion of a small volume of normal saline into the gastric antrum to facilitate identification of the gastric lumen and placement of a pigtail drainage catheter, followed by continuous air insufflation through the catheter to achieve adequate gastric distension and close apposition of the anterior gastric wall to the abdominal wall. Under real-time ultrasound guidance, gastric wall fixation and gastrostomy tube placement were successfully completed.Results The procedure was completed smoothly with satisfactory gastric distension and a clear, safe puncture pathway. The gastrostomy tube was accurately positioned, and no perioperative complications, including bleeding, infection, or peritonitis, were observed. Enteral nutrition was initiated 24 hours after the procedure and was well tolerated.Conclusion Water–air sequential infusion–assisted UGPG is a feasible and safe alternative for gastrostomy in patients with complete esophageal obstruction who are unsuitable for endoscopic or fluoroscopic approaches. This radiation-free, minimally invasive technique avoids the need for general anesthesia and may represent a valuable option for high-risk or critically ill patients. Further studies with larger sample sizes are warranted to validate its clinical utility.
Objectives This study aims to explore the diagnostic effectiveness of sound touch elastography (STE), visco-elastography, and ultrasound attenuation analysis (USAT) in some chronic liver diseases (CLDs).Materials and Methods From February 1st to September 30th, 2023, participants underwent multiple examinations on the same day, including sound touch elastography (STE), visco-elastography, ultrasound attenuation analysis (USAT), FibroScan-VCTE, and FibroScan-CAP. Statistical analysis utilized t-tests and rank sum tests to compare differences between the hepatic fibrosis and non-hepatic fibrosis groups, the hepatocellular injury state and non-hepatocellular injury state groups, and the fatty liver and non-fatty liver groups. The ROC curve of sound touch elastography, visco-elastography, and ultrasound attenuation analysis was calculated to obtain cut-off values, area under the curve, and corresponding sensitivity and specificity values.Results This study comprised 310 patients, with age, height, alanine aminotransferase, ultrasound attenuation analysis, and FibroScan-CAP identified as significant variables for liver fibrosis, hepatocellular injury state, and non-alcoholic fatty liver disease. The determined cut-off values for sound touch elastography, visco-elastography, and ultrasound attenuation analysis were 8.09 (7.30-9.17), 1.62 (1.49-1.74), and 0.68 (0.59-0.68), respectively. Based on these cut-off values, preliminary diagnoses can be made for seven different types of chronic liver diseases.Conclusion The combination of sound touch elastography, visco-elastography, and ultrasound attenuation analysis demonstrates strong reliability for early screening and diagnosis of chronic liver diseases, encompassing liver fibrosis, non-alcoholic fatty liver, and hepatocellular injury state.
Background Hepatic portal venous gas (HPVG) is a critical imaging finding, often indicative of an acute abdominal catastrophe of gastrointestinal origin. The condition progresses rapidly and is associated with an extremely high mortality rate. Conventional conservative management or surgical intervention carries significant risk, particularly for perioperative patients.Case Summary A 64-year-old female patient was admitted to our hospital on August 1, 2023, with a chief complaint of abdominal pain, distension, and cessation of defecation and flatus for one day. The diagnosis was small bowel obstruction, for which an endoscopic nasojejunal feeding tube placement was performed. On August 30, a follow-up abdominal CT scan revealed intrahepatic biliary duct dilation and HPVG. Due to clinical deterioration, the patient was transferred to the ICU. Following a multidisciplinary consultation, an ultrasound-guided portal vein puncture and catheterization was performed first. This intervention successfully alleviated the signs of HPVG, thereby reducing the risk for the subsequent laparotomy. The patient was ultimately successfully treated.Conclusions In this case, the hepatic portal vein was directly punctured under ultrasound guidance, and a PICC catheter was inserted into the portal vein. A mixture of blood and gas was successfully aspirated post-puncture. An immediate post-procedural scan revealed a significant reduction of gas within the intrahepatic portal veins, alleviating HPVG and mitigating the risk for the subsequent laparotomy. This demonstrates that this method can effectively provide direct relief from HPVG and offers a novel therapeutic approach for the management of similar cases.
Breast cancer is one of the most prevalent cancers affecting women worldwide. Ultrasound is extensively utilized for clinical screening and diagnosis due to its affordability, absence of radiation, and rapid imaging capability. To enhance diagnostic accuracy, computer-aided diagnosis (CAD) systems have been developed, with segmentation and classification being key techniques. This review systematically examines 62 recent studies on breast ultrasound segmentation and classification, covering various imaging techniques such as B-mode, elastography, 3D ultrasound, contrast-enhanced ultrasound (CEUS), and color Doppler. specifically, we detail the challenges and deep-learning-based methods associated with these modalities. Comparative analysis reveals that current deep learning approaches typically achieve Dice coefficients ranging from 0.79 to 0.91 for segmentation and classification accuracies exceeding 88.2% in multimodal settings. Finally, this article identifies critical research gaps, including data scarcity and model interpretability, and discusses future directions such as multimodal fusion and explainable AI (XAI) to further improve clinical applicability.
Placenta is a vital organ of a unique circulatory system that allows the exchange of nutrients between maternal and fetal circulations, supporting fetal growth during pregnancy. The placental nutrient transfer capacity is precisely modulated by the signals originating from the fetus, mother, and placenta itself, thereby ensuring appropriate regulation of fetal growth. However, the abnormality of any link in this process may lead to the failure of the regulation, and thus the placenta can no longer meet the fetal demand, causing fetal growth restriction (FGR). This review investigates the morphological and functional alterations in the regulation of placental growth and development, as well as the uteroplacental circulation in human pregnancies complicated by FGR. Additionally, it discusses the ultrasound application in the assessment of placental insufficiency.
Hepatocellular carcinoma (HCC), constituting 75-85% of primary liver cancer cases, ranks as the third leading cause of global cancer-related mortality. The early diagnosis of HCC is critical for determining optimal clinical therapeutic strategies. Radiofrequency ablation (RFA) has emerged as a preferred for managing early-stage HCC cases, primarily due to its less invasive nature, simplicity and safety. Advanced imaging for pre-operative evaluation of HCC has been increasingly used. Contrast-enhanced ultrasound (CEUS) has improved the clinical application of ultrasound (US) in HCC. It also provide more accurate informations for guiding RFA procedures. In the future, CEUS, through its convergence with emerging artificial intelligence (AI)-driven technologies, will play an even greater role in HCC management. This systematic review evaluates the utility of Sonazoid-enhanced contrast ultrasound (Sonazoid-CEUS), particularly its unique Kupffer phase imaging, in improving HCC diagnostic and optimizing RFA precision via real-time monitoring capabilities and prolonged imaging windows (up to 60 minutes).
Gastroparesis (GP) is a gastric motility disorder characterized by delayed gastric emptying in the absence of mechanical obstruction. This expert consensus covers aspects of GP etiology (diabetes, surgery, drug-induced, idiopathic, scleroderma, etc.), symptomatology, diagnosis (scintigraphic imaging, ultrasound contrast meal assessment, and radiopaque marker gastric motility testing), and treatment (pharmacological therapy, nutritional support, traditional Chinese medicine, and interventions targeting the pylorus, such as endoscopic surgery and surgical procedures). GP significantly impacts patients' psychological well-being and quality of life, potentially leading to psychological disorders such as anxiety and depression, and also imposes a heavy economic burden on patients and society. Therefore, this expert consensus advocates for the establishment of a multidisciplinary team diagnosis and treatment model, which will further standardize and optimize the diagnosis and treatment process of GP in our country and is crucial for improving patients' therapeutic outcomes and quality of life.
Objective Perimenopause is the transition from the reproductive period to menopause, marked by a decline in the number and function of oocytes. Transvaginal ultrasound is a reliable method for assessing ovarian reserve. Ajwa dates (Phoenix dactylifera L.) contain macro- and micronutrients, phenolics, and flavonoids, which have antioxidant potential that may improve ovarian condition in perimenopausal women. This study aims to determine the effect of ajwa date consumption on ovarian follicle characteristics assessed by transvaginal ultrasound in perimenopausal women. Methods This quasi-experimental pre-post control group study was conducted at RSIA Sitti Khadijah I Muhammadiyah Makassar, Indonesia, from February to August 2023. A total of 44 perimenopausal women aged 42-48 years were randomly assigned to intervention (n = 28) or control groups (n = 16). Ovarian follicle changes were analyzed using the Mann-Whitney U test. Results The intervention group showed a decrease in follicle number with an increase in follicle size, while the control group showed an increase in follicle number and a decrease in size. These differences were statistically significant (P u0026lt; 0.05). Conclusion Consumption of ajwa dates as an exogenous antioxidant can influence the number and size of antral follicles, which serve as predictive indicators of menopausal transition.
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 u201Cblack boxu201D 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.
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 revolutionsu2014including mechanical loading of biomolecules and regulation of ion channel proteinsu2014while 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 characteristicsu2014wavelength tunability and cross-scale integrationu2014will continue to drive the deep fusion of physics, biology, and information science, fostering unexploited interdisciplinary synergy.
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.
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.
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.
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.
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.
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.
