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.

ObjectiveThe objective of this study was to develop a sonographic technique using two-dimensional (2D) markers for detecting isolated fetal cleft palate (no cleft lip) and to evaluate the ability of 2D and three-dimensional (3D) sonography to image the normal and abnormal palate.

Methods Seventy-three fetuses with a high risk of cleft palate at 12-39 weeks of gestation were referred for specialist ultrasound. A detailed evaluation of the palate was performed through 2D ultrasound, which revealed the appearance of the palatine line in the sagittal plane; the palate and alveolar ridge in the coronal plane of the fetal face; the horizontal plate of the palatine bone in the axial maxillary plane; and the soft palate in the transverse plane of the cavum pharyngis. Subsequently, 3D ultrasound imaging of the palate was performed in all fetuses. Antenatal diagnoses were compared with postnatal findings or autopsy findings.

Results Visualization of 2D markers was accomplished in all fetuses, and 3D assessment was achieved in 97% of fetuses. Cleft palate was suspected in 16 cases (21.9%), among which 14 were suspected on the basis of both 2D and 3D evaluation, and two were suspected only on the basis of 3D evaluation. A normal palate was observed in 57 fetuses (78.1%). The mean gestational age was 27 weeks (range of 12 weeks to 39 weeks). All 16 fetuses with suspected cleft palate were confirmed by postnatal or autopsy findings, no false-positives were observed, and one case with a bifid uvula was missed among 57 fetuses with a presumed normal palate.

Conclusions The fetal palate can be evaluated with 2D markers and 3D sonography. The detection of isolated cleft palate is more sensitive when 2D markers are present in all four planes.

Objective This study aimed to evaluate the development of atherosclerosis in ApoE-deficient dogs fed with a high-fat diet (HFD) using vascular duplex ultrasonography (VDU).

Methods Thirty beagle dogs were enrolled, including 10 wild-type, 16 heterozygous (ApoE^-/+), and four homozygous (ApoE^-/-) mutant dogs. The dogs were categorized into either the normal diet (ND) or HFD group. Plasma lipids levles were tested at baseline and then after feeding the dog a different diet for 6 months. The carotid arteries, abdominal aorta (AO) and iliac arteries were examined using VDU. Artery sections of the ApoE^-/- dogs were analyzed.

Results After HFD, lipids especially triglycerides, total cholesterol and low-density lipoprotein (LDL) in the wild type and ApoE^-/+ dogs were significantly increased. Both the intima-media thickness (IMT) of the common carotid artery (CCA) and AO in the wild type and ApoE^-/+ dogs significantly increased. In the ApoE^-/+ dogs, the mean percentages increases in CCA-IMT and AO-IMT after HFD were higher than those in the ND dogs. The mean values of CCA-IMT and AO-IMT in the ApoE^-/-dogs increased to 2-2.5 folds after HFD. Histological analysis confirmed that the carotid and iliac arteries had advanced atherosclerotic lesions in the ApoE^-/- dogs.

Conclusions HFD may accelerate the development of atherosclerosis in ApoE-deficient dogs, which is an optimal large-animal model of atherosclerosis.

Objective Aimed to evaluate patients with essential hypertension (EH) using four-dimensional automatic left atrial quantification (4DLAQ) To assess the occurrence of EH.

Methods This study selected 80 patients with EH for the EH group and 36 healthy individuals for the control group. Various cardiac parameters, including left atrial diameter (LAD), interventricular septal thickness (IVST), left ventricular end-diastolic diameter (LVDD), left ventricular posterior wall thickness (LVPWT), early E-wave velocity of mitral valve diastole/mitral valve ring myocardial displacement velocity (E/e'), biplanar left ventricular ejection fraction (biplanLVEF), left atrial minimum volume (LAVmin), lateral left atrial maximum volume (LAVmax), left atrial presystolic volume (LAVpreA), left atrial ejection fraction obtained by two-dimensional echocardiography (LAEF), left atrial passive ejection fraction (LAPEF), left atrial active ejection fraction (LAAEF), left atrial reservoir longitudinal strain (LASr), left atrial catheter longitudinal strain (LAScd), left atrial systolic longitudinal strain (LASct), left atrial reservoir circular strain (LASr_c), left atrial catheter circular strain (LAScd_c), and left atrial systolic circular strain (LASct_c) were measured using 4DLAQ. Binary logistic regression was employed to analyze the effect of 4DLAQ strain parameters on EH. Receiver operating characteristic (ROC) curves were used to assess the predictive value of 4DLAQ strain parameters for EH.

Results Systolic blood pressure and diastolic blood pressure in the EH group were higher than those in the control group (P = 0.000 and 0.000, respectively). In the EH group, LAD, IVST, LVDD, LVPWT, E/e', LAVmin, LAVmax, and LAVpreA were significantly increased (P = 0.000, 0.000, 0.072, 0.000, 0.000, 0.001, 0.052, and 0.004, respectively), whereas biplanLVEF, LAEF, LAPEF, LAAEF, LASr, LAScd, LASct, LASr_c, LAScd_c, and LASct_c significantly decreased (P = 0.090, 0.000, 0.009, 0.064, 0.000, 0.000, 0.000, 0.000, 0.000, and 0.689, respectively). Bland-Altman plots were used to illustrate the relationship between variables and audience consensus. LASr and LAScd were identified as independent risk factors for EH. The area under the ROC curve (AUC) for LASr was 0.925, (95% confidence interval [CI] = 0.879-0.971) with a sensitivity of 80.00%, specificity of 94.44%, using a cut-off value of 20%. For LAScd, the AUC-ROC was 0.878 (95% CI = 0.818-0.939 with a sensitivity of 76.25%, specificity 86.11%, and using a critical value of -11%.

Conclusion LASr and LAScd exhibited superior predictive capabilities for EH, with LASr performing the best. This study fills a critical gap in left atrial research and holds significant clinical implications.

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.

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.

Pure embryonal carcinoma (EC) is a rare malignant tumor that primarily occurs in infants with a sex development disorder and adults aged in their thirties to forties. This tumor behaves more aggressively than other germ cell tumors in the testis, and most of them should undergo surgery or chemotherapy. However, only a few case reports have involved imaging, and most of them are limited. Here, we present a case of pure EC in the testis of a 27-year-old male with multimodal ultrasonography, computed tomography, and magnetic resonance imaging.

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.

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.

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.

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.

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.

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.

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.

Objective: To determine whether COVID-19 causes an increase in spleen dimensions in individuals with a recent history of COVID-19 infection.

Methods: This case-control study was conducted at the Radiology Department of the University of Lahore Teaching Hospital and Sehat Medical Complex, both in Lahore. The study sample comprised 384 individuals, selected using a convenience sampling technique. Participants included individuals of all age groups and both genders; however, those under 18 were excluded due to the potential for incomplete spleen maturation. Other exclusion criteria included a history of splenectomy, the presence of traumatic or non-traumatic splenic lesions, or any other splenic abnormalities. Data collection commenced after obtaining approval from the Research Ethics Committee at the University of Lahore. The Siemens Sonovista c3000 Grey Scale Ultrasound Machine was used, and the data were analyzed using SPSS version 24.

Results: In a study involving 384 participants, the mean age was 35.7 ± 6.14, ranging from 22 to 50 years. Of these, 296 (71.1%) were female, and 88 (22.9%) were male. Echogenicity varied, with 29 participants (7.6%) exhibiting heterogeneous echogenicity and 355 (92.4%) showing homogeneous echogenicity. Spleen margins were irregular in 67 participants (17.4%) and smooth in 317 participants (82.6%). Regarding the history of Covid-19, 188 participants (49%) tested negative, while 196 participants (51%) tested positive.

Conclusion: Patients with a history of COVID-19 exhibited a significant increase in spleen length, volume, and thickness.

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.

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.

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.

Objectives: The incidence of chronic liver diseases in children is increasing worldwide due to congenital, metabolic, autoimmune and viral diseases. Currently, liver biopsy for fibrosis assessment is considered the gold standard. However, this procedure is invasive, may result in unavoidable complications and is prone to sampling errors. These limitations have led to an increasing demand for noninvasive methods for fibrosis screening. Artificial intelligence integration in ultrasound diagnosis of liver fibrosis has gained interest in clinical research. In the current study we used a cloud-based artificial intelligence platform utilizing transfer learning to evaluate the accuracy of B-mode ultrasound based AI model compared to pediatric radiologists in detection of liver fibrosis in a pediatric population.
Methods: For this IRB approved study, charts of 190 pediatric patients who were referred for liver biopsy and ultrasound were reviewed. On average 14 images of different liver areas were selected and a single image per decision was used for both radiologist and AI reads. A supervised machine learning model for image classification was developed using Google Vision AutoML (Google Inc., Mountain View, CA, USA). Data was divided for model development (80% of cases (154 cases) = 2324 images) and a model validation cohort for external testing (20% (36 cases) = 360 images). As a comparator, three blinded radiologists read the ultrasound images of the validation cohort and provided a binary diagnosis of fibrosis versus non fibrotic liver appearance. Tissue sampling was used as the reference standard for all cases.
Results: There were 99 and 91 patients in the biopsy proven fibrosis and non-fibrosis group, respectively. The model’s internal evaluation resulted in precision of 78.2%, recall of 78.5% and average precision of 87.7%. In the external validation cohort, three radiologists (Mean ± Standard Deviation) and Google AutoML (confidence interval (CI)) achieved a sensitivity of 42.04% ± 0.04 and 70.56% (63.32% to 77.10% CI), specificity of 50.18% ± 0.04 and 45.00% (37.59% to 52.58% CI), positive predictive value of 45.76% ± 0.01 and 56.19% (52.17% to 60.14% CI), negative predictive value of 46.39% ± 0.01 and 60.45% (53.65% to 66.86% CI) and accuracy of 46.11% ± 0.01 and 57.78% (52.49% to 62.94% CI). When evaluating agreement across multiple images from the same patient, intra-reader agreement was 77.2% for AutoML and 90.8%-92.5% for the 3 radiologists. The models' F1 scores for the development and validation cohort were 0.78 and 0.62, respectively.
Conclusions: Liver fibrosis assessment in children is challenging without biopsy. An ultrasound-based AI model showed high sensitivity compared to radiologists, albeit still without suitable diagnostic performance for clinical use.

Objective: To explore the diagnostic value of real-time shear wave elastography (SWE) in hepatic hemangioma (ANGI) and hepatocellular carcinoma (HCC).

Methods: A total of 88 patients diagnosed with angiosarcoma (ANGI) or hepatocellular carcinoma (HCC) in our department from February 2019 to February 2020 were included in this study. These patients were assigned to two groups based on pathological diagnosis: the ANGI group (n = 42) and the HCC group (n = 46). All patients underwent SWE examination, and the clinical efficacy was analyzed by comparing the average value of Young's modulus of the two groups and plotting the receiver operator characteristic (ROC) curve.

Results: There was no significant difference in lesion diameter between ANGI and HCC patients (P > 0.05). The ANGI group exhibited a significantly higher average Young's modulus for both the lesions and the adjacent liver tissues compared to the HCC group (P < 0.001); The area under the curve (AUC) of the average value of Young’s modulus of the lesion to ANGI and HCC was 0.875 (95% CI = 0.795-0.955).

Conclusion: SWE demonstrates high diagnostic accuracy in distinguishing ANGI from HCC, providing valuable clinical evidence for differentiating between the two diseases.

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.

Objective: To evaluate accuracy and feasibility of a handheld ultrasound machine for measuring carotid artery intima-media thicknesses (CIMT) and hemodynamic parameters by different expertise levels of ultrasound operators.

Methods: The operators were divided into three groups based on the level of their medical expertise: ultrasound technician, sonographer, and nursing staff. Operators from each group measured the CIMT and hemodynamic parameters of 25 volunteers using both handheld ultrasound and a conventional ultrasound machine. The reliability and reproducibility of handheld ultrasound measurements of CIMT and hemodynamic parameters (peak systolic velocity (PSV), end-diastolic velocity (EDV)) in operators were analyzed.

Results: After a period of training, there was no statistically significant difference between the mean CIMT measured using handheld ultrasound among the three operators (0.45 ± 0.09 mm, 0.50 ± 0.07 mm, 0.46 ± 0.08 mm, P> 0.05, respectively), as well as PSV (83.30 ± 15.42 cm/s, 76.28 ± 13.26 cm/s, 81.12 ± 21.21 cm/s, P> 0.05, respectively) and EDV (21.04 ± 4.12 cm/s, 21.87 ± 5.05 cm/s, 20.17 ± 5.90 cm/s, respectively, P> 0.05). Furthermore, there was a good repeatability and consistent of handheld ultrasound device in measuring mean CIMT in the ultrasound technician and sonographer groups (r = 0.662, 0.691, respectively, P < 0.01).

Conclusions: Under the premise of proper training, handheld ultrasound systems are feasible for rapid and primary assessment of carotid artery by operators with different levels of expertise.

Objective: This study aims to assess the performance of the Chat Generative Pre-Trained Transformer (ChatGPT), specifically versions GPT-3.5 and GPT-4, on ultrasonography board-style questions, and subsequently compare it with the performance of third-year radiology residents on the identical set of questions.
Methods: The study, conducted from May 19 to May 30, 2023, utilized a selection of 134 multiple-choice questions sourced from a commercial question bank for American Registry for Diagnostic Medical Sonography (ARDMS) examinations and imported into the ChatGPT model (encompassing GPT-3.5 and GPT-4 versions). ChatGPT’s responses were evaluated overall, by topic, and by GPT version. An identical question set was assigned to three third-year radiology residents, enabling a direct comparison of performances with ChatGPT.
Results: GPT-4 correctly responded to 82.1% of questions (110 of 134), significantly surpassing the performance of GPT-3.5 (P = 0.003), which correctly answered 66.4% of questions (89 of 134). Although GPT-3.5’s performance was statistically indistinguishable from the average performance of the radiology residents (66.7%, 89.3 of 134) (P = 0.969), there was a notable difference in the accuracy in question-answering accuracy between GPT-4 and the residents (P = 0.004).
Conclusions: ChatGPT demonstrated significant competency in responding to ultrasonography board-style questions, with the GPT-4 version markedly surpassing both its predecessor GPT-3.5 and the radiology residents.

Objective: Drug-induced liver injury (DILI) is one of the most challenging forms of liver disorder. We aimed to use ultrasound dual elastography, by combining strain and shear wave imaging, to noninvasively assess liver inflammation and injury severity of DILI.

Methods: 291 DILI patients were included in the prospective multicenter study and divided into training and validation cohorts. All patients received liver biopsy and dual elastography examination. Liver inflammation grading (G0-4) and fibrosis staging (F0-4) were considered as the gold standard of liver injury and G+F ≥ 5 was defined as severe liver injury. Indexes of dual elastography and serological indicators (DESI) were selected and analyzed with multivariable logistic regression to build DESI models for evaluating liver inflammation, and the C score model was built with the same method for diagnosing severe liver injury.

Results: Areas under the receiver operating characteristic curve (AUCs) of the DESI model to assess liver inflammation ≥ G2 were 0.887 and 0.868 in training and validation cohorts, respectively. AUCs of the DESI model in diagnosing ≥ G3 were 0.893 and 0.896 in the two cohorts, respectively. The C score accurately assessed severe liver injury with AUCs of 0.909 and 0.885 in two cohorts. Of the 87 patients with mild clinical severity, 10 (11.49%) had severe pathological injury, which could be identified by C score.

Conclusion: Dual elastography demonstrated high performance in diagnosing liver inflammation and identifying severe pathological liver injury of DILI, making up for the deficiency of serological indicators alone for evaluating DILI severity.

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.

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.

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.

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.

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.

A gastric GIST which causes gastroduodenal intussusception is rare. A Pubmed search only identified 21 published cases of gastroduodenal intussusception due to gastric GIST. Only 2 of them mentioned ultrasound without further analysis. Here, we report a case of gastroduodenal intussusception due to a gastric GIST with multiple imaging especially ultrasound. A 72-year-old Chinese woman was admitted to hospital because of epigastric pain, black stool lasting and occasional vomiting for 2 months. She underwent abdominal ultrasound, endoscopy, and contrast enhanced CT in turn. Abdominal ultrasound revealed a hypoechoic, medium-sized lesion beside pancreatic head. Endoscopy showed a submucosal lesion of gastric fundus overlapping into duodenum. The lesion manifested slight enhancement in the arterial phase on enhanced CT scans. The patient underwent laparoscopic exploration and partial gastrectomy. The histological examination revealed a low-risk gastric GIST of spindle-shaped cell type.

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.

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.

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 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.