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

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

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

Uterine artery pseudoaneurysm is a rare complication associated with obstetric or gynecological procedures. We described a case report of a primigravid, underwent cesarean section who evolved with massive, intermittent vaginal bleeding requiring multiple blood transfusions. The diagnosis of uterine artery pseudoaneurysm was suspected on transvaginal Doppler ultrasound and confirmed on computed tomography angiography. Due to the patient's stability, age and undefined offspring, superselective uterine artery embolization was chosen for treatment and the procedure successfully performed.

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

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

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

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

Objective: To investigate the best combined method of S-Detect, a computer-aided diagnosis (CAD) system, with breast ultrasound (US) according to radiologists’ experience.

Methods: From March 2019 to June 2019, 259 breast masses in 255 women were included in this study. Ultrasonographic images of the target masses were prospectively analyzed by radiologists and CAD. Three combined methods, including method 1 [selective downgrading combination for Breast Imaging Reporting and Data System (BI-RADS) 4a lesions], method 2 (selective upgrading combination for BI-RADS 3 lesions) and method 3 (selective upgrading or downgrading combination for BI-RADS 3 or 4a lesions), were applied to interpret the CAD results. The sensitivity, specificity, the area under the receiver operating characteristic curve (AUC) of experienced or inexperienced radiologists before and after adding CAD results were compared using the histopathological results as a reference standard.

Results: In identifying breast malignancy, the AUC for CAD was similar to that of experienced radiologists (P= 0.410), but higher than that of inexperienced radiologists (P= 0.003). When combining CAD with experienced radiologists based on method 1 and combining CAD results with inexperienced radiologists based on method 3, the AUCs were significantly improved (P= 0.024 and 0.003, respectively) compared to US alone, with significantly increased specificity (P< 0.001 for both) and no significantly decreased sensitivity (P> 0.05 for both).

Conclusion: The combination of CAD system and conventional ultrasound can improve ultrasound diagnostic performance in determining breast malignancy. The method 1 and method 3 combinations are respectively recommended for experienced and inexperienced radiologists when CAD is combined with conventional breast ultrasound.

Objective: To evaluate and compare the diagnostic performance of B-mode ultrasound (B-mode US), strain elastography (SE), color Doppler ultrasound (color Doppler US) and the combination of these modalities in differentiation between fibroadenomas and phyllodes tumors.

Methods: This retrospective study included 220 breast lesions, between January 2016 and January 2018. B-mode US, strain elastography, and color Doppler US were realized to evaluate each lesion. The results of three modalities were compared between the B-mode US and the combination of B-mode US, elastography, and color Doppler US: sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). The pathological findings were used as the reference standard.

Results: Among 220 breast masses, 189 of the lesions were fibroadenomas, 31 were phyllodes tumors. Addition of strain elastography and color Doppler US to B-mode US increased the specificity from 27.5% (95% confidence interval [CI]: 21.15%, 33.88%) to 95.24% (95% CI: 92.20%, 98.27%; P < 0.001); PPV from 16.46% (95% CI: 10.79%,22.14%) to 60.87% (95% CI: 40.92%, 80.82%; P < 0.001) and AUC from 0.584 (95% CI: 0.481, 0.687) to 0.886 (95% CI: 0.824, 0.948; P < 0.001).

Conclusion: The combination B-mode US, strain elastography and color Doppler US increase the diagnostic performance in distinguishing fibroadenomas from phyllodes tumors.

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

Appendiceal mucocele is a rare disease. Sometimes it is discovered accidentally and sometimes it resembles acute appendicitis. The incidence is 0.2% to 0.7% of all appendectomies specimens. A case report of A 35-year female presented to the emergency room with a history of progressively increasing abdominal pain on and off for 3 months. Tenderness in the right lower quadrant of the abdomen was noted with a palpable mass in the right iliac fossa. An abdominal ultrasound examination was performed, and the patient was found to have an Appendicular mucocele-onion peel sign (sonographic layering within a cystic mass). The patient underwent surgery, and the diagnosis was confirmed by the histopathological report. If mucocele is treated incorrectly, pseudomyxoma peritonei which is characterized by a malignant process may develop. The term mucocele is widely used in diagnosing both benign and malignant lesions.

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

Thyroid metastatic tumors are a series of rare clinical disease with an incidence of approximately 1.4-3%, of which metastatic tumor from the digestive system is even rarer. Thyroid metastatic tumor from the digestive system is characterized by a rapid growth tumor with high invasiveness and poor prognosis. Ultrasound (US) and contrast-enhanced ultrasound (CEUS) could provide more information for making diagnosis, determining lesion range, and offering biopsy guidance. Ultrasound-guided core needle biopsy (CNB) should be used instead of fine needle aspiration (FNA) to confirm the pathological diagnosis. Here, we report two cases of thyroid metastatic tumor from the digestive system. By describing these cases, we aimed to assess the clinical value of conventional US and CEUS in the diagnosis and treatment of these tumors.

Undifferentiated embryonal sarcoma of the liver (UESL) is an aggressive mesenchymal tumor, which presents a low incidence in adults. The low morbidity and atypical clinical manifestations conduce to difficulty in preoperative diagnosis. Imaging manifestations of this tumor is important for its identification. Contrast-enhanced ultrasound (CEUS) was a promising tool to characterize hepatic lesions and proved to have high diagnostic accuracy. The present study reported the CEUS characteristics of UESL in two adult patients, which exhibited peripheral rim hyper-enhancement along with progressively centripetal enhancement and large areas of perfusion defection. Sonographers should be fully aware of this rare disease for better differential diagnosis.

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

Objective: Heavy metal pollution has become one of the environmental contamination problems in today's world. Adsorption materials can effectively remove heavy metal ions from the water. There are some shortcomings for traditional adsorbents, such as difficult separation after adsorption, long separation time, and may cause secondary pollution in the environment without recycling. The aim of this study was to seek new materials with effective ways to absorb heavy metal ions in the water.

Methods: A new kind of adsorption material consisted of polylactic acid (PLA) microbubble and graphene oxide (GO) (i.e., PLA@GO microbubbles) was fabricated which can combine by electrostatic adsorption with the assistance of PAH. The influence of the initial concentration of heavy metal of lead ion, pH value, and absorption time on the adsorption effect of PLA@GO microbubbles was tested in this study.

Results: Graphene oxide modified on PLA microbubble has huge specific surface area and various functional groups, which can adsorb heavy metal ions in water. The resulting PLA@GO microbubble showed a homogeneous spherical structure with a size of 500-1.5 μm, which was suitable for its effective separation from water. The optimal dosage of PLA@GO microbubbles was 10 mg, the pH value of the solution was 5.0, and the adsorption time was 20 minutes which correlates to 75 mg/L of leadions.

Conclusion: The characteristics of the PLA@GO microbubbles showed a strong adsorption capacity, high adsorption efficiency, and a shorter balance time which provided an environment-friendly new material to remove heavy metal ions from water.

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

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

Objectives To assess the clinical efficacy and safety of ultrasound-guided stellate ganglion block in the treatment of allergic rhinitis.

Methods Sixty patients with allergic rhinitis were selected as study subjects and were treated with stellate ganglion blocking. We observed the appearance and time of the patient's Horner syndrome, and compared the patient's clinical symptom scores before and after one month treatment, and calculated the clinical efficacy and overall effective rate.

Results After ultrasound-guided stellate ganglion blocking treatment, 97% of patients occurred Horner syndrome within 2 minutes. The scores of sneezing, runny nose, nasal itching, and nasal congestion significantly reduced at one month after treatment compared with those before treatment, in which there was statistically significant difference (P<0.05). The clinical efficacy rate also reached 96.7% after procedures. No serious complications occurred in this study.

Conclusions Ultrasound-guided stellate ganglion block can significantly improve the clinical symptoms and the life quality for patients with allergic rhinitis, and it is worthy of extensive clinical application.

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

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

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

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

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

Objective To predict sonographic features of malignancy in thyroid nodules by using convolutional neural networks (CNNs) ResNet50 model.

Methods A cohort of 461 patients having sonographic thyroid nodules with histology diagnosis were randomly split into training set (70%), validation set (15%) and testing set (15%). Labeled sonographic patterns of thyroid nodules were used to train CNNs ResNet50 in training set, where algorithm pipelines were used to explicitly delineate structures of interest using segmentation algorithms to measure predefined characteristics of these structures as to be predictive and to use these features to train models that predict the malignancy in thyroid nodules. The prognostic accuracy of ResNet50 model was evaluated on validation set and compared with the individual sonographic specialists in testing set. Accuracy, sensitivity, specificity, and efficiency of ResNet50 model was measured using receiver operating characteristic (ROC) curve.

Results Measurements showed the evaluation indexes of ResNet50 model were as follows: accuracy: 94.39%, sensitivity: 92.45%, specificity: 96.30%, efficiency: 96.08%, F1 value: 94.23%, and AUC: 93.40%. The prognostic accuracy and other indexes of ResNet50 model was not subordinate compared to sonographic specialists (P < 0.05).

Conclusion These results highlight the emerging role of deep learning techniques including CNNs in precision medicine and suggest an expanding utility for computational analysis of sonographic images in the future practice. This study showed a computational approach can be used for learning sonographic features of thyroid nodules using ResNet50 model to combine the power of adaptive machine learning and algorithms with traditional sonographic assessment.

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

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

The Annual National Congress of the Chinese Society of Ultrasound in Medicine (CSUM) is one of the most prestigious and influential academic conferences in China, with the largest number of participants. The 22th Annual Meeting of Chinese Society of Ultrasound in Medicine (CSUM 2022) will be held from November 3 to November 6 in Chengdu, Sichuan, China. The scientific program of the meeting received 4,625 proceeding papers which covers the latest developments and trends in basic and clinical ultrasound research, continuing education and promotion of ultrasound-related guidelines and consensus, clinical application of new technologies such as interventional ultrasound, elastography, real-time three-dimensional ultrasound, and contrast-enhanced ultrasound.

The conference will hold a Young English-Presentation Forum to provide a stage for early career doctors to display academic achievement and cultivate the international vision of young physicians. A total of 271 papers were submitted for this forum, including 20 on Ob/Gyn ultrasound, 75 on abdominal ultrasound, 55 on interventional ultrasound, 45 on superficial organs and vascular ultrasound, and 76 on echocardiography. Under the recommendation of the Academic Committee of the General Assembly, the journal of AUDT selected 25 abstracts from the oral presentation group to be published online and in print. Following publication in AUDT, these abstracts can be cited as scientific publications and shared with the international community.

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

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

Objective: To evaluate the real-time accuracy of cloud handheld ultrasound system using AI technology in screening carotid plaque.

Methods: 2627 ultrasound images of the carotid artery are collected using the cloud handheld system. Bounding boxes of carotid plaques are labeled by qualified sonographers, and the dataset is trained using a lightweight YOLOv3 model. An additional and separate 390 images are collected and tested using the evaluation metrics average recall (AR), average precision (AP), and frames per second (FPS) for quantifying classification performance and time consumption.

Results: We use a plaque grading definition with a thickness of 1.2-1.5 mm defined as small plaque, 1.5-3 mm as medium plaque, and more than 3 mm thick as large plaque. Our model achieves AP^IoU=0.50 with 96.5%, with AP^large is 79.9%, AP^medium is 90.7%, AP^small is 93.5%; AR^IoU=0.50 is 64.5%, where AR^large is 60.6%, AR^medium is 58.3%, AR^small is 70.8%, and FPS is 33.3.

Conclusion: We establish a framework for data set construction, model selection, training, and testing of carotid ultrasound images and verify the effectiveness of real-time AI technology in the automatic detection of carotid artery plaque.

Internal carotid artery dissection (ICAD) is the result of a tear at the intimal layer of the internal carotid artery, which is a common cause of stroke in young and middle age patients. We present multimodal vascular ultrasound findings (include color Doppler flow, power Doppler flow, advanced dynamic flow, superb microvascular and contrast enhanced ultrasound imaging) of a 30-year-old female with ICAD and thrombus in false lumen. To our best knowledge, this is the first report that several superior vascular ultrasound imaging techniques have been used to evaluate ICAD.

Retroperitoneal ectopic pregnancy (REP) is extremely rare and can be easily missed due to its special location. As most REP are close to the major abdominal blood vessels, misdiagnosis can lead to serious consequences, even threatening the patient's life. This report presents a 26-year-old naturally conceived patient who was diagnosed with REP through transvaginal ultrasound and underwent early surgery with good recovery.

Langerhans cell histiocytosis (LCH) is an uncommon disease of unknown etiology, which involves mainly in the bone and skin and rarely the thyroid. Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. However, thyroid LCH complicated with PTC is rarer. Awareness of sonographic feature is helpful for clinical managment, especially prior to the surgery. Here, we report two patients who have histologically confirmed thyroid LCH complicated with PTC and summarize these cases with sonographic features including enlargement of the thyroid, multiple hypoechoic nodules with microcalcifications as well as enlarged cervical lymph nodes. The relvent literatures will be reviewed and discussed.

The incidence of giant cystic meconium peritonitis (GCMP) in pregnancies following artificial reproductive techniques is rare. We report the first case of GCMP following in vitro fertilization and embryo transfer in China. A 32-year-old woman with a history of bilateral salpingectomy due to ectopic pregnancy attended our in vitro fertilization and embryo transfer program. Meconium peritonitis, polyhydramnios, bowel dilatation, and intra-abdominal calcification were detected by ultrasound at 33 weeks of gestation. Cesarean section due to scarred uterus was performed at 35⁺¹ weeks of gestation. A normal female infant with a distended abdomen was delivered. Emergency laparotomy was performed on the female infant because of the presence of a giant abdominal mass. A 0.2-cm perforation was found in the ileum. Enterostomy could not be performed due to the heavy adhesion. Prenatal diagnosis of meconium peritonitis is possible through careful ultrasonographic examination, and prognosis can be improved through early surgical intervention and intensive postoperative support.

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

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

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