Interventional ultrasound (IUS) is an important branch of modern minimally invasive medicine that has been widely applied in clinical practice due to its unique techniques and advantages. As a relatively emerging field, IUS has progressed towards standardization, precision, intelligence, and cutting-edge directions alone with more than 40 years of development, which is becoming increasingly important techniques in clinical medicine. This article will briefly review the development and advancement of IUS for diagnosis and treatment in China in the era of precision medicine from the aspects of artificial intelligence, virtual navigation, molecular imaging, and nanotechnology.

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.

Objectives To measure the CSA of the HST musculature measured with ultrasonography in soccer players undergoing ACLR and compare limb differences with healthy controls.

Methods A case-control study was performed with patients after anterior cruciate ligament repair (ACLR) and healthy controls in which cross-sectional areas (CSA) obtained using a model TE7 ultrasound machine (MINDRAY ®, USA) in B mode (4.2 to 13 MHz) with a multifrequency linear array transducer (L12-4S). Three CSA images were taken of the semitendinosus muscle (ST) and the long head of the biceps femoris (BFlh), at a distance of 30% and 70% of the ischial tuberosity insertion. Mean differences between groups were analyzed using SPSS v.20 (IBM®, USA), and statistical analyses were performed using non-parametric techniques to determine differences between groups (Student's t-test) and Cohen's correlation coefficient to quantify effect size.

Results 14 ACLR operated 17 ± 5.4 months ago and 12 healthy controls (W = 6; M = 20M; 24.5 ± 3.92 years; BMI = 25.1 ± 2.32 kg/m²) were recruited. There were differences between groups in CSA-ST70 (Post-ACLR = 1.43 ± 1.029 cm² vs Control 2.65 ± 0.664 cm², T Student = -3.68, 95% CI [-Inf, -0.648], P < 0. 001, ES = -1.418), but not in CSA-ST30 (Post-ACLR = 8.42 ± 1.596 cm² vs Control 9.16 ± 0.945 cm², T Student = -1.535; 95% CI [-Inf, -0.0793], P = 0. 068, ES = -0.5607), CSA-BFlh30 (Post-ACLR = 8.79 ± 1.47 cm² vs Control 8.87 ± 2.312 cm², T Student = -0.123; 95% CI [-Inf, 1.1049], P = 0.452, ES = -0. 049) or CSA-BFlh70 (Post-ACLR = 6.91 ± 1.011 cm² vs Control 7.01 ± 1.453 cm², T Student = -0.214; 95% CI [-Inf, 0.6795], P = 0.416, ES = -0.0783).

Conclusion Ultrasound measurement of the CSA can be an image marker to identify muscle weakness or atrophy that predicts functional loss early.

The American College of Radiology has implemented the Liver Imaging Reporting and Data System (LI-RADS) to help detect, interpret, and guide the management of suspected lesions on surveillance imaging for hepatocellular carcinoma (HCC) in patients with cirrhosis. The classification of indeterminate nodules with a grading algorithm can be used for multiple imaging modalities (US, CT, and MRI) and incorporates multiple imaging features to appropriately classify observations with different likelihood of being HCC. Contrast-enhanced ultrasound (CEUS) LI-RADS has been fully implemented since 2017. The aim of this pictorial article is to provide a comprehensive review of CEUS LI-RADS utilization, discuss its advantages, and highlight areas for potential improvement.

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.

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.

Real-time intelligent segmentation of ultrasound video object is a demanding task in the field of medical image processing and serves as an essential and critical step in image-guided clinical procedures. However, obtaining reliable and accurate medical image annotations often necessitates expert guidance, making the acquisition of large-scale annotated datasets challenging and costly. This presents obstacles for traditional supervised learning methods. Consequently, semi-supervised learning (SSL) has emerged as a promising solution, capable of utilizing unlabeled data to enhance model performance and has been widely adopted in medical image segmentation tasks. However, striking a balance between segmentation accuracy and inference speed remains a challenge for real-time segmentation. This paper provides a comprehensive review of research progress in real-time intelligent semi-supervised ultrasound video object segmentation (SUVOS) and offers insights into future developments in this area.

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.

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.

Objective A non-invasive assessment of the risk of benign and malignant follicular thyroid cancer is invaluable in the choice of treatment options. The extraction and fusion of multidimensional features from ultrasound images of follicular thyroid cancer is decisive in improving the accuracy of identifying benign and malignant thyroid cancer. This paper presents a non-invasive preoperative benign and malignant risk assessment system for follicular thyroid cancer, based on the proposed deep feature extraction and fusion of ultrasound images of follicular thyroid cancer.

Methods First, this study uses a convolution neural network (CNN) to obtain a global feature map of the image, and the fusion of global features cropped to local features to identify tumor images. Secondly, this tumour image is also extracted by googleNet and ResNet respectively to extract features and recognize the image. Finally, we employ an averaging algorithm to obtain the final recognition results.

Results The experimental results show that the method proposed in this study achieved 89.95% accuracy, 88.46% sensitivity, 91.30% specificity and an AUC value of 96.69% in the local dataset obtained from Peking University Shenzhen Hospital, all of which are far superior to other models.

Conclusion In this study, a non-invasive risk prediction system is proposed for ultrasound images of thyroid follicular tumours. We solve the problem of unbalanced sample distribution by means of an image enhancement algorithm. In order to obtain enough features to differentiate ultrasound images, a three-branched feature extraction network was designed in this study, and a balance of sensitivity and specificity is ensured by an averaging algorithm.

Objective To evaluate the feasibility and capability of UT in detecting musculoskeletal system lesions in the limbs and to explore its image quality.

Materials and Methods The Institutional Review Board has approved this prospective single-center study. This study included participants with various musculoskeletal and neurologic disorders in the limbs who provided written consent from October 2022 to April 2023. In addition to other radiological examinations (X-rays, CT, or MRI) and conventional handheld ultrasound scans requested by clinicians based on the conditions, each participant also underwent UT scanning using our developed limb ultrasound imaging system during the same period. Four radiologists and ultrasound physicians with more than five years of experience in musculoskeletal diagnostics analyzed the two-dimensional and three-dimensional images of the examination area.

Results Overall, 50 participants were evaluated (mean age, 36 years ± 18 [SD]; 26 males). The conditions included musculoskeletal tumors (n = 10), postoperative follow-up of musculoskeletal tumors (n = 20), peripheral nerve disorders in the limbs (n = 10) and postoperative pain in orthopedic surgery (n = 10). In all UT images, the region of interest was completely displayed, and internal structures such as muscles and nerves were clearly visible. Compared to conventional ultrasound images, the reconstructed three-dimensional images intuitively displayed the relationship between the lesions and surrounding tissues. Furthermore, UT did not exhibit metal artifacts when observing soft tissues around metallic implants, providing more comprehensive soft tissue information and more intuitive stereoscopic images.

Conclusion Clinical results of the UT system have demonstrated its feasibility as an automated and standardized imaging technique for musculoskeletal imaging, providing a new imaging modality for the diagnosis of musculoskeletal diseases in the human body.

This paper reports a case of closed reduction internal fixation of pelvic fracture and minimally invasive hemostasis procedure and hematoma catheterization and drainage for the active pelvic bleeding site and giant hematoma, guided by a robot-assisted teleultrasound. In this case, the robot-assisted teleultrasound-guided minimally invasive interventional hemostasis and catheterization utilized a partial master-slave approach. It was preliminarily confirmed that robot-assisted teleultrasound-guided minimally invasive hemostasis and puncture catheterization for hematoma were accurate and effective. The robot-assisted teleultrasound overcomes the reliance on physician experience in ultrasound-guided interventional diagnostics and treatment, and promoting the use of minimally invasive "visualized" technology across any distance.

Diabetic peripheral neuropathy (DPN) is one of the most common chronic complications of diabetes, which can lead to neuropathic pain, foot ulcers, and even disability, and greatly reduces survival. Therefore, early diagnosis and prevention of DPN is of great importance to reduce symptoms and disability rate. Ultrasound elastography is a noninvasive method to evaluate changes in nerve tissue composition by obtaining the elastic modulus of tissue and visually displaying the stiffness in the form of images. This paper summarizes the application progress of ultrasound elastography in the evaluation of peripheral neuropathy in recent years, in order to provide reference for the future clinical application of large samples.

In the digital age, the miniaturization of portable ultrasound equipment has brought both opportunities and challenges to the healthcare industry. Handheld ultrasound (HHU) devices are tablet or smartphone-sized scanners that are highly portable, have lower costs, produce no harmful side effects, and consume less power, making them suitable for use in different environments. HHU devices are primarily designed for new users of ultrasound scanners with varying backgrounds to evaluate different structures of the human body in various clinical settings. HHU applications based on Fifth-generation (5G) wireless network communication and artificial intelligence (AI) technology provide new healthcare solutions. The main application scenarios for HHU devices currently include in-hospital use, remote medical treatment, emergency rescue, and home monitoring. These scenarios allow for rapid image acquisition and real-time image interpretation, thereby improving the efficiency and quality of healthcare, reducing medical costs, and improving the allocation and utilization of medical resources. However, there remain some technical challenges and weaknesses such as device safety, data privacy, and network stability. With the continuous integration of AI technology, HHU applications will find wider use and promotion, bringing about more opportunities and challenges to the healthcare industry. This article reviews the application experience and insights of 5G technology in the field of HHU, aiming to provide fresh evidence and references for future research and applications.

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.

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.

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.

Early detection of vascular disease is fundamental to the prevention and treatment of systemic vascular lesions. The timely identification of vascular damage can be achieved by comprehensively assessing the structural anomaly and/or functional degeneration of the vasculature. The assessment may to some extent indicate the long-term detrimental effects of cardiovascular disease (CVD) risk factors on vascular health. A key aspect in the evaluation of vascular function is the measurement of arterial stiffness. In 2012, the arterial velocity-pulse index (AVI) and arterial pressure-volume index (API) were introduced, which are noninvasively measured with a brachial cuff, and can reflect the status of arterial stiffness in both the aorta and the brachial artery. A large number of relevant studies have demonstrated the strong associations between AVI/API and various CVD risk factors, underlining the substantial relevance of the indices in CVD risk assessment. In this review, we provide a systematic review of the progresses made in brachial cuff-based measurements of arterial stiffness. In addition, we summarize the results of the recent studies focused on exploring the associations of AVI/API with relevant risk factors as well as their roles in CVD assessment.

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.

Objective Inpainting is a technique for fixing or removing undesired areas of an image.

Methods In present scenario, image plays a vital role in every aspect such as business images, satellite images, and medical images and so on.

Results and Conclusion This paper presents a comprehensive review of past traditional image inpainting methods and the present state-of-the-art deep learning methods and also detailed the strengths and weaknesses of each to provide new insights in the field.

Objective VaveHealth is a company that developed an app-based POCUS (Point of Care Ultrasound) education platform. Our objective is to provide educators with insights into novel approaches to medical education by comparing the platform to PowerPoint-based education, the standard and current technique used to instruct medical students in the United States.

Methods We used a non-inferiority study to assess if the app-based platform was not less efficacious than the current standard of PowerPoint-based education. Thirty-three military medical students were provided with app-based or PowerPoint-based education for instructions on performing a focused assessment with sonography for trauma (FAST exam). Physicians evaluated each image and assigned a score from 1-5. The final scores were the average of all views. In addition, a two-sample t-test of the final scores and each view of the FAST was used to measure whether the VaveHealth platform was non-inferior to a PowerPoint-based model.

Results Overall, the VaveHealth group had lower average scores on each view and a lower average total score. There was no statistically significant difference in overall scores (VaveHealth = 7.65, PowerPoint = 9.04, P = 0.07). Subgroup analysis showed no statistically significant difference in student performance in the views of the splenorenal recess (VaveHealth score = 1.60, PowerPoint score = 1.65, P = 0.42), hepatorenal recess (VaveHealth score = 2.45, PowerPoint score = 3.00, P = 0.11), and suprapubic (VaveHealth score = 2.10, PowerPoint score = 2.46, P = 0.23) regions. In the subxiphoid region, students in the VaveHealth had a statistically significantly lower average score (VaveHealth score = 1.70, PowerPoint score = 2.08, P = 0.04).

Conclusion VaveHealth education is not a viable alternative to traditional PowerPoint education for POCUS training based on the lower raw scores and statistically significantly lower scores on one of the views of the FAST exam.

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.

Granulosa cell tumor of the testis is a rare intermediate stromal cell tumor that can be distinguished in adult and juvenile type. The juvenile type is commonly presented in infants less than a year old-most often during the first 6 months of life and can be associated with ambiguous genitalia and chromosomal anomalies. We report two cases of juvenile granulosa cell tumor (JGCT) of the testis diagnosed in the neonatal period and review the typical sonographic findings of this entity.

The integration of medical imaging and artificial intelligence (AI) has revolutionized interventional therapy of valvular heart diseases (VHD), owing to rapid development in multimodality imaging and healthcare big data. Medical imaging techniques, such as echocardiography, cardiovascular magnetic resonance (CMR) and computed tomography (CT), play an irreplaceable role in the whole process of pre-, intra- and post-procedural intervention of VHD. Different imaging techniques have unique advantages in different stages of interventional therapy. Therefore, single imaging technique can’t fully meet the requirements of complicated clinical scenarios. More importantly, a single intraoperative image provides only limited vision of the surgical field, which could be a potential source for unsatisfactory prognosis. Besides, the non-negligible inter- and intra-observer variability limits the precise quantification of heart valve structure and function in daily clinical practice. With the help of analysis clustered and regressed by big data and exponential growth in computing power, AI broken grounds in the interventional therapy of VHD, including preoperative planning, intraoperative navigation, and postoperative follow-up. This article reviews the state-of-the-art progress and directions in the application of AI for medical imaging in the interventional therapy of VHD.

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.

Objective This cross-sectional study was designed considering disagreements on the normal range of Median Nerve Cross-Sectional Area (MNCSA) and its association with age.

Methods In this cross-sectional descriptive study, the upper limbs of 98 healthy subjects (46 men and 52 women) were assessed bilaterally by sonography, and MNCSA was measured at the distal wrist crease.

Results Mean MNCSA values for subjects older and younger than 40 were 11.25 mm² and 10.21 mm², respectively. The results showed that the MNCSA significantly increased after 40 years of age.

Conclusion According to the present study's findings, advances in age can increase the MNCSA and affect the diagnostic accuracy of MNCSA measurement in CTS diagnosis.

Objective To investigate the effectiveness of SMI in evaluating hepatic IRI and detecting its therapy response.

Methods Thirty rats were randomly divided into sham (n = 12), IRI (n = 12), and andrographolide pretreatment (n = 6) groups. SMI, pathological, and biochemical examinations were conducted for the sham and IRI groups at 4 (n = 6) and 24 h (n = 6) after reperfusion, respectively. Two ultrasonologists measured the vascular index (VI). The interobserver agreement was evaluated using the intraclass correlation coefficient (ICC). The rat liver parameters, including Suzuki's score, alanine aminotransferase (ALT), and aspartate aminotransferase (AST), were obtained at different time steps in each group. For the andrographolide pretreatment group, data were obtained at 24 h after reperfusion to further verify the advantage of VI. Parameters were analyzed for correlations and compared between each group at 4 and 24 h.

Results The ICC between two ultrasonologists who measured the VI was 0.912 (95%CI: 0.889-0.940). Suzuki's score and VI were negatively correlated (r = -0.504, P = 0.012). Compared with the sham group, the IRI group showed a significant decrease in the VI at 4 and 24 h after reperfusion [(24.78 ± 3.23) versus (20.22 ± 2.55); (22.67 ± 1.36) versus (19.27 ± 2.23), P < 0.05)]. The VI in the andrographolide pretreatment group was higher than that in the IRI group [(21.90 ± 1.47) versus (19.27 ± 2.23), P <0.05].

Conclusions The VI on SMI can be used as a noninvasive and sensitive index to evaluate hepatic IRI and detect its therapeutic response.

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 ovaries are normally situated on the lateral aspect of the uterus in a shallow depression called an ovarian fossa. Maldescended ovaries occur when the ovary has not been localized in an intrapelvic location. Maldescended ovaries can be found with a normal uterus and more often with Mullerian duct abnormalities. There is no established association between ovarian tumors and maldescended ovaries. The present case report is a very rare case of mature cystic teratoma of a maldescended right ovary located within the subhepatic region. To bring this into focus in cases with an abnormal dermoid cyst site in a female. The patient should be carefully evaluated for ovarian sites and the possibility of maldescended ovaries should be taken into account. The maldescended ovaries with complicated cysts or with tumor should be included in the differential diagnosis of causes of abdominal pain in females.

This paper begins with the medical digital ultrasonic imaging workstations' development history and stages of PACS (Picture Archiving and Communication Systems). We analyze the actual application scenarios and pain points in medical digital ultrasonic imaging and introduce the support of medical digital ultrasonic imaging workstations for the entire business process. At the same time, we explain the role of AI functions in promoting business improvements throughout the process, using Shenzhen Maternal and Child Health Hospital as an application case study. This paper also discusses the difficulties faced by the development of AI in medical digital ultrasonic imaging and provides some solutions and suggestions. We offer a perspective on the future development of artificial intelligence in medical digital ultrasonic imaging. We explore potential application scenarios in areas such as empowering the ultrasound process with intelligent management, ultrasound consultation, cloud-based electronic films, and the Internet of Things (IoT) services.

Objective MicroRNAs (miRNAs) play important roles in various diseases’ development and progression. The aim of this study is to identify the differently expressed miRNAs (DEmiRNAs) and differently expressed genes (DEGs) between BPH and PCa.

Methods Selecting BPH and PCa tissues from GEO database (GSE118038 as test dataset; GSE30994 as validation dataset), we identified DEmiRNAs and DEGs between BPH and PCa using GEO2R online tool and “Deseq2” R package. We applied random forest method to select hub DEmiRNAs, combining age and BMI, to establish a nomogram model for BPH detection. Finally, GO and KEGG enrichment analyses were conducted to explore the underlying mechanisms and pathways of DEmiRNAs in BPH.

Results We found 26 DEmiRNAs between BPH and PCa, of which 21 DEmiRNAs were up-regulated and 5 DEmiRNAs were down-regulated. Via forest random method, we selected miR-636, miR-324-3p, miR-210-3p and miR-3615 as hub DEmiRNAs in BPH. Combing these four hub DEmiRNAs, age and BMI, we established a nomogram model to distinguish BPH from PCa. Through “miRWalk” online tool, we targeted 499 hub DEGs between BPH and PCa, and found most of genes enriched in muscle system process, muscle contraction, contractile fiber, myofibril, actin binding, passive transmembrane transporter activity, focal adhesion, axon guidance.

Conclusion Our results suggested that miR-636, miR-324-3p, miR-210-3p and miR-3615 might the hub DEmiRNAs between BPH and PCa, which may play a crucial role to distinguish BPH from PCa.

Sarcoidosis is a granulomatous disease of unknown etiology that can involve various organs and tissues. The clinical manifestations vary greatly, so it is difficult to make a clear diagnosis of sarcoidosis with just the clinical manifestation and imaging findings. The diagnosis and treatment of a patient with systemic sarcoidosis was reported: a 51-year-old woman presented with a dry cough. Computed tomography (CT), magnetic resonance imaging (MRI), and conventional ultrasonography (US) suggested miliary nodules and inflammatory changes in the lungs, there was mediastinal, retroperitoneal and hilar lymph node enlargement and uneven liver echo, respectively. Positron emission tomography/computed tomography (PET-CT) further suggested that the lesions were distributed throughout the body, including the lymph nodes and muscles of the extremities; thus, systemic lymphoma was considered. Finally, ultrasound-guided biopsy of different sites yielded the same histopathological findings: sarcoidosis. The sarcoidosis in this case is characterized by a large number of involved sites with a wide range, and a variety of imaging data were complete but failed to suggest a diagnosis. Finally, a clear histopathological result was obtained under the guidance of ultrasound. This article reviewed the relevant literature and concluded that ultrasound-guided puncture to obtain histopathological results is a simple and effective method for the diagnosis of sarcoidosis.

Objective: Computed tomography (CT) imaging of the chest is an effective diagnostic tool assisting physicians in making a diagnosis. This study aimed to propose a new convolutional neural network for classifying the lung nodules of the patient through chest CT scan data to determine whether the patient has related disease genes.
Methods: We proposed a DenseNet-based neural network structure that uses multi-scale convolutional kernels to obtain features of different receptive fields, which are fed into a DenseNet containing four improved DenseBlocks, followed by a classification module to obtain the model output, i.e., whether a lung nodule contains a cancer gene. We conducted classification experiments on a CT scan dataset containing 465 training samples and 117 test samples.
Results: The results showed that DenseNet was better than ResNet in terms of classification, whereas ResNet was better than VGG, which was consistent with the findings of previous studies. However, because these models were more complex, they suffered from overfitting problems. Among all of the models used in this paper, our proposed network achieved the best results in terms of accuracy, F1 score, and sensitivity without an over fitting. The accuracy was 72.0%, sensitivity was 78%, and F1 score was 68%.
Conclusion: The proposed DenseNet neural network can improve and assist medical imaging diagnostic physicians in the initial diagnosis of lung nodules.

With the development of network technology and intelligent robot technology, Robot-assisted teleultrasound has played an important role in clinical fields. The application of real-time remote ultrasound technology has made the ultrasonic diagnosis break through the limitation of time and space distance, and solved the problem of shortage of medical resources to a certain extent. This article introduces the development and application basis of robot-assisted teleultrasound, summarizes the clinical application status, and discusses the advantages and limitations of its current application. In addition, we discuss the value in application scenario, interventional therapy and intracavitary ultrasound in the future.

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.

Primary squamous cell carcinoma of the thyroid (PSCCT) is a rare clinical disease characterized by rapid growth, high invasiveness, and a poor prognosis. A 66-year-old male patient was admitted due to throat pain and dysphagia. Ultrasound revealed a calcified hypoechoic mass in the right lobe of the thyroid gland, measuring approximately 35.3 ml. Ultrasound and PET-CT both indicated high suspicion of malignancy. The patient underwent contrast-enhanced US-guided biopsy, and the pathological results revealed poorly differentiated squamous cell carcinoma. CEUS was performed regularly during the Chemotherapy combined with pembrolizumab (PD-1) treatment courses. The vital area was significantly reduced with neither recurrence nor cervical lymph node metastasis. Surgical resection and chemotherapy combined with immunotherapy had a significant treatment effect in this case. CEUS is helpful for diagnosis confirmation, biopsy guidance and efficacy evaluation and has important clinical application value.

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.