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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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: An observational cross-sectional study to assess sonographic splenomegaly quantitatively in thalassemia patients grouped with respect to transfusion given whole blood vs packed red cells.
Methods: A study was conducted among 330 patients equally divided into two groups, undergoing an abdominal ultrasound examination with a transducer frequency ranging from 3-5 MHz during the period December 2021 to August 2022. An independent t-test was applied to compare the splenic volume in thalassemia patients given whole blood transfusions versus packed red cells transfusions, and Cohen's d was used to indicate the standardized difference between two ultrasound splenic volume means.
Results: The mean splenic volume of the patients who received whole blood cells was 320.62 ± 219.05 cm³, which is greater than the patients who received packed red cells, whose mean was 60.72 ± 58.72 cm³, The splenomegaly was quantitatively assessed in six age groups ranging from 1 to 3 years, 4 to 6 years, 7 to 9 years, 10 to 12 years, 13 to 15 years, and 16-18 years and mean splenic volume in each age group was compared to those receiving whole blood or packed red cells transfusion. there is a statistically significant difference between both transfusion receiving groups, having a larger Cohen’s d size effect of 1.62.
Conclusion: Ultrasound is a reliable imaging modality for assessing splenic volume and linear parameters of the spleen with greater splenomegaly in thalassemia patients with whole blood transfusions than those with packed red cells when quantitatively assessed according to relevant age groups. Thalassemia patients should be transfused packed red cells to delay splenomegaly, that should be assessed sonographically.

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.

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.

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.

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.

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.

Pelvic floor dysfunction (PFD) is a series of diseases with anatomical and/or functional abnormalities of the pelvic organs, which is common in women and can considerably interfere with their quality of life. Imaging is increasingly being used and can contribute towards better understanding, management, and prediction of long-term outcomes in women who suffer from PFD. Of the available techniques such as X-ray, computed tomography, magnetic resonance imaging, and ultrasound, the latter is generally superior for female pelvic floor imaging, especially in the form of transperineal imaging. This technique is safe, cost-effective, simple, widely available, and can provide an overview of the female pelvic floor. This review will outline the basic methodology, introduce recent researches in the field, and provide an overview of likely future utility of this technique in the evaluation of PFD.

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.

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.

Objective: In the present study, we aimed to evaluate the diagnostic performance of superb microvascular imaging (SMI) for breast masses through a meta-analysis.

Methods: PubMed, EMBASE, Cochrane Library, and Web of Science Core Collection databases, as well as the most comprehensive Chinese academic databases in medicine, such as China Biology Medicine Disc, China National Knowledge Infrastructure, and Wanfang Database, were systematically searched. All included studies used histopathological results as the reference standard. To assess the diagnostic performance, we calculated the pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), diagnostic odds ratio (DOR), and the area under the curve (AUC). In addition, a subgroup meta-regression analysis was performed to investigate the heterogeneity.

Results: Fourteen of 1,075 identified articles were included in the current review. The pooled sensitivity, specificity, LR+, LR-, and DOR for SMI were 0.85 (95% CI: 0.79-0.89), 0.80 (95% CI: 0.71-0.87), 4.3 (95% CI: 2.9-6.2), 0.19 (95% CI 0.14-0.26), and 22 (95% CI: 14-36), respectively. The AUC was 0.90 (0.87 - 0.92). Meta-regression showed that there were heterogeneities for sensitivity, but not for specificity.

Conclusions: SMI had good sensitivity, specificity, and excellent diagnostic value in distinguishing benign breast masses from malignant masses and could potentially help select suspicious breast masses for surgery.

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.

Objective: In this paper, we retrospectively analyzed the ultrasonographic features of paratesticular leiomyoma to help doctors correctly diagnose the disease before operation and guide surgical treatment.

Methods: From 2013 to 2020, 16 cases of paratesticular leiomyomas confirmed by pathology in our hospital were retrospectively analyzed. The retrospective analysis included the ultrasound and color Doppler flow imaging (CDFI) of paratesticular leiomyoma which were evaluated by two experienced radiologists based on the features of ultrasound images of lesions.

Results: All the 16 paratesticular leiomyoma presented as hypoechoic masses with a clear boundary and round shape. The echotexture of 6 large leiomyomas showed a typical vortex appearance. In the remaining 10 small leiomyomas, thin cord-like hyper-echogenic appearance was found. Color Doppler flow imaging showed that the blood flow signal within a large leiomyoma mass (n = 1) was significantly higher than that of surrounding testis while the blood flow signals of the smaller leiomyomas (n = 5) had significantly less flow signals compared with the testis. The remaining leiomyoma masses (n = 10) had similar flow signals to the testicular blood flow.

Conclusion: Sonography can be used to detect testicular tumors and to differentiate extratesticular from intratesticular masses. The ultrasound features of paratesticular leiomyoma included hypo- and hyperechoic mass, a round or typical vortex shape as well as various vascularity based on the size of the tumors.

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 investigate the accuracy of contrast-enhanced ultrasound (CEUS) in the diagnosis and localization of active needle-tract bleeding (ANTB) during thermal ablation for liver cancer.

Methods: Between June 2013 and December 2018, liver cancer patients with poor coagulation function or suspected needle-tract bleeding during ablation were enrolled in the study. Conventional US and CEUS were applied to detect ANTB and the bleeding sites, and sequentially abdominal paracentesis drainage (APD) was used as the gold standard for the diagnosis of ANTB. Thermal ablation guided by conventional US or CEUS was used to stop the bleeding.

Results: Among the 77 enrolled patients, 21 patients (27.3%, 21/77) had ANTB. In total, four (19.0%) and 21 (100%) cases of ANTB were diagnosed by colour Doppler US and CEUS, respectively. The sensitivity, specificity and accuracy of colour Doppler US for diagnosing ANTB were 19.0%, 100% and 77.6%, respectively. The sensitivity, specificity, and accuracy of CEUS for diagnosing ANTB were 100%, 100% and 100%, respectively. The sensitivity of CEUS to diagnose ANTB was significantly higher than that of colour Doppler US (P < 0.001). All cases of ANTB were arterial bleeding and were stopped by thermal ablation under the guidance of colour Doppler US or CEUS.

Conclusion: Compared to colour Doppler US, CEUS could detect ANTB with significantly higher sensitivity and accurately guide needle-tract ablation during thermal ablation of liver cancer.

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.

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.

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.

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.

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.

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.

ObjectiveIntrathyroidal thymic carcinoma (ITTC) is a rare epithelial tumor of the thyroid gland. Since ITTC is rare, its imaging findings have not been well defined. In the present study, we studied the US appearance of ITTC by analyzing ten cases retrospectively.

Methods Patients were identified by searching the surgical pathology records at our hospital. There were three male and seven female patients ranging in age from 40 to 79 years. The ultrasound (US) features were evaluated, and the relevant clinical data were combined with the fine needle aspiration (FNA) results from previous publications.

Results The average length of the nodules was 48 mm. Of all ten nodules, half were located in the right lobe of the thyroid, and half were located in the left lobe, either immediately adjacent to the lower lobe or within the lower part of the thyroid lobe. All nodules manifested with a completely solid composition and heterogeneous hypoechoic echogenicity. Three nodules had striped hyperechogenicity within the nodule. All nodules had irregular margins. Seven had a horizontal shape, and three had a vertical shape. Four nodules manifested with mainly peripheral vascularity, two nodules showed mainly central vascularity, and three had mixed vascularity. The vascularity of one remaining nodule was unknown. Cervical lymph node metastasis was detected in two cases by US examination. According to previous literature, FNA cytology revealed atypical type of thyroid cancer, poorly differentiated carcinoma, or a high-grade malignant thyroid neoplasm without further definitive classification.

ConclusionA large cancer-like nodule located within the lower thyroid in middle-aged people, combined with an atypical type of thyroid cancer after FNA, indicated that ITTC should be considered.

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.

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.

The use of contrast-enhanced ultrasound (CEUS) has expanded over the past decade to include a variety of diagnostic and therapeutic applications. These include urgent clinical situations that require timely diagnosis and subsequent treatment. With the introduction of microbubble ultrasound contrast agents (UCAs), CEUS provides increased sensitivity and specificity over conventional ultrasound. Within the trauma setting, CEUS benefits include point of care imaging and an ability to monitor perfusion in real-time. Additionally, UCAs are non-nephrotoxic, and can be used when contrast enhanced CT is contraindicated. In this review, we discuss recent advancements of CEUS within trauma settings.

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.

Background and Purpose: Surgery of ultrasound-detected non-palpable breast lesions should be guided by ultrasound. Commonly radiologists localize the lesion under ultrasound preoperatively, which necessitates the availability of a localization device and may involve a substantial cost. We performed a study to prospectively assess the feasibility of ultrasound-guided localization without any special device.
Methods: Women with non-palpable benign breast masses were assigned to the “guide wire insertion” (GWI) or the “local coordinates and skin inking” (LOCSI) groups. In both groups, the tumor was marked as a shadow on the skin by the radiologist under ultrasound. In the GWI group, a guidewire was inserted, and in the LOCSI group, the local coordinates of the lesion relative to the skin and the nipple as well as its clockwise placement were reported.
Results: Overall, 29 cases were included in the study, 11 in the GWI and 18 in the LOCSI groups. In all cases, the specimen was correctly excised. The weights of the resected specimens were significantly higher with GWI; LOCSI prevented excessive tissue extraction. Clinicians reported LOCSI as “very easy” more frequently, and surgery took less time.
Conclusions: Overall, our study showed that LOCSI was feasible and can be a suitable method in areas with limited resources. We propose similar studies with a larger sample size, inclusion of malignant cases for margin assessment, and estimation of the cost-effectiveness of the technique.

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.