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Deep Learning on Ultrasound Imaging for Breast Cancer Diagnosis and Treatment: Current Applications and Future Perspectives
Changyan Wang, BS, Haobo Chen, MS, Jieyi Liu, BS, Changchun Li, BS, Weiwei Jiao, BS, Qihui Guo, BS, Qi Zhang, PhD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (2): 91-113.   DOI: 10.37015/AUDT.2023.230012
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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.

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Current Status, Prospect and Bottleneck of Ultrasound AI Development: A Systemic Review
Siyi Xun, MA, Wei Ke, PhD, Mingfu Jiang, MA, Huachao Chen, BA, Haoming Chen, BA, Chantong Lam, PhD, Ligang Cui, MD, Tao Tan, PhD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (2): 61-72.   DOI: 10.37015/AUDT.2023.230020
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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.

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AI-based ChatGPT Impact on Medical Writing and Publication
Mofan Li, Yongyue Zhang, MM, Yang Sun, MM, Ligang Cui, PhD, Shumin Wang, PhD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (2): 188-192.   DOI: 10.37015/AUDT.2023.230013
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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.

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ChatGPT Related Technology and Its Applications in the Medical Field
Tairui Zhang, BS, Linxue Qian, MD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (2): 158-171.   DOI: 10.37015/AUDT.2023.230028
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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.

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Advanced Application of Artificial Intelligence for Pelvic Floor Ultrasound in Diagnosis and Treatment
Enze Qu, MD, Xinling Zhang, MD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (2): 114-121.   DOI: 10.37015/AUDT.2023.230021
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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.

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Ultrasonographic Identification of Muscle Atrophy in Hamstring Muscles after Anterior Cruciate Ligament Repair among Soccer Players: A Case-control Study
Sebastián Eustaquio Martín Pérez, MSc, Raúl Hernández García, PT, Alberto Brito Lorenzo, PT, Carlos Daniel Sabater Cruz, PT, Mario Herrera Pérez, PhD, Fidel Rodríguez Hernández, PhD, Kristin Briem, PhD, Isidro Miguel Martín Pérez, MD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (4): 381-389.   DOI: 10.37015/AUDT.2023.230036
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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.

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Ultrasound Image Generation and Modality Conversion Based on Deep Learning
Shujun Xia, MD, Jianqiao Zhou, MD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (2): 136-139.   DOI: 10.37015/AUDT.2023.230011
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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.

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Artificial Intelligence in Prenatal Ultrasound: Clinical Application and Prospect
Wenjia Guo, MM, Shengli Li, MM, Xing Yu, MD, Huaxuan Wen, BM, Ying Yuan, MM, Xia Yang, MM
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (2): 82-90.   DOI: 10.37015/AUDT.2023.230024
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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.

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Advances in Intelligent Segmentation and 3D/4D Reconstruction of Carotid Ultrasound Imaging
Cancan Cui, MD, Zhaojun Li, PhD, Yanping Lin, PhD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (2): 140-151.   DOI: 10.37015/AUDT.2023.230015
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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.

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Rapid Screening of Carotid Plaque in Cloud Handheld Ultrasound System Based on 5G and AI Technology
Wenjun Zhang, MD, Mi Zhou, PhD, Qingguo Meng, MD, Lin Zhang, MS, Xin Liu, MS, Paul Liu, PhD, Dong Liu, PhD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (2): 152-157.   DOI: 10.37015/AUDT.2023.230018
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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 APIoU=0.50 with 96.5%, with APlarge is 79.9%, APmedium is 90.7%, APsmall is 93.5%; ARIoU=0.50 is 64.5%, where ARlarge is 60.6%, ARmedium is 58.3%, ARsmall 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.

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Advances in the Research of Ultrasound and Artificial Intelligence in Neuromuscular Disease
Tianxiang Li, BS, Fei Ji, BS, Ruina Zhao, MD, Huazhen Liu, MD, Meng Yang, MD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (2): 122-129.   DOI: 10.37015/AUDT.2023.230025
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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.

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State-of-the-Art and Development Trend of Interventional Ultrasound in China
Yang Qi, MD, Dengsheng Sun, MD, Linyao Wang, MD, Jie Yu, MD, Ping Liang, MD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (4): 313-320.   DOI: 10.37015/AUDT.2023.230049
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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.

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Intelligent Ultrasonic Diagnosis and Clinical Application: Technical Development and Prospectives
Rendong Chen, PhD, Xiaoqian Wang, BS, Ping Liang, MD, Xiaoping Ouyang, PhD, Dexing Kong, PhD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (2): 73-81.   DOI: 10.37015/AUDT.2023.230019
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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.

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Application and Prospect of AI and ABVS-based in Breast Ultrasound Diagnosis
Rui Chen, MM, Fangqi Guo, MM, Jia Guo, MD, Jiaqi Zhao, MD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (2): 130-135.   DOI: 10.37015/AUDT.2023.230017
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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.

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Application of the Virtual Reality in the Teaching of Ultrasonography
Zheng Zhang, MS, Li Liu, MD, Desheng Sun, MD, Dirong Zhang, MD, Fengbei Kong, MS, Yalin Wu, PhD, Yu Shi, MD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (2): 193-196.   DOI: 10.37015/AUDT.2023.230026
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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.

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Contrast-Enhanced Ultrasound LI-RADS: A Pictorial Review
Osama Mahmoud, BS, Ajay Makkena, BS, Corinne E. Wessner, MS, MBA, RDMS, Ji-Bin Liu, MD, John R. Eisenbrey, PhD, Andrej Lyshchik, MD, PhD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (4): 321-332.   DOI: 10.37015/AUDT.2023.230041
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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.

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Development of 5G-based Remote Ultrasound Education: Current Status and Future Trends
Jiaojiao Ma, MD, Xinying Jia, MD, Guanghan Li, MD, Dandan Guo, MD, Xuehua Xi, MD, Tongtong Zhou, MD, Ji-Bin Liu, MD, Bo Zhang, MD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (2): 197-203.   DOI: 10.37015/AUDT.2023.230022
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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.

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The Impact of Deep Learning on Ultrasound in Diagnosis and Therapy: Enhancing Clinical Decision Support, Workflow Efficiency, Quantification, Image Registration, and Real-time Assistance
Won-Chul Bang, PhD, Vice President, Yeong Kyeong Seong, PhD, Jinyong Lee
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (2): 204-216.  
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This review article introduces the main concepts and architectures of deep learning networks for medical imaging tasks, such as classification, detection, segmentation, and generation. It then surveys how deep learning has been applied to ultrasound imaging for various purposes, such as image processing, diagnosis, and workflow enhancement. It covers different organs and body parts that can be imaged by ultrasound, such as liver, breast, thyroid, heart, kidney, prostate, nerve, muscle, and fetus. It also discusses how deep learning can help with view recognition, registration, and quantification, measurement, image registration for interventional guidance, and real-time assistance while scanning. Moreover, it explores how generative AI can be used in the future medical field by leveraging deep learning for ultrasound imaging, such as generating realistic and diverse images, virtual organs/patients with diseases, synthesizing missing or corrupted data and augmenting existing data for training and testing.

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Domestic Large Model Technology and Medical Applications Analysis
Chengwen Zhang, PhD, Xing Yu, MD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (2): 172-187.   DOI: 10.37015/AUDT.2023.230027
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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.

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Clinical Application of Ultrasound Tomography in Diagnosis of Musculoskeletal Diseases
Cong Wei, MD, Hui Zhang, PhD, Tao Ying, MD, Bing Hu, MD, Yini Chen, MD, Hongtao Li, MD, Qiude Zhang, PhD, Mingyue Ding, PhD, Jie Chen, MD, Ming Yuchi, PhD, Yuanyi Zheng, MD
Advanced Ultrasound in Diagnosis and Therapy    2024, 8 (1): 7-14.   DOI: 10.37015/AUDT.2024.230060
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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.

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Artificial Intelligence-assisted Medical Imaging in Interventional Management of Valvular Heart Disease
Wenwen Chen, BS, Yuji Xie, MD, Zisang Zhang, MD, Ye Zhu, MS, Yiwei Zhang, MD, Shuangshuang Zhu, MD, PhD, Chun Wu, MD, PhD, Ziming Zhang, MD, Xin Yang, PhD, Man wei Liu, MD, PhD, Mingxing Xie, MD, PhD, Li Zhang, MD, PhD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (3): 217-227.   DOI: 10.37015/AUDT.2023.230030
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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.

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Advances and Applications of Transperineal Ultrasound Imaging in Female Pelvic Floor Dysfunction
Shuangyu Wu, MM , Xinling Zhang, MD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (3): 235-247.   DOI: 10.37015/AUDT.2023.220044
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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.

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Semi-supervised Learning for Real-time Segmentation of Ultrasound Video Objects: A Review
Jin Guo, MD, Zhaojun Li, PhD, Yanping Lin, PhD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (4): 333-347.   DOI: 10.37015/AUDT.2023.230016
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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.

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Experience and Enlightenment of Handheld Ultrasound Applications in Multiple Scenarios Based on 5G Technology
Huihui Chai, MS, Xiaowan Bo, MD, Lehang Guo, MD, Chengzhong Peng, MD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (4): 356-365.   DOI: 10.37015/AUDT.2023.230029
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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.

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Lung Nodule Classification in CT Images Using Improved DenseNet
Xiuping Men, PhD, Vladimir Y. Mariano, PhD, Aihua Duan, PhD, Xiaoyan Shi, PhD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (3): 272-278.   DOI: 10.37015/AUDT.2022.220018
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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.

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Arterial Stiffness and Cardiovascular Risk: The Role of Brachial Cuff-measured Index
Lin Jin, MD, Xinyi Li, BS, Mengjiao Zhang, MS, Xujie Zhang, BS, Chaoyu Xian, BS, Fuyou Liang, PhD, Zhaojun Li, MD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (4): 348-355.   DOI: 10.37015/AUDT.2023.230045
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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.

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Application Progress of Ultrasound Elastography in the Evaluation of Diabetic Peripheral Neuropathy
Siqi Zheng, MM, Min Bai, MM
Advanced Ultrasound in Diagnosis and Therapy    2024, 8 (1): 1-6.   DOI: 10.37015/AUDT.2024.230006
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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.

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A Non-Invasive Follicular Thyroid Cancer Risk Prediction System Based on Deep Hybrid Multi-feature Fusion Network
Yalin Wu, PhD, Qiaoli Ge, MM, Linyang Yan, PhD, Desheng Sun, MD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (4): 373-380.   DOI: 10.37015/AUDT.2023.230023
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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.

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Comparison of Sonographic Quantitative Assessment of Splenomegaly in Thalassemia Patients Receiving Whole Blood and Packed Red Cell Transfusions
Muhammad Arif Afridi, MS , Raham Bacha, PhD , Nadeem Ullah, BS , Syed Muhammad Yousaf Farooq, PhD , Malik Mairaj Khalid, BS , Imran Khan, BS , Ashfaq Ahmad, Mujahid Sher, PhD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (3): 279-284.   DOI: 10.37015/AUDT.2023.220039
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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.

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Ultrasound-Guided Attenuation Parameter May Replace B-mode Ultrasound in Diagnosing Nonalcoholic Fatty Liver Disease
Bo Jiang, MD, Yiman Du, MD, Xiang Fei, MD, Jianing Zhu, MD, Lianhua Zhu, MD, Qiuyang Li, MD, Yukun Luo, MD, PhD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (3): 260-266.   DOI: 10.37015/AUDT.2023.220037
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Objective: To compare the diagnostic sensitivity and consistency of ultrasound-guided attenuation parameter (UGAP) with B-mode ultrasound in nonalcoholic fatty liver disease (NAFLD) patients, and explored their correlation with clinical indicators.
Methods: Patients suspected of NAFLD from July to November 2021 were enrolled in this prospective study. After performing the B-mode ultrasound and UGAP examination, all patients were divided into four groups according to the grade of NAFLD obtained by two modalities, respectively. The diagnostic agreement of the two modalities were evaluated, and the diagnostic sensitivity was compared by the McNemar test. The correlation between clinical indicators and the attenuation coefficient (AC) of UGAP was analyzed by linear regression.
Results: The intraclass correlation coefficient of UGAP was 0.958 (95%CI: 0.943,0.970), while the kappa value of B-mode ultrasound grading was 0.799 (95%CI: 0.686, 0.912). The diagnostic sensitivity of UGAP was higher than that of B-mode ultrasound (99.0% vs. 32%, P < 0.001). BMI and TG can be distinguished in different grades of NAFLD diagnosed by B-mode ultrasound, while BMI, ALT, HDL, and Apo A can be distinguished in different grades of NAFLD diagnosed by UGAP. BMI (r = 0.502, P < 0.001), ALT (r = 0. 396, P < 0.001), TG (r = 0.418, P < 0.001), HDL (r = -0. 359, P < 0.001) and Apo A (r = -0.228, P = 0.020) were linearly correlated with the AC value of UGAP.
Conclusions: Compared with the B-mode ultrasound, UGAP had a higher sensitivity and consistency in diagnosing NAFLD, and correlated well with some laboratory indicators, which may be more valuable in screening and diagnosis of NAFLD.

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Localization of Nonpalpable Breast Lumps by Ultrasound Local Coordinates and Skin Inking: A Randomized Controlled Trial
Leila Bayani, MD, Donya Goodarzi, BS, Reza Mardani, MD, Bita Eslami, PhD, Sadaf Alipour, MD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (3): 267-271.   DOI: 10.37015/AUDT.2023.220033
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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.

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Withdrawal: Reproducibility of Ultrasound in Intima-media Thickness Measurement of Common Carotid Artery
Ziman Chen, MD, Chaoqun Wu, MD
Advanced Ultrasound in Diagnosis and Therapy    0, (): 284-287.   DOI: 10.37015/AUDT.2023.220031
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Systematic Approaches and Designs for the Optimal Diagnosis and Treatment of Thyroid Nodules via Fine Needle Aspiration
Jian-Quan Zhang, PhD, Lei Yan, MD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (3): 254-259.   DOI: 10.37015/AUDT.2023.230033
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With the increasing utilization of semi-thyroidectomy and rapid advancements in ultrasound-guided thermal ablation therapy for the management of papillary thyroid carcinoma (PTC) and PTC cervical lymph node metastasis, ultrasound-guided fine-needle aspiration biopsy (FNAB) has emerged as the predominant approach for the pre-treatment cytopathologic diagnosis of PTC. Numerous expert consensuses and practice guidelines have delineated the acquisition of sufficient, high-quality cellular specimens for cytological examination. However, new challenges keep emerging in the real-world practice of thyroid FNAB, primarily stemming from the perceptions and expertise of physicians or technicians who perform FNAB. The aim of this study was to delineate the key deficiencies in specimen collection during FNAB, elucidate principles of systematic thinking, and propose preventive measures for these issues, along with a range of innovative concepts and technical approaches. Effectively addressing these concerns will enhance FNAB implementation and facilitate advancements in novel therapeutic modalities, such as thermal ablation, to ameliorate prognosis.

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The Leap from Digitalization to Intelligentization of Medical Digital Ultrasonic Imaging Workstations
Yande Zhang, DBA, Yanjun Cheng, MBME, Yingxin Li, MCS, Shengli Li, MM
Advanced Ultrasound in Diagnosis and Therapy    0, (): 305-312.  
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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.

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Clinical Application of Robot-assisted Teleultrasound
Keyan Li, MD, Faqin Lv, MD, Junlai Li, MD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (3): 228-234.   DOI: 10.37015/AUDT.2023.230031
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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.

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Juvenile Granulosa Cell Tumor of the Testis: A Preoperative Approach of the Diagnosis with Ultrasound
Rodanthi Sfakiotaki, MS, Sergia Liasi, BM, Eleni Papaiakovou, BM, Irene Vraka, PhD, Marina Vakaki, PhD, Chrysoula Koumanidou, PhD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (4): 409-411.   DOI: 10.37015/AUDT.2023.220038
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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.

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Review on Image Inpainting using Intelligence Mining Techniques
V. Merin Shobi, MCA, MPhil , ME, F. Ramesh Dhanaseelan, MSc, MTech , PhD
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (4): 366-372.   DOI: 10.37015/AUDT.2023.230007
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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.

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Cover, Foreword and Content
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (3): 0-0.  
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Decreased Cerebral Flow Velocities from General Anesthesia are Not Associated with Cerebral Hyperperfusion Syndrome
Yumei Liu, MD, PhD, Yang Hua, MD, Yabing Wang, MD, PhD, Nan Zhang, MS, Ting Ma, MD, PhD, Yue Zhao, MS, Na Li, MS, Na Lei, MS, Ran Liu, MS
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (3): 248-253.   DOI: 10.37015/AUDT.2023.220032
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Objective: General anesthesia (GA) can decrease cerebral flow velocities and predispose patients to cerebral hyperperfusion syndrome (CHS) and other perioperative adverse events after carotid endarterectomy (CEA). The aim of this study was to investigate whether decreased pre-operative flow velocity is associated with an increased risk of CHS and perioperative cerebral infarct, and to further identify risk factors if there is any.
Methods: We retrospectively evaluated 920 consecutive patients who received CEA from 2010 to 2020 at a major academic hospital in China. Middle cerebral artery (MCA) blood flow velocities were measured before and after induction of the GA by transcranial Doppler (TCD). Patients were classified into two groups: the NORMAL group if flow velocity decreased<30% and the LOW group if flow velocity decreased ≥30%. The ultrasonographic diagnostic criterion of CHS was defined as the 100% increase in flow velocity by TCD from the baseline to post-CEA. The occurrence of CHS, perioperative cerebral infarction was compared between the two groups.
Results: 399 (43.4%) were classified as LOW measurement, and 521 (56.6%) patients were classified as NORMAL measurement. In the LOW group, there were more patients with diabetes, fewer patients with ipsilateral ICA severe stenosis and the opening of anterior/posterior communicating artery. Although the occurrence of CHS per ultrasonography criteria was higher in the LOW group (21.3% vs 15.7%, P = 0.03), the occurrence of CHS per clinical criteria (3.2%, vs 2.1%, P = 0.28) or the perioperative cerebral infarct between the two groups (5.8% vs 5.0%, P = 0.60) is equivalent.
Conclusion: Patients with decreased flow velocities post-GA were more likely to meet the ultrasonography criteria for CHS, but they are not at risk of developing clinical CHS or perioperative cerebral infarct.

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Cover, Foreword and Content
Advanced Ultrasound in Diagnosis and Therapy    2023, 7 (2): 0-0.  
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Open Access, Peer-reviewed

ISSN 2576-2516 (Online)

ISSN 2576-2508 (Print)

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