Diagnostic ultrasound is widely used for evaluating carpal tunnel syndrome (CTS), an entrapment neuropathy of the median nerve (MN). Decreased mobility of the MN inside the carpal tunnel has been reported in CTS, and various methods have been used to evaluate MN mobility; however, there is still no conclusive understanding of its connection with CTS. The purpose of this study is to conduct a systematic review and meta-analysis of the current published literature on ultrasonographic evaluations of transverse and longitudinal MN displacement and to identify the relationship between MN mobility and CTS. This study was conducted in accordance with the 2020 PRISMA statement and the Cochrane Collaboration Handbook. Comparative studies that investigated differences in MN displacement between CTS patients and healthy controls were retrieved by searching the Cochrane Library, Embase and PubMed. A total of 15 case–control studies were included. Nine of 12 studies evaluating transverse MN displacement and 4 of 5 studies evaluating longitudinal MN gliding showed that the MN was less mobile in CTS patients than in healthy subjects. Despite the large heterogeneity among the 15 included studies, this systematic review and meta-analysis provide evidence that the mobility of the MN is significantly reduced in both transverse and longitudinal planes in CTS patients compared to healthy controls. Five of the 15 included studies reported that a decrease in transverse or longitudinal MN displacement in CTS was correlated with clinical symptoms or with severity as measured by a nerve conduction study (NCS).

In addition to nerve conduction studies (NCSs), ultrasonography has been widely used as an alternative tool for diagnosing carpal tunnel syndrome (CTS). Although the results of NCSs are influenced by local skin temperature, few studies have explored the effects of skin temperature on ultrasonography of the median nerve. Since swelling and intraneural blood flow of the median nerve might be influenced by local temperature changes, the aim of this study was to evaluate the cross-sectional area (CSA) and intraneural blood flow of the median nerve under three skin temperatures (30 °C, 32 °C, 34 °C). Fifty patients with CTS and 50 healthy volunteers were consecutively recruited from a community hospital. Each participant received physical examinations and NCSs and underwent ultrasonography, including power Doppler, to evaluate intraneural vascularity. The CSA of the median nerve in the CTS patients was significantly larger than that in the healthy controls at all three temperatures. However, significant differences in the power Doppler signals of the median nerve between the two studied groups were observed only at 30 and 32 °C, not at 34 °C. The significant difference in the intraneural vascularity of the median nerve between the patients with CTS and the healthy subjects was lost at higher temperatures (34 °C). Therefore, the results of power Doppler ultrasonography in diagnosing CTS should be cautiously interpreted in patients with a high skin temperature or those who reside in warm environments.

Breast cancer is the most common form of cancer and is still the second leading cause of death for women in the world. Early detection and treatment of breast cancer can reduce mortality rates. Breast ultrasound is always used to detect and diagnose breast cancer. The accurate breast segmentation and diagnosis as benign or malignant is still a challenging task in the ultrasound image. In this paper, we proposed a classification model as short-ResNet with DC-UNet to solve the segmentation and diagnosis challenge to find the tumor and classify benign or malignant with breast ultrasonic images. The proposed model has a dice coefficient of 83% for segmentation and achieves an accuracy of 90% for classification with breast tumors. In the experiment, we have compared with segmentation task and classification result in different datasets to prove that the proposed model is more general and demonstrates better results. The deep learning model using short-ResNet to classify tumor whether benign or malignant, that combine DC-UNet of segmentation task to assist in improving the classification results.

Background: Carpal tunnel syndrome (CTS) is a common neuropathy caused by compression of the median nerve (MN) within the carpal tunnel, which causes pain, paresthesia, or altered sensation. While a small carpal tunnel area is considered a risk factor for CTS, varying carpal tunnel dimensions in CTS patients have been obtained via axial computed tomography and magnetic resonance imaging (MRI). Methods: In this retrospective study, MR images from 49 CTS patients and 38 healthy controls were analyzed to investigate differences in the carpal tunnel area and carpal boundaries between the groups and to explore the relationships of these parameters with CTS severity. Results: Our findings revealed that compared with the controls, CTS patients presented significantly larger cross-sectional areas (CSAs) of the MN and carpal tunnel and increased MN flattening ratios. The CSAs of the MN showed moderate positive correlations with severity (r = 0.395, p < 0.001), symptom score (r = 0.354, p < 0.001), and disability score (r = 0.300, p < 0.001), while the carpal tunnel area showed weaker but significant correlations with severity (r = 0.268, p = 0.002), symptom score (r = 0.173, p = 0.026), and disability score (r = 0.183, p = 0.018). The ratios of the MN CSA to those of the carpal tunnel, the interior carpal boundary (ICB), the exterior carpal boundary (ECB), and the wrist were disproportionately greater in the CTS patients. Among them, both the MN-to-ICB and MN-to-ECB ratios had fair to good diagnostic values (area under the curve = 0.725 and 0.794, respectively). Conclusions: These results highlight the utility of MRI-derived CSA measurements and ratios in identifying pathophysiological changes in CTS patients, particularly crowding of the MN inside the carpal tunnel. Further studies are recommended to refine MRI-based diagnostic protocols for CTS.

A style transfer aims to produce synthesized images that retain the content of one image while adopting the artistic style of another. Traditional style transfer methods often require training separate transformation networks for each new style, limiting their adaptability and scalability. To address this challenge, we propose a flow-based image style transfer framework that integrates Randomized Hierarchy Flow (RH Flow) and a meta network for adaptive parameter generation. The meta network dynamically produces the RH Flow parameters conditioned on the style image, enabling efficient and flexible style adaptation without retraining for new styles. RH Flow enhances feature interaction by introducing a random permutation of the feature sub-blocks before hierarchical coupling, promoting diverse and expressive stylization while preserving the content structure. Our experimental results demonstrate that Meta FIST achieves superior content retention, style fidelity, and adaptability compared to existing approaches.