UNASSIGNED: To investigate the status quo and the needs of medical imaging technicians (MITs) in the radiology department of secondary and tertiary hospitals in China, so as to provide references and support for the development of the medical imaging technology industry and the relevant policymaking by health administrative departments.
UNASSIGNED: The questionnaire was developed by the Chinese Society of Imaging Technology. The radiology department of each hospital involved in the survey recommended one MIT to fill out the online questionnaire. The contents included: (a) the basic information of the hospital; (b) a general overview of the MITs in the hospital; (c) daily work; (d) career development and promotion; (e) research status and needs, etc. Differences in the number of MIT staff were compared using the Mann-Whitney U test and the chi-square test was used to compare the differences in the selected numbers of MITs in need between regions or between different levels of hospitals.
UNASSIGNED: In this investigation, valid questionnaires were finally obtained from a total of 5403 hospitals in 31 provinces in China. The total number of MITs of the hospitals covered in the sample was 67481. The number of MITs in each hospital was 9 (5, 16). The male-to-female ratio was 1.41:1. MITs who were 20 to 40 years old accounted for 78%. The proportions of MITs who had completed doctorate, master\'s, undergraduate, junior college, and technical secondary school or lower level education were 0.6%, 3.3%, 60.7%, 30.8%, and 4.55%, respectively. The proportions of chief MITs, deputy chief MITs, supervisor MITs, primary MITs, assistant technician and those below were 1.0%, 4.21%, 22.1%, 51.8%, and 20.9%, respectively. The overall professional satisfaction of MITs was good. \"Lack of opportunities for learning and communication\" was quoted as the main problem MITs encountered in regard to improving their job-related competency. 59.2% of the respondents had not published any academic papers in the past five years, and only 7.0% of the respondents had published in journals included in the Science Citation Index (SCI) in the past five years.
UNASSIGNED: MITs in China are on average relatively young and the number of MITs has greatly increased. At this stage, more attention should be given to the cultivation of talents and continuing education of MITs and the construction of the discipline should be further strengthened, so as to provide strong support for the development of the medical imaging technology industry in China.

Ameloblastic fibrodentinoma (AFD) is a rare benign odontogenic tumor that resembles an ameloblastic fibroma with dysplastic dentin. This report presents a rare case of mandibular AFD with imaging features in a young patient. Panoramic radiography and computed tomography revealed a well-defined lesion with internal septa and calcified foci, causing inferior displacement of the adjacent molars as well as buccolingual cortical thinning and expansion of the posterior mandible. The lesion was surgically removed via mass excision, and the involved tooth was extracted under general anesthesia. During the 5-year follow-up period, no evidence of recurrence was observed. Radiologic features of AFD typically reveal a moderately to well-defined mixed lesion with varying degrees of radiopacity, reflecting the extent of dentin formation. Radiologists should consider AFD in the differential diagnosis when encountering a multilocular lesion with little dense radiopacity, particularly if it is associated with delayed eruption, impaction, or absence of involved teeth, on radiographic images of young patients.

UNASSIGNED: A fracture is considered a medical emergency leading to considerable complications.
UNASSIGNED: This study aimed to describe the accelerating action of Ag-NPs-FG on fracture healing in rabbits.
UNASSIGNED: Silver NPs (AgNPs) were reduced with fenugreek (FG), loaded into a starch gel base, and investigated for their morphology, size, and charge. Four equal groups were randomly formed of 40 adult male rabbits. A 3.5 mm diameter bone defect was created at the proximal metaphysis of the right tibia in each rabbit. Groups 1-4 were injected with placebo saline, AgNPs-FG, plain gel, and FG-gel at the bone defect zone, respectively. The healing was assessed for 8 weeks postoperatively based on the radiographic, bone turnover markers, and histopathological examinations.
UNASSIGNED: The AgNPs-FG was obtained as a faint reddish color, spherical in shape, with an absorbance of 423 nm, a size of 118.0 ± 1.7 nm, and a surface charge of -7.8 ± 0.518 mV. The prepared AgNPs-FG hydrogel was clear, translucent, and homogenous. The pH values were 6.55-6.5 ± 0.2, the viscosity of 4,000 and 1,875 cPs, and spreadability of 1.6 ± 0.14 and 2.0 ± 0.15 for both FG and AgNPs-FG hydrogel, respectively. The radiographic union scale was significantly (p < 0.05) improved in group 2 with a significant (p < 0.05) increase in bone turnover markers was found in comparison to other treated groups. Histopathological examination revealed the formation of mature bone on the 28th postoperative day in groups 2 and 4.
UNASSIGNED: Colloidal nano-formulation of AgNPs-FG loaded hydrogel could be a promising formulation to accelerate rabbits\' tibial bone healing process.

In response to the issues of single-scale information loss and large model parameter size during the sampling process in U-Net and its variants for medical image segmentation, this paper proposes a multi-scale medical image segmentation method based on pixel encoding and spatial attention. Firstly, by redesigning the input strategy of the Transformer structure, a pixel encoding module is introduced to enable the model to extract global semantic information from multi-scale image features, obtaining richer feature information. Additionally, deformable convolutions are incorporated into the Transformer module to accelerate convergence speed and improve module performance. Secondly, a spatial attention module with residual connections is introduced to allow the model to focus on the foreground information of the fused feature maps. Finally, through ablation experiments, the network is lightweighted to enhance segmentation accuracy and accelerate model convergence. The proposed algorithm achieves satisfactory results on the Synapse dataset, an official public dataset for multi-organ segmentation provided by the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), with Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95) scores of 77.65 and 18.34, respectively. The experimental results demonstrate that the proposed algorithm can enhance multi-organ segmentation performance, potentially filling the gap in multi-scale medical image segmentation algorithms, and providing assistance for professional physicians in diagnosis.
针对医学图像分割中U型网络（U-Net）及其变体下采样过程中单尺度信息丢失、模型参数量较大的问题，本文提出了一种基于像素编码和空间注意力的多尺度医学图像分割方法。首先，通过重新设计变换器（Transformer）结构输入策略，提出了像素编码模块，使模型能够从多尺度图像特征中提取全局语义信息，获取更丰富的特征信息，同时在Transformer模块中引入可变形卷积，加快收敛速度的同时提升模块性能。其次，引入空间注意力模块并加入残差连接，使模型能够重点关注融合后特征图的前景信息。最后，通过消融实验实现网络轻量化并提升分割精度，加快模型收敛。本文所提算法在国际计算机医学图像辅助协会官方公开多器官分割公共数据集——突触（Synapse）数据库中得到令人满意的结果，戴斯相似性系数（DSC）和95%豪斯多夫距离系数（HD95）分别为77.65和18.34。实验结果表明，本文算法能够提高多器官分割结果，有望完善多尺度医学图像分割算法的空白，并为专业医师提供辅助诊断。.

(1) Background: An online survey-based observational cross-sectional study aimed at elucidating the experience and attitudes of an unstructured population regarding diagnostic imaging. (2) Methods: Invitations to participate were distributed using mixed-mode design to deidentified residents aged 18 years and older. Main outcome measures included morbidity structure and incidence of diagnostic imaging administrations. (3) Results: Respondents (n = 1069) aged 44.3 ± 14.4 years; 32.8% suffered from cardiovascular diseases (CVD); 9.5% had chronic respiratory pathology; 28.9% considered themselves healthy. Respondents with COVID-19 history (49.7%) reported higher rates of computed tomography (CT) (p < 0.0001), magnetic resonance imaging (MRI) (p < 0.001), and ultrasound (p < 0.05). COVID-19 history in CVD respondents shifted imaging administrations towards CT and MRI (p < 0.05). Every tenth respondent received MRI, CT, and ultrasound on a paid basis; 29.0% could not pay for diagnostic procedures; 13.1% reported unavailable MRI. Professional status significantly affected the pattern of diagnostic modalities (p < 0.05). MRI and CT availability differed between respondents in urban and rural areas (p < 0.0001). History of technogenic events predisposed responders to overestimate diagnostic value of fluorography (p < 0.05). (4) Conclusions: Preparedness to future pandemics requires the development of community-based outreach programs focusing on people\'s awareness regarding medical imaging safety and diagnostic value.

The field of computed tomography (CT), which is a basic diagnostic tool in clinical practice, has recently undergone rapid technological advances. These include the evolution of dual-energy CT (DECT) and development of photon-counting computed tomography (PCCT). DECT enables the acquisition of CT images at two different energy spectra, which allows for the differentiation of certain materials, mainly calcium and iodine. PCCT is a recent technology that enables a scanner to quantify the energy of each photon gathered by the detector. This method gives the possibility to decrease the radiation dose and increase the spatial and temporal resolutions of scans. Both of these techniques have found a wide range of applications in radiology, including vascular studies. In this narrative review, the authors present the principles of DECT and PCCT, outline their advantages and drawbacks, and briefly discuss the application of these methods in vascular radiology.

Vulval cancer is a rare gynaecological cancer, accounting for 3% of all gynaecological malignancies, with 47,000 cases in 2022 globally. Various imaging modalities are widely used in conjunction with clinical assessment in the diagnosis and staging of vulval cancers; however, there is significant heterogeneity in which modalities are recommended in international guidelines, reflecting the paucity of evidence in this area. We reviewed the current evidence for the role of imaging in vulval cancer. A systematic search of the literature was performed on the PubMed database using the MeSH terms \'vulval neoplasm\' and \'diagnostic imaging\'. We found that there is insufficient evidence to support the routine use of imaging for primary vulval tumours. For nodal assessment, there is no ideal imaging modality with sensitivity or specificity that is superior to other modalities. For distant metastases, CT CAP and FDG-PET/CT have the most evidence to support their use. In conclusion, the evidence for role of imaging in vulval cancer is limited by the heterogeneity of the study design and diagnostic criteria used in each study and the small sample size and retrospective nature of most studies.

The congenital Gerbode defect is defined as an abnormal communication between the left ventricle and the right atrium. This review aimed to summarize existing evidence, shed light on the clinical implications, and identify knowledge gaps. The systematic literature search was conducted in the PubMed and Google Scholar medical databases using specifically selected keywords. The inclusion of each publication was assessed according to predefined eligibility criteria based on the PICOM (Population, Phenomenon of Interest, Context, Methodology) schema. Titles and abstracts were screened independently by two authors. Available full-text versions of included publications were reviewed and relevant information was extracted. A total of 78 reports were included. The compilation of all congenital Gerbode defect cases described in the literature revealed a variety of clinical presentations comprising dyspnea, palpitations, growth retardation, and asymptomatology. A suitable multimodal diagnostic approach for newborns consists of auscultation, TTE, and optionally TEE and MRI. Because of its rarity, diversity of findings, unknown pathophysiology, and similarity to more common cardiac diseases, the diagnostic challenge remains significant. To prevent untreated long-term sequelae, early individualized treatment is recommended. Surgical defect closure is preferred to device closure for evidence reasons, although major developments are currently taking place. In conclusion, the congenital Gerbode defect provides a diagnostic challenge for pediatricians to allow early diagnosis and intervention in order to improve patients\' quality of life.

BACKGROUND: After breast conserving surgery (BCS), surgical clips indicate the tumor bed and, thereby, the most probable area for tumor relapse. The aim of this study was to investigate whether a U-Net-based deep convolutional neural network (dCNN) may be used to detect surgical clips in follow-up mammograms after BCS.
METHODS: 884 mammograms and 517 tomosynthetic images depicting surgical clips and calcifications were manually segmented and classified. A U-Net-based segmentation network was trained with 922 images and validated with 394 images. An external test dataset consisting of 39 images was annotated by two radiologists with up to 7 years of experience in breast imaging. The network\'s performance was compared to that of human readers using accuracy and interrater agreement (Cohen\'s Kappa).
RESULTS: The overall classification accuracy on the validation set after 45 epochs ranged between 88.2% and 92.6%, indicating that the model\'s performance is comparable to the decisions of a human reader. In 17.4% of cases, calcifications have been misclassified as post-operative clips. The interrater reliability of the model compared to the radiologists showed substantial agreement (κreader1 = 0.72, κreader2 = 0.78) while the readers compared to each other revealed a Cohen\'s Kappa of 0.84, thus showing near-perfect agreement.
CONCLUSIONS: With this study, we show that surgery clips can adequately be identified by an AI technique. A potential application of the proposed technique is patient triage as well as the automatic exclusion of post-operative cases from PGMI (Perfect, Good, Moderate, Inadequate) evaluation, thus improving the quality management workflow.

Diagnosing endometrial carcinoma correctly is essential for appropriate treatment, as it is a major health risk. As machine learning (ML) and artificial intelligence (AI) have grown in popularity, so has interest in their potential to improve cancer diagnosis accuracy. In the context of endometrial cancer, this study attempts to examine the efficacy as well as the accuracy of AI-assisted diagnostic approaches. Additionally, it aims to methodically evaluate the contribution of AI and ML techniques to the improvement of endometrial cancer diagnosis. Following PRISMA guidelines, we performed a thorough search of numerous databases, including Medline via Ovid, PubMed, Scopus, Web of Science, and Google Scholar. Ten years were searched, encompassing both basic and advanced research. Peer-reviewed papers and original research studies that explicitly looked at the application of AI/ML in endometrial cancer diagnosis were the main targets of the well-defined selection criteria. Using the Critical Appraisal Skills Programme (CASP) methodology, two independent researchers conducted a thorough screening process and quality assessment of included studies. The review found a notable inclination towards the effective use of AI in endometrial carcinoma diagnostics, namely in the identification and categorization of endometrial cancer. Artificial intelligence models, particularly Convolutional Neural Networks (CNNs) and deep learning algorithms have shown remarkable precision in detecting endometrial cancer. They frequently achieve or even exceed the diagnostic proficiency of human specialists. The use of artificial intelligence in medical diagnostics signifies revolutionary progress in the field of oncology. AI-assisted diagnostic tools have demonstrated the potential to improve the precision and effectiveness of cancer diagnosis, namely in cases of endometrial carcinoma. This innovation not only enhances the quality of patient care but also indicates a transition towards more individualized and efficient treatment approaches in the field of oncology. The advancement of AI technology is expected to play a crucial role in medical diagnostics, particularly in the field of cancer detection and treatment, perhaps leading to a significant transformation in the approach to these areas.