Cardiac implantable electronic device (CIED) is commonly used to treat arrhythmias. The amount of CIED implantation has increased year by year since its first application in 1958. At the same time, the CIED infection rate also increases significantly. Although the infection rate is very low for the primary implantation, the consequences of CIED infection are serious, and it is often necessary to remove the equipment for treatment. The mortality rate in patients with CIED infections is high, and the economic burden is huge. In this paper, the epidemiology, pathogenesis and pathogen, manifestations and diagnosis, risk factors, treatment and preventive measures of CIED infection were systematically described based on the recently published guidelines and international consensus literature, so as to provide theoretical guidance for the prevention of CIED infections.
Objective To summary the recent progression of imaging methods which mainly applied on the early detection and qualitative diagnosis of pancreatic cancer. Method The newest related literatures between home and abroad were collected and reviewed. Results Ultrasonic, computed tomography, magnetic resonance imaging and positron emission tomography mostly be used on pancreatic cancer detection and diagnosis. Conclusion Each method gets its own advantage even computed tomography seems like dominated on the detection and diagnosis pancreatic cancer, moreover, magnetic resonance imaging has been improved rapidly in recent years which shows its enormous potential.
ObjectiveTo analyze the influencing factors for image quality of 18F-deoxyglucose (FDG) positron emission tomography (PET)/CT systemic tumor imaging and explore the method of control in order to improve the PET/CT image quality. MethodsRetrospective analysis of image data from March to June 2011 collected from 1 000 18F-FDG whole body tumor imaging patients was carried out. We separated standard films from non-standard films according to PET/CT image quality criteria. Related factors for non-standard films were analyzed to explore the entire process quality control. ResultsThere were 158 cases of standard films (15.80%), and 842 of non-standard films (84.20%). Artifact was a major factor for non-standard films (93.00%, 783/842) followed by patients’ injection information recording error (2.49%, 21/842), the instrument factor (1.90%, 16/842), incomplete scanning (0.95%, 8/842), muscle and soft tissue uptake (0.83%, 7/842), radionuclide contamination (0.59%, 5/842), and drug injection (0.24%, 2/842). The waste film rate was 5.80% (58/1 000), and the redoing rate was 2.20% (22/1 000). ConclusionComplex and diverse factors affect PET/CT image quality throughout the entire process, but most of them can be controlled if doctors, nurses and technicians coordinate and cooperate with each other. The rigorous routine quality control of equipment and maintenance, patients’ full preparation, appropriate position and scan field, proper parameter settings, and post-processing technology are important factors affecting the image quality.
A mobile operating room information management system with electronic medical record (EMR) is designed to improve work efficiency and to enhance the patient information sharing. In the operating room, this system acquires the information from various medical devices through the Client/Server (C/S) pattern, and automatically generates XML-based EMR. Outside the operating room, this system provides information access service by using the Browser/Server (B/S) pattern. Software test shows that this system can correctly collect medical information from equipment and clearly display the real-time waveform. By achieving surgery records with higher quality and sharing the information among mobile medical units, this system can effectively reduce doctors' workload and promote the information construction of the field hospital.
Lung diseases such as lung cancer and COVID-19 seriously endanger human health and life safety, so early screening and diagnosis are particularly important. computed tomography (CT) technology is one of the important ways to screen lung diseases, among which lung parenchyma segmentation based on CT images is the key step in screening lung diseases, and high-quality lung parenchyma segmentation can effectively improve the level of early diagnosis and treatment of lung diseases. Automatic, fast and accurate segmentation of lung parenchyma based on CT images can effectively compensate for the shortcomings of low efficiency and strong subjectivity of manual segmentation, and has become one of the research hotspots in this field. In this paper, the research progress in lung parenchyma segmentation is reviewed based on the related literatures published at domestic and abroad in recent years. The traditional machine learning methods and deep learning methods are compared and analyzed, and the research progress of improving the network structure of deep learning model is emphatically introduced. Some unsolved problems in lung parenchyma segmentation were discussed, and the development prospect was prospected, providing reference for researchers in related fields.
There are some problems in positron emission tomography/ computed tomography (PET/CT) lung images, such as little information of feature pixels in lesion regions, complex and diverse shapes, and blurred boundaries between lesions and surrounding tissues, which lead to inadequate extraction of tumor lesion features by the model. To solve the above problems, this paper proposes a dense interactive feature fusion Mask RCNN (DIF-Mask RCNN) model. Firstly, a feature extraction network with cross-scale backbone and auxiliary structures was designed to extract the features of lesions at different scales. Then, a dense interactive feature enhancement network was designed to enhance the lesion detail information in the deep feature map by interactively fusing the shallowest lesion features with neighboring features and current features in the form of dense connections. Finally, a dense interactive feature fusion feature pyramid network (FPN) network was constructed, and the shallow information was added to the deep features one by one in the bottom-up path with dense connections to further enhance the model’s perception of weak features in the lesion region. The ablation and comparison experiments were conducted on the clinical PET/CT lung image dataset. The results showed that the APdet, APseg, APdet_s and APseg_s indexes of the proposed model were 67.16%, 68.12%, 34.97% and 37.68%, respectively. Compared with Mask RCNN (ResNet50), APdet and APseg indexes increased by 7.11% and 5.14%, respectively. DIF-Mask RCNN model can effectively detect and segment tumor lesions. It provides important reference value and evaluation basis for computer-aided diagnosis of lung cancer.
ObjectiveTo systematically review the impact of different electronic health technologies on weight loss outcomes in overweight and simple obesity populations. MethodsThe Cochrane Library, Embase, PubMed and WOS databases were electronically searched to collect randomized controlled trials (RCTs) related to the objectives from inception to May 2024. Two reviewers independently screened literature, extracted data and assessed the risk of bias of the included studies. Meta-analysis was then performed by using RevMan 5.4 and Stata 18 software. ResultsA total of 9 RCTs involving 2 416 patients with overweight or simple obesity were included. The meta-analysis results showed that body weight (MD=−0.81, 95%CI −1.1 to −0.52, P<0.001), BMI (MD=−0.63, 95%CI −0.89 to −0.37, P<0.001), waist circumference (MD=−1.06, 95%CI −1.70 to −0.42, P<0.001) and energy intake (SMD=−0.44, 95%CI −0.75 to −0.13, P=0.005) in the e-health technology group were significantly improved compared with the control group. But there was no statistically significant difference in physical activity between two groups. ConclusionThe available evidence suggests that e-health technology is an effective tool for weight loss. Due to the limited quality and quantity of the included studies, more high quality studies are needed to verify the above conclusion.
肺栓塞( PE) 的确诊依赖于肺动脉的影像学检查。电子计算机断层扫描肺动脉造影( CTPA) 诊断PE 的敏感性和特异性高[ 1] , 而且该项检查是无创技术, 患者痛苦小, 并发症少, 已成为诊断PE 的一线技术[ 2,3] 。随着CT 仪器的不断升级和改进以及检查技术的不断研究, CT 在PE 中的应用不再仅限于PE 的定性诊断, 还用于肺动脉栓塞程度的量化、右心室改变的诊断、患者预后判断以及下肢深静脉血栓形成( DVT) 的诊断等。
The establishment of brain metabolic network is based on 18fluoro-deoxyglucose positron emission computed tomography (18F-FDG PET) analysis, which reflect the brain functional network connectivity in normal physiological state or disease state. It is now applied to basic and clinical brain functional network research. In this paper, we constructed a metabolic network for the cerebral cortex firstly according to 18F-FDG PET image data from patients with temporal lobe epilepsy (TLE).Then, a statistical analysis to the network properties of patients with left or right TLE and controls was performed. It is shown that the connectivity of the brain metabolic network is weakened in patients with TLE, the topology of the network is changed and the transmission efficiency of the network is reduced, which means the brain metabolic network connectivity is extensively impaired in patients with TLE. It is confirmed that the brain metabolic network analysis based on 18F-FDG PET can provide a new perspective for the diagnose and therapy of epilepsy by utilizing PET images.