Article of the Year 2021
Micro-CT Evaluation of Four Root Canal Obturation TechniquesRead the full article
Scanning publishes international and interdisciplinary research focused on scanning electron, scanning probe, and scanning optical microscopies, and their advancement and applications.
Chief Editor, Guosong Wu is a Professor at the College of Mechanics & Materials in Hohai University. His research interests include surface engineering, corrosion science, metals and plasma related technologies.
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DSA Image Analysis of Clinical Features and Nursing Care of Cerebral Aneurysm Patients Based on the Deep Learning Algorithm
Objective. A deep learning algorithm was developed for automatic detection and localization of intracranial aneurysms in DSA, and its clinical characteristics were analyzed, and targeted nursing measures were formulated. Methods. Using a retrospective multicenter study method based on radiology reports, DSA images of aneurysms were randomly divided into 75 cases in the training set, 20 cases in the internal test set, and 35 cases in the external test set. Using a computer-aided detection method based on the three-dimensional U-Net (3D U-Net), after preprocessing DSA images, automatic segmentation of intracranial blood vessels is performed to obtain regions of interest, and based on the segmentation results, physicians’ annotations are introduced. The 3D U-Net network model is trained and adjusted, and the obtained model is used to automatically detect the cerebral aneurysm area. Results. Fivefold cross-validation was used for the training set and the internal test set, and a sensitivity of was obtained. Automatic detection of aneurysms was performed on the external test set, and the average false positive rate was 0.86 FPs/case (false positives/case). The resulting sensitivity was 82.9%. The classification comparison of external test sets showed that the sensitivity of the method for detecting aneurysms with sizes of 5.00~<10.00 mm and ≥10.00 mm (88.2% and 100.0%) was higher than that for aneurysms with sizes of <3.00 mm and 3.00~<5.00 mm (50.0% and 72.7%). The sensitivity of patients aged 50-60 years and >60 years (90.0% and 87.5%) was higher than that of patients aged <50 years (66.7%), and there was little difference between different genders (84.6% in males and 81.8% in females). Conclusion. The deep learning algorithm has high diagnostic performance in detecting intracranial aneurysms, which is verified by external datasets.
Developments of Interfacial Measurement Using Cavity Scanning Microwave Microscopy
In the field of materials research, scanning microwave microscopy imaging has already become a vital research tool due to its high sensitivity and nondestructive testing of samples. In this article, we review the main theoretical and fundamental components of microwave imaging, in addition to the wide range of applications of microwave imaging. Rather than the indirect determination of material properties by measuring dielectric constants and conductivity, microwave microscopy now permits the direct investigation of semiconductor devices, electromagnetic fields, and ferroelectric domains. This paper reviews recent advances in scanning microwave microscopy in the areas of resolution and operating frequency and presents a discussion of possible future industrial and academic applications.
Influence of Comprehensive Nursing Intervention Combined with WeChat Platform Propaganda and Education of ERAS Concept on Postoperative Functional Recovery of Patients with Gallbladder Polyps
To analyze the effect of comprehensive nursing intervention based on ERAS’s concept in laparoscopic gallbladder polyp (GP) surgery on patients’ postoperative quality of life and nursing job satisfaction. Ninety patients with polyps were included in this article until October 2021. In this format, the 45 cases are divided into governing bodies and committees according to their processing time. As recommended by the ERAS committee, the committee provides daily and patient care, as well as training on the WeChat platform. The pain level (visual analogue scale (VAS) score), the quality of life (life quality index (GLQI) score), and the incidence of complications were compared between the two groups before and after the intervention. The VAS score of the control group at 2 h after operation was lower than that of the control group, and the difference was statistically significant (). After the intervention, the GLQI scores of the two groups were higher than those before the intervention, and the GLQI scores of the control group were higher than those of the control group, with significant differences (all ). Studies have shown that comprehensive nursing intervention applied to patients with gallbladder polyps can reduce postoperative pain with less complications and can also improve nursing satisfaction, which is worthy of clinical promotion.
High-Intensity Injury Recognition Pattern of Sports Athletes Based on the Deep Neural Network
In order to solve the problem of low efficiency and accuracy of injury image recognition for sports athletes in high-intensity injury treatment, this paper proposes an injury recognition mode based on the deep neural network. In this paper, the image of sports injury is converted to gray level, and the contour of the injury part in the image is extracted according to the combination of adaptive thresholding and mathematical morphology. In this model, the seed points are selected, the active contour is used to approximate the initial contour, and the curve fitting method is used to fit the obtained discrete points to obtain the final damaged contour. The digital matrix is constructed by using the extracted number of pixels at the damaged position and relevant information. The images are arranged into feature vectors with a length of 64 according to the mode of column concatenation. The overall mean vector of the image is calculated. The calculation results, training samples, and image samples to be recognized are substituted into the Euclidean distance to obtain the preliminary recognition results of the damaged position of the image of sports injury. Then, the image segmentation is realized by clustering. The clustering segmentation results are used to color describe the pixel categories of the original image, calculate the relative damage proportion area in the sports injury image, and identify the damage parts of the high-intensity sports injury image. The experimental results show that the recognition rate of the neural network is 80%-100%, and the recognition time of this method is 0-0.6/s. The above method can improve the accuracy of the recognition of the damaged part of the sports injury image and shorten the recognition time and has certain feasibility in determining the sports injury part.
Adhesive Bond Integrity of Experimental Zinc Oxide Nanoparticles Incorporated Dentin Adhesive: An SEM, EDX, μTBS, and Rheometric Analysis
Objective. Our study is aimed at preparing an experimental adhesive (EA) and assessing the influence of adding 5-10 wt.% concentrations of zinc oxide (ZnO) nanoparticles on the adhesive’s mechanical properties. Methods. Field emission scanning electron microscopy (FESEM) and energy dispersive X-ray (EDX) spectroscopy were employed to investigate the morphology and elemental distribution of the filler nanoparticles. To examine the adhesive properties, microtensile bond strength (μTBS) testing, an investigation of the rheological properties, degree of conversion (DC), and analysis of the interface between the adhesive and dentin were carried out. Results. The SEM micrographs of ZnO nanoparticles demonstrated spherical agglomerates. The EDX plotting confirmed the incidence of Zn and oxygen (O) in the ZnO nanoparticles. The highest μTBS was observed for nonthermocycled (NTC) 5 wt.% ZnO group ( MPa), followed by the NTC-10 wt.% ZnO group ( MPa). Most of the failures observed were adhesive in nature. A gradual reduction in the viscosity was observed at higher angular frequencies, and the addition of 5 and 10 wt.% ZnO to the composition of the EA lowered its viscosity. The 5 wt.% ZnO group demonstrated suitable dentin interaction by showing the formation of resin tags, while for the 10 wt.% ZnO group, compromised resin tag formation was detected. DC was significantly higher in the 0% ZnO (EA) group. Conclusion. The reinforcement of the EA with 5 and 10 wt.% concentrations of ZnO nanoparticles produced an improvement in the adhesive’s μTBS. However, a reduced viscosity was observed for both nanoparticle-reinforced adhesives, and a negotiated dentin interaction was seen for 10 wt.% ZnO adhesive group. Further research exploring the influence of more filler concentrations on diverse adhesive properties is recommended.
Sports Medical Image Modeling of Injury Prevention in Wushu Training
In order to solve the problem of injury prevention in Wushu training, this paper proposes a research on modeling using sports medical images. The main content of this technology research is to drive the muscle strength modeling method based on the sports medical image data. According to the acquisition of MRI/CT images, through the research and application of DFIS, it is concluded that the research on sports medical image modeling has a high accuracy for injury prevention in Wushu training. The experimental results show that translation ~0.27, ~0.63, , ~1.2, ~3.4, ~1.04, and sports medical images have a high accuracy for injury prevention in Wushu training. It is proved that the research on sports medical image modeling is effective and accurate for the problem of injury prevention in Wushu training.