Search Results (36)

A digital microscope plays a crucial role in the better and faster diagnosis of an abnormality using various techniques. There has been significant development in this domain of digital pathology. Sickle cell disease (SCD) is a genetic disorder that affects hemoglobin in red blood cells. The traditional method for diagnosing sickle cell disease involves preparing a glass slide and viewing the slide using the eyepiece of a manual microscope. The entire process thus becomes very tedious and time consuming. This paper proposes a semi-automated system that can capture images based on a predefined program. It has an XY stage for moving the slide horizontally or vertically and a Z stage for focus adjustments. The case study taken here is of SCD. The proposed hardware captures SCD slides, which are further used to classify them with respect to normal. They are processed using deep learning models such as Darknet-19, ResNet50, ResNet18, ResNet101, and GoogleNet. The tested models demonstrated strong performance, with most achieving high metrics across different configurations varying with an average of around 97%. In the future, this semi-automated system will benefit pathologists and can be used in rural areas, where pathologists are in short supply. Full article

Previous studies have explored use of smart glasses in telemedicine, but no study has investigated its use in teleradiography. The purpose of this study was to implement a six-month pilot program for Western Australian X-ray operators (XROs) to use smart glasses to obtain assisted reality support in their radiography practice from their supervising radiographers, and evaluate its effectiveness in terms of XROs’ competence improvement and equipment usability. Pretest–posttest design with evaluation of the XROs’ competence (including their X-ray image quality) and smart glasses usability by XROs in two remote centers and their supervising radiographers from two sites before and after the program using four questionnaire sets and X-ray image quality review was employed in this experimental study. Paired t-test was used for comparing mean values of the pre- and post-intervention pairs of 11-point scale questionnaire and image quality review items to determine any XROs’ radiography competence improvements. Content analysis was used to analyze open questions about the equipment usability. Our study’s findings based on 13 participants (11 XROs and 2 supervising radiographers) and 2053 X-ray images show that the assisted reality support helped to improve the XROs’ radiography competence (specifically X-ray image quality), with mean post-intervention competence values of 6.16–7.39 (out of 10) and statistical significances (p < 0.001–0.05), and the equipment was considered effective for this purpose but not easy to use. Full article

(1) Background: Cementation of glass fiber posts to root canals has been associated with various failures, especially debonding. This narrative review aims to present the contemporary concepts concerning the adhesive cementation of glass fiber post and to discuss the optimal management of these factors. (2) Methods: Electronic search was performed in MEDLINE/Pub Med and Google Scholar using selected keywords examining the parameters post length, surface treatment of glass fiber posts, post space preparation and dentin pretreatment, resin cement selection, adhesive systems and hybrid layer formation, and clinical techniques. (3) Results: The search led to the selection of 44 articles. Epoxy resin-based endodontic sealers are recommended and the use of temporary cement in the root canal should be avoided. The minimum length of a glass fiber post adhesively cemented to a root canal is 5 mm. Irrigating the root canals with chlorhexidine, MTAD, or EDTA (alone or in combination with NaOCl) after post space preparation seems to enhance the bond strength. Silane application on the surface of the post seems to be beneficial. Concerning resin cements and adhesive systems, the results were rather inconclusive. Finally, resin cement should be applied inside the root canal with an elongation tip and photoactivation should be delayed. (4) Conclusions: Contemporary concepts of adhesive cementation of glass fiber posts can indeed improve the bond between glass fiber posts, resin cement, and root canal dentin, however, evidence coming from long-term randomized prospective clinical trials is needed in order to obtain safer conclusions. Full article

There is a vast amount of published literature concerning dental veneers; however, the effects of tooth preparation, aging, veneer type, and resin cement type on the failure of dental veneers in laboratory versus clinical scenarios are not clear. The purpose of the present narrative review was to determine the principal factors associated with failures of dental veneers in laboratory tests and to understand how these factors translate into clinical successes/failures. Articles were identified and screened by the lead author in January 2024 using the keywords ‘‘dental veneer”, “complication”, “survival rate”, “failure”, and “success rate” using PubMed/Medline, Scopus, Google Scholar, and Science Direct. The inclusion criteria included articles published between January 1999 and January 2024 on the topics of preparation of a tooth, aging processes of the resin cement and veneer, translucency, thickness, fabrication technique of the veneer; shade, and thickness of the resin cement. The exclusion criteria included articles that discussed marginal and internal fit, microhardness, water sorption, solubility, polishability, occlusal veneers, retention, surface treatments, and wear. The results of the present review indicated that dental veneers generally have a high survival rate (>90% for more than 10 years). The amount of preserved enamel layer plays a paramount role in the survival and success rates of veneers, and glass-ceramic veneers with minimal/no preparation showed the highest survival rates. Fracture was the primary failure mechanism associated with decreased survival rate, followed by debonding and color change. Fractures increased in the presence of parafunctional activities. Fewer endodontic complications were associated with veneer restorations. No difference was observed between the maxillary and mandibular teeth. Clinical significance: Fractures can be reduced by evaluation of occlusion immediately after cementation and through the use of high-strength veneer materials, resin cements with low moduli, and thin layers of highly polished veneers. Debonding failures can be reduced with minimal/no preparation, and immediate dentin sealing should be considered when dentin is exposed. Debonding can also be reduced by preventing contamination from blood, saliva, handpiece oil, or fluoride-containing polishing paste; through proper surface treatment (20 s of hydrofluoric acid etching for glass ceramic followed by silane for 60 s); and through use of light-cured polymerization for thin veneers. Long-term color stability may be maintained using resin cements with UDMA-based resin, glass ceramic materials, and light-cure polymerization with thin veneers. Full article

In recent decades, technological advancements have transformed the industry, highlighting the efficiency of automation and safety. The integration of augmented reality (AR) and gesture recognition has emerged as an innovative approach to create interactive environments for industrial equipment. Gesture recognition enhances AR applications by allowing intuitive interactions. This study presents a web-based architecture for the integration of AR and gesture recognition, designed to interact with industrial equipment. Emphasizing hardware-agnostic compatibility, the proposed structure offers an intuitive interaction with equipment control systems through natural gestures. Experimental validation, conducted using Google Glass, demonstrated the practical viability and potential of this approach in industrial operations. The development focused on optimizing the system’s software and implementing techniques such as normalization, clamping, conversion, and filtering to achieve accurate and reliable gesture recognition under different usage conditions. The proposed approach promotes safer and more efficient industrial operations, contributing to research in AR and gesture recognition. Future work will include improving the gesture recognition accuracy, exploring alternative gestures, and expanding the platform integration to improve the user experience. Full article

(1) Background: The compound ytterbium trifluoride is used as a component of several dental materials, and this is reviewed in the current article. (2) Methods: Published articles on this substance were identified initially from PubMed, and then from Science Direct and Google Scholar. The publications identified in this way showed that ytterbium trifluoride has been included in a variety of dental restorative materials, including composite resins, glass polyalkenoate cements, and calcium trisilicate cements. (3) Results: Ytterbium trifluoride is reported to be insoluble in water. Despite this, its presence is associated with fluoride release from dental materials. There is evidence that it reacts with the components of calcium trisilicate cements to form small amounts of a variety of compounds, including ytterbium oxide, Yb₂O₃, and calcium–ytterbium fluoride, CaYbF₅. In nanoparticulate form, it has been shown to reinforce glass polyalkenoates and it also provides high contrast in X-ray images. (4) Conclusions: Ytterbium trifluoride is a useful component of dental materials, though some of the published findings suggest that there are aspects of its chemistry which are poorly understood. Full article

Composite resin is universally used for posterior teeth restorations. Fibers have been suggested for the mechanical improvement of the restorations. This study assessed the fracture resistance of class II fiber-reinforced composite restorations and compared it with the fracture resistance of three control groups: (1) healthy teeth, (2) non-fiber-reinforced restorations and (3) unrestored cavities. A search was performed using PubMed, Web of Science and Google Scholar from 15 May to 12 June 2023. Only in vitro studies from the last 10 years were included for this systematic analysis. This study was registered in the PROSPERO database, it followed PRISMA guidelines and the risk of bias was assessed using the QUIN tool. Fracture resistance median values, in Newtons (N), were calculated for the experimental and control groups (95% confidence interval). For pairwise comparison, nonparametric tests (p < 0.05) were applied. Twenty-four in vitro studies met the inclusion criteria. The fracture resistance of the experimental group was 976.0 N and differed (p < 0.05) from all controls. The experimental group showed lower values of fracture resistance than healthy teeth (1459.9 N; p = 0.048) but higher values than non-fiber-reinforced restorations (771.0 N; p = 0.008) and unrestored cavities (386.6 N; p < 0.001). In vitro systematic outcomes evidenced that glass and/or polyethylene fibers improved the fracture resistance of composite restorations. Full article

Dental cements are in a constant state of evolution, adapting to better align with the intricacies of tooth structure and the dynamic movements within the oral cavity. This study aims to evaluate the efficacy of zirconia-reinforced glass ionomer cement—an innovative variant of modified glass ionomer cements—in terms of its ability to withstand compressive forces and prevent microleakage during dental caries reconstruction. An extensive search was conducted across various databases, encompassing PubMed-MEDLINE, Scopus, Embase, Google Scholar, prominent journals, unpublished studies, conference proceedings, and cross-referenced sources. The selected studies underwent meticulous scrutiny according to predetermined criteria, followed by the assessment of quality and the determination of evidence levels. In total, 16 studies were incorporated into this systematic review and network meta-analysis (NMA). The findings suggest that both compomer and giomer cements exhibit greater compressive strength and reduced microleakage values than zirconia-reinforced glass ionomer cement. In contrast, resin-modified glass ionomer cement (RMGIC) and high-viscosity glass ionomer cement (GIC) demonstrate less favorable performance in these regards when compared with zirconia-reinforced glass ionomer cement. Full article

Waste disposal remains a challenge due to land availability, and environmental and health issues related to the main disposal method, landfilling. Combining computer vision (machine learning) and robotics to sort waste is a cost-effective solution for landfilling activities limitation. The objective of this study was to combine transfer and ensemble learning to process collected waste images and classify landfill waste into nine classes. Pretrained CNN models (Inception–ResNet-v2, EfficientNetb3, and DenseNet201) were used as base models to develop the ensemble network, and three other single CNN models (Models 1, 2, and 3). The single network performances were compared to the ensemble model. The waste dataset, initially grouped in two classes, was obtained from Kaggle, and reorganized into nine classes. Classes with a low number of data were improved by downloading additional images from Google search. The Ensemble Model showed the highest prediction precision (90%) compared to the precision of Models 1, 2, and 3, 86%, 87%, and 88%, respectively. All models had difficulties predicting overlapping classes, such as glass and plastics, and wood and paper/cardboard. The environmental costs for the Ensemble network, and Models 2 and 3, approximately 15 g CO₂ equivalent per training, were lower than the 19.23 g CO₂ equivalent per training for Model 1. Full article

The introduction of solid-phase imprinting has had a significant impact in the molecular imprinting field, mainly due to its advantage of orienting the template immobilisation, affinity separation of nanoMIPs and faster production time. To date, more than 600 documents on Google Scholar involve solid-phase synthesis, mostly relying on silanes mediating template immobilisation on the solid phase. Organosilanes are the most explored functionalisation compounds due to their straightforward use and ability to promote the binding of organic molecules to inorganic substrates. However, they also suffer from well-known issues, such as lack of control in the layer’s deposition and poor stability in water. Since the first introduction of solid-phase imprinting, few efforts have been made to overcome these limitations. The work presented in this research focuses on optimising the silane stability on glass beads (GBs) and iron oxide nanoparticles (IO-NPs), to subsequently function as solid phases for imprinting. The performance of three different aminosilanes were investigated; N-(6-aminohexyl) aminomethyltriethoxy silane (AHAMTES), 3-Aminopropyltriethoxysilane (APTES), and N-(2-aminoethyl)-3-aminopropyltriethoxysilane (AEAPTES), as well as studying the effect of dipodal silane bis(triethoxysilyl)ethane (BTSE). A stable solid phase was consequently achieved with 3% v/v AEAPTES and 2.4% BTSE, providing an upgraded protocol from Canfarotta et al. for the silanisation of the solid phase for molecular imprinting purposes. Full article

Head-mounted displays (HMDs) have the potential to greatly impact the surgical field by maintaining sterile conditions in healthcare environments. Google Glass (GG) and Microsoft HoloLens (MH) are examples of optical HMDs. In this comparative survey related to wearable augmented reality (AR) technology in the medical field, we examine the current developments in wearable AR technology, as well as the medical aspects, with a specific emphasis on smart glasses and HoloLens. The authors searched recent articles (between 2017 and 2022) in the PubMed, Web of Science, Scopus, and ScienceDirect databases and a total of 37 relevant studies were considered for this analysis. The selected studies were divided into two main groups; 15 of the studies (around 41%) focused on smart glasses (e.g., Google Glass) and 22 (59%) focused on Microsoft HoloLens. Google Glass was used in various surgical specialities and preoperative settings, namely dermatology visits and nursing skill training. Moreover, Microsoft HoloLens was used in telepresence applications and holographic navigation of shoulder and gait impairment rehabilitation, among others. However, some limitations were associated with their use, such as low battery life, limited memory size, and possible ocular pain. Promising results were obtained by different studies regarding the feasibility, usability, and acceptability of using both Google Glass and Microsoft HoloLens in patient-centric settings as well as medical education and training. Further work and development of rigorous research designs are required to evaluate the efficacy and cost-effectiveness of wearable AR devices in the future. Full article

Background: The usage of whole-slide images has recently been gaining a foothold in medical education, training, and diagnosis. Objectives: The first objective of the current study was to compare academic performance on virtual microscopy (VM) and light microscopy (LM) for learning pathology, anatomy, and histology in medical and dental students during the COVID-19 period. The second objective was to gather insight into various applications and usage of such technology for medical education. Materials and methods: Using the keywords “virtual microscopy” or “light microscopy” or “digital microscopy” and “medical” and “dental” students, databases (PubMed, Embase, Scopus, Cochrane, CINAHL, and Google Scholar) were searched. Hand searching and snowballing were also employed for article searching. After extracting the relevant data based on inclusion and execution criteria, the qualitative data were used for the systematic review and quantitative data were used for meta-analysis. The Newcastle Ottawa Scale (NOS) scale was used to assess the quality of the included studies. Additionally, we registered our systematic review protocol in the prospective register of systematic reviews (PROSPERO) with registration number CRD42020205583. Results: A total of 39 studies met the criteria to be included in the systematic review. Overall, results indicated a preference for this technology and better academic scores. Qualitative analyses reported improved academic scores, ease of use, and enhanced collaboration amongst students as the top advantages, whereas technical issues were a disadvantage. The performance comparison of virtual versus light microscopy meta-analysis included 19 studies. Most (10/39) studies were from medical universities in the USA. VM was mainly used for teaching pathology courses (25/39) at medical schools (30/39). Dental schools (10/39) have also reported using VM for teaching microscopy. The COVID-19 pandemic was responsible for the transition to VM use in 17/39 studies. The pooled effect size of 19 studies significantly demonstrated higher exam performance (SMD: 1.36 [95% CI: 0.75, 1.96], p < 0.001) among the students who used VM for their learning. Students in the VM group demonstrated significantly higher exam performance than LM in pathology (SMD: 0.85 [95% CI: 0.26, 1.44], p < 0.01) and histopathology (SMD: 1.25 [95% CI: 0.71, 1.78], p < 0.001). For histology (SMD: 1.67 [95% CI: −0.05, 3.40], p = 0.06), the result was insignificant. The overall analysis of 15 studies assessing exam performance showed significantly higher performance for both medical (SMD: 1.42 [95% CI: 0.59, 2.25], p < 0.001) and dental students (SMD: 0.58 [95% CI: 0.58, 0.79], p < 0.001). Conclusions: The results of qualitative and quantitative analyses show that VM technology and digitization of glass slides enhance the teaching and learning of microscopic aspects of disease. Additionally, the COVID-19 global health crisis has produced many challenges to overcome from a macroscopic to microscopic scale, for which modern virtual technology is the solution. Therefore, medical educators worldwide should incorporate newer teaching technologies in the curriculum for the success of the coming generation of health-care professionals. Full article

In recent years, the number of studies using whole-slide imaging (WSIs) of histopathology slides has expanded significantly. For the development and validation of artificial intelligence (AI) systems, glass slides from retrospective cohorts including patient follow-up data have been digitized. It has become crucial to determine that the quality of such resources meets the minimum requirements for the development of AI in the future. The need for automated quality control is one of the obstacles preventing the clinical implementation of digital pathology work processes. As a consequence of the inaccuracy of scanners in determining the focus of the image, the resulting visual blur can render the scanned slide useless. Moreover, when scanned at a resolution of 20× or higher, the resulting picture size of a scanned slide is often enormous. Therefore, for digital pathology to be clinically relevant, computational algorithms must be used to rapidly and reliably measure the picture’s focus quality and decide if an image requires re-scanning. We propose a metric for evaluating the quality of digital pathology images that uses a sum of even-derivative filter bases to generate a human visual-system-like kernel, which is described as the inverse of the lens’ point spread function. This kernel is then used for a digital pathology image to change high-frequency image data degraded by the scanner’s optics and assess the patch-level focus quality. Through several studies, we demonstrate that our technique correlates with ground-truth z-level data better than previous methods, and is computationally efficient. Using deep learning techniques, our suggested system is able to identify positive and negative cancer cells in images. We further expand our technique to create a local slide-level focus quality heatmap, which can be utilized for automated slide quality control, and we illustrate our method’s value in clinical scan quality control by comparing it to subjective slide quality ratings. The proposed method, GoogleNet, VGGNet, and ResNet had accuracy values of 98.5%, 94.5%, 94.00%, and 95.00% respectively. Full article

This study aimed to describe different staining protocols for the main dental ceramics. A bibliographic search was conducted in the main health databases PubMed and Scholar Google, in which 100 studies published were collected. In vitro and in silico studies, case reports, and systematic and literature reviews, on ceramic materials, were included. Therefore, articles that did not deal with the topic addressed were excluded. Ceramics can be classified into glass-matrix ceramics (etchable), polycrystalline (non-etchable), and hybrid ceramics. In this context, different fabrication methods, method indications, and characterization layers can be used for each ceramic group and numerous protocols differ according to the choice of material. Several ceramic systems are available, thus professionals in the prosthetic area need constant updates on dental ceramic restorations and their proper characterizations. Full article

In this paper, a microscopic oil displacement visualization experiment based on the glass etching model to simulate the tight oil reservoir of underground rocks is carried out. At present, water flooding technology is widely used in the development of oil and gas fields, and the remaining oil content is still very high after water flooding. It is the key to improving oil recovery to identify and study the remaining oil form distribution after water flooding. The experiment result shows there are five types of residual oil after water flooding: columnar residual oil, membranous residual oil, oil droplet residual oil, blind terminal residual oil and cluster residual oil. A convolution neural network is suitable for complex image characteristics with good robustness. In recent years, it has made a breakthrough in a set of small and efficient neural networks with SqueezeNet, Google Inception and the flattened network method put forward. In order to solve the problems of low automation, low efficiency and high error rate in the traditional remaining oil form recognition algorithm after water flooding in tight oil reservoirs, an image recognition algorithm based on the MobileNets convolutional neural network model was proposed in this paper to achieve accurate recognition of the remaining oil form. Based on traditional image processing methods which, respectively, extracted the whole picture of the different types of remaining oil in the image block, it uses the MobileNets network structure to classify different types of image block and realizes the layered depth convolution neural network system. The experiment result shows that the model can accurately identify the remaining oil forms, and the overall recognition accuracy is up to 83.8% after the convergence of the network model, which infinitely identifies the remaining oil forms in the morphological library, proving the strong generalization and robustness of the model. Full article