Influence of fiber insertion and different material type on stress distribution in endocrown restorations: a 3D-FEA study

How do different intraoral scanners and milling machines affect the fit and fatigue behavior of lithium disilicate and resin composite endocrowns?

The aim of this in vitro study was to evaluate the effect of the combinations of two different intraoral scanners (IOS), two milling machines, and two restorative materials on the marginal/internal fit and fatigue behavior of endocrowns produced by CAD-CAM. Eight groups (n= 10) were considered through the combination of TRIOS 3 (TR) or Primescan (PS) IOS; 4-axes (CR; CEREC MC XL) or 5-axes (PM; PrograMill PM7) milling machines; and lithium disilicate (LD; IPS e.max CAD) or resin composite (RC; Tetric CAD) restorative materials. Specific surface treatments were applied to each material, and the bonding to its corresponding Endocrown-shaped fiberglass-reinforced epoxy resin preparations was performed (Variolink Esthetic DC). Computed microtomography (μCT) was performed to assess the marginal/internal fit, as well as a mechanical fatigue test (20 Hz, initial load = 100 N/5000 cycles; step-size = 50 N/10,000 cycles until a threshold of 1500 N, then, the step-size was increased if needed to 100 N/10,000 cycles until failure or a threshold of 2800 N) to evaluate the restorations long-term behavior. Complementary analysis of the fracture features and surface topography in scanning electron microscopy was performed. Three-way ANOVA and Kaplan-Meier test (α = 0.05) were performed for marginal/internal fit, and fatigue behavior data, respectively. PS scanner, CR milling machine, and RC endocrowns resulted in a better marginal fit compared to their counterparts. Still, the PM machine resulted in a better pulpal space fit compared to the CR milling machine. Regardless of the scanner and milling machine, RC endocrowns exhibited superior fatigue behavior than LD ones. LD endocrowns presented margin chipping regardless of the milling machine used. Despite minor differences in terms of fit, the 'IOS' and 'milling machine' factors did not impair the fatigue behavior of endocrowns. Resin-composite restorations resulted in a higher survival rate compared to glass-ceramic ones, independently of the digital devices used in the workflow.

Biomechanical performance of resin composite on dental tissue restoration: A finite element analysis

This study investigates the biomechanical performance of various dental materials when filled in different cavity designs and their effects on surrounding dental tissues. Finite element models of three infected teeth with different cavity designs, Class I (occlusal), Class II mesial-occlusal (MO), and Class II mesio-occluso-distal (MOD) were constructed. These cavities were filled with amalgam, composites (Young's moduli of 10, 14, 18, 22, and 26 GPa), and glass carbomer cement (GCC). An occlusal load of 600 N was distributed on the top surface of the teeth to carry out simulations. The findings revealed that von Mises stress was higher in GCC material, with cavity Class I (46.01 MPa in the enamel, 23.61 MPa in the dentin), and for cavity Class II MO von Mises stress was 43.64 MPa, 39.18 MPa in enamel and dentin respectively, while in case of cavity Class II MOD von Mises stress was 44.67 MPa in enamel, 27.5 in the dentin. The results showed that higher stresses were generated in the non-restored tooth compared to the restored one, and increasing Young's modulus of restorative composite material decreases stresses in enamel and dentin. The use of composite material showed excellent performance which can be a good viable option for restorative material compared to other restorative materials.

Present status and future directions: The restoration of root filled teeth

This narrative review will focus on a number of contemporary considerations relating to the restoration of root filled teeth and future directions for research. Clinicians are now more than ever, aware of the interdependence of the endodontic and restorative aspects of managing root filled teeth, and how these aspects of treatment are fundamental to obtaining the best long-term survival. To obtain the optimal outcomes for patients, clinicians carrying out endodontic treatment should have a vested interest in the restorative phase of the treatment process, as well as an appreciation for the structural and biomechanical effects of endodontic-restorative procedures on restoration and tooth longevity. Furthermore, the currently available research, largely lacks appreciation of occlusal factors in the longevity of root filled teeth, despite surrogate outcomes demonstrating the considerable influence this variable has. Controversies regarding the clinical relevance of minimally invasive endodontic and restorative concepts are largely unanswered with respect to clinical data, and it is therefore, all too easy to dismiss these ideas due to the lack of scientific evidence. However, conceptually, minimally invasive endodontic-restorative philosophies appear to be valid, and therefore, in the pursuit of improved clinical outcomes, it is important that the efficacies of these treatment protocols are determined. Alongside an increased awareness of the preservation of tooth structure, developments in adhesive bonding, ceramic materials and the inevitable integration of digital dentistry, there is also a need to evaluate the efficacy of new treatment philosophies and techniques with well-designed prospective clinical studies.

FN-OCT: Disease Detection Algorithm for Retinal Optical Coherence Tomography Based on a Fusion Network

Optical coherence tomography (OCT) is a new type of tomography that has experienced rapid development and potential in recent years. It is playing an increasingly important role in retinopathy diagnoses. At present, due to the uneven distributions of medical resources in various regions, the uneven proficiency levels of doctors in grassroots and remote areas, and the development needs of rare disease diagnosis and precision medicine, artificial intelligence technology based on deep learning can provide fast, accurate, and effective solutions for the recognition and diagnosis of retinal OCT images. To prevent vision damage and blindness caused by the delayed discovery of retinopathy, a fusion network (FN)-based retinal OCT classification algorithm (FN-OCT) is proposed in this paper to improve upon the adaptability and accuracy of traditional classification algorithms. The InceptionV3, Inception-ResNet, and Xception deep learning algorithms are used as base classifiers, a convolutional block attention mechanism (CBAM) is added after each base classifier, and three different fusion strategies are used to merge the prediction results of the base classifiers to output the final prediction results (choroidal neovascularization (CNV), diabetic macular oedema (DME), drusen, normal). The results show that in a classification problem involving the UCSD common retinal OCT dataset (108,312 OCT images from 4,686 patients), compared with that of the InceptionV3 network model, the prediction accuracy of FN-OCT is improved by 5.3% (accuracy = 98.7%, area under the curve (AUC) = 99.1%). The predictive accuracy and AUC achieved on an external dataset for the classification of retinal OCT diseases are 92 and 94.5%, respectively, and gradient-weighted class activation mapping (Grad-CAM) is used as a visualization tool to verify the effectiveness of the proposed FNs. This finding indicates that the developed fusion algorithm can significantly improve the performance of classifiers while providing a powerful tool and theoretical support for assisting with the diagnosis of retinal OCT.