Outcome of teeth restored with CAD/CAM zirconium dioxide post-cores: a retrospective study with a follow-up period of 3-6 years.

STATEMENT OF PROBLEM: Computer aided design/computer aided manufacturing (CAD/CAM) zirconia post-cores is one of the options of post crown restoration materials due to their esthetic properties and superior mechanical strength. However, the clinical effect on aesthetics and strength properties is unclear due to the lack of results of their long-term follow-up. PURPOSE: This retrospective clinical study aims to analyze the survival rate, clinical manifestations, and failure factors after CAD/CAM zirconia post-core restoration. MATERIAL AND METHODS: Clinical and radiographic examinations were performed on 342 patients with 400 teeth for 3-6 years postsurgical follow-up examination. The patients were all received CAD/CAM zirconia post-cores and all-ceramic crowns at the Department of Prosthodontics in the public hospital. The retrospective outcomes were conducted after zirconia post restoration, including survival rate by Kaplan-Meier analysis and findings of manifestations and failure factors. The effects of gender and dental position on survival rate were analyzed by Cox-Mantel Test. RESULTS: This study retrospectively evaluated 261 teeth from 229 patients with a 35% drop-out rate. The survival rate was 96.0%, and the success rate was 92.4%. According to the tooth position classification, the survival rate was 100% for 101 anterior teeth, 95.4% for 69 premolars, and 88.3% for 91 molars. According to gender, the survival rate of the male group was 92.3%, while that of the female group was 98.0%, with a significant difference (P < 0.01). The complications included crown fracture (1.9%) periapical inflammation (1.9%), crown debonding (1.1%), percussion abnormal (1.9%) and root fracture (0.8%). CONCLUSIONS: Within the limitations of this retrospective study, it can be concluded that CAD/CAM zirconia post-cores are clinically promising. Compared with the posterior teeth, CAD/CAM zirconia post-cores are more suitable for anterior teeth.

Deep learning in the overall process of implant prosthodontics: A state-of-the-art review.

Artificial intelligence represented by deep learning has attracted attention in the field of dental implant restoration. It is widely used in surgical image analysis, implant plan design, prosthesis shape design, and prognosis judgment. This article mainly describes the research progress of deep learning in the whole process of dental implant prosthodontics. It analyzes the limitations of current research, and looks forward to the future development direction.

Performance comparison of multifarious deep networks on caries detection with tooth X-ray images.

OBJECTIVES: Deep networks have been preliminarily studied in caries diagnosis based on clinical X-ray images. However, the performance of different deep networks on caries detection is still unclear. This study aims to comprehensively compare the caries detection performances of recent multifarious deep networks with clinical dentist level as a bridge. METHODS: Based on the self-collected periapical radiograph dataset in clinic, four most popular deep networks in two types, namely YOLOv5 and DETR object detection networks, and UNet and Trans-UNet segmentation networks, were included in the comparison study. Five dentists carried out the caries detection on the same testing dataset for reference. Key tooth-level metrics, including precision, sensitivity, specificity, F1-score and Youden index, were obtained, based on which statistical analysis was conducted. RESULTS: The F1-score order of deep networks is YOLOv5 (0.87), Trans-UNet (0.86), DETR (0.82) and UNet (0.80) in caries detection. A same ranking order is found using the Youden index combining sensitivity and specificity, which are 0.76, 0.73, 0.69 and 0.64 respectively. A moderate level of concordance was observed between all networks and the gold standard. No significant difference (p > 0.05) was found between deep networks and between the well-trained network and dentists in caries detection. CONCLUSIONS: Among investigated deep networks, YOLOv5 is recommended to be priority for caries detection in terms of its high metrics. The well-trained deep network could be used as a good assistance for dentists to detect and diagnose caries. CLINICAL SIGNIFICANCE: The well-trained deep network shows a promising potential clinical application prospect. It can provide valuable support to healthcare professionals in facilitating detection and diagnosis of dental caries.

Numerical hemolysis performance evaluation of a rotary blood pump under different speed modulation profiles.

Introduction: Speed modulation methods have been studied and even used clinically to create extra pulsation in the blood circulatory system with the assistance of a continuous flow rotary blood pump. However, fast speed variations may also increase the hemolysis potential inside the pump. Methods: This study investigates the hemolysis performance of a ventricular assist rotary blood pump under sinusoidal, square, and triangular wave speed modulation profiles using the computational fluid dynamics (CFD) method. The CFD boundary pressure conditions of the blood pump were obtained by combining simulations with the pump's mathematical model and a complete cardiovascular lumped parameter model. The hemolysis performance of the blood pump was quantified by the hemolysis index (HI) calculated from a Eulerian scalar transport equation. Results: The HI results were obtained and compared with a constant speed condition when the blood pump was run under three speed profiles. The speed modulations were revealed to slightly affect the pump hemolysis, and the hemolysis differences between the different speed modulation profiles were insignificant. Discussion: This study suggests that speed modulations could be a feasible way to improve the flow pulsatility of rotary blood pumps while not increasing the hemolysis performance.

Caries segmentation on tooth X-ray images with a deep network.

OBJECTIVES: Deep learning has been a promising technology in many biomedical applications. In this study, a deep network was proposed aiming for caries segmentation on the clinically collected tooth X-ray images. METHODS: The proposed network inherited the skip connection characteristic from the widely used U-shaped network, and creatively adopted vision Transformer, dilated convolution, and feature pyramid fusion methods to enhance the multi-scale and global feature extraction capability. It was then trained on the clinically self-collected and augmented tooth X-ray image dataset, and the dice similarity and pixel classification precision were calculated for the network's performance evaluation. RESULTS: Experimental results revealed an average dice similarity of 0.7487 and an average pixel classification precision of 0.7443 on the test dataset, which outperformed the compared networks such as UNet, Trans-UNet, and Swin-UNet, demonstrating the remarkable improvement of the proposed network. CONCLUSIONS: This study contributed to the automatic caries segmentation by using a deep network, and highlighted the potential clinical utility value.

On-line parameter identification of the lumped arterial system model: A simulation study.

A lumped model of the arterial system has been used in constructing a hybrid mock loop due to its real-time response. However, the parameters of the model are always from a general case and not adapted to a specific patient. In this study, we focused on on-line parameter identification of the lumped model of the arterial system that could be used for a specific patient. A five-element lumped arterial model is adopted in this study, in which the five parameters are to be determined. The aortic flow rate and the venous pressure are chosen as the inputs of the model, and aortic pressure as the output. An iterative optimization based on the established state space equations of the five-element model is used to seek the best parameter values by minimizing the difference between the model prediction and the previously obtained aortic pressure. The method is validated using simulated data from a complete numerical cardiovascular model. Results show that the method can track the dynamic variation of the parameters very well. Finally, a sensitivity analysis of the model parameters is conducted to interpret the effect of parameter changes. The good performance of the identification demonstrates the potential application of this method to customize a hybrid mock loop for a specific patient or clinically monitor the arterial vessel characteristics in real time.