Bone plays an important role in dental implant treatment success. The goal of this literature review is to analyze the influence of bone definition and finite element parameters on stress in dental implants and bone in numerical studies. A search was conducted of Pubmed, Science Direct and LILACS, and two independent reviewers performed the data extraction. The quality of the selected studies was assessed using the Cochrane Handbook tool for clinical trials. Seventeen studies were included. Titanium was the most commonly-used material in dental implants. The magnitude of the applied loads varied from 15 to 300 N with a mean of 182 N. Complete osseointegration was the most common boundary condition. Evidence from this review suggests that bone is commonly defined as an isotropic material, despite being an anisotropic tissue, and that it is analyzed as a ductile material, instead of as a fragile material. In addition, and in view of the data analyzed in this review, it can be concluded that there is no standardization for conducting finite element studies in the field of dentistry. Convergence criteria are only detailed in two of the studies included in this review, although they are a key factor in obtaining accurate results in numerical studies. It is therefore necessary to implement a methodology that indicates which parameters a numerical simulation must include, as well as how the results should be analyzed.
Dental caries is the most prevalent dental disease worldwide, and neural networks and artificial intelligence are increasingly being used in the field of dentistry. This systematic review aims to identify the state of the art of neural networks in caries detection and diagnosis. A search was conducted in PubMed, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and ScienceDirect. Data extraction was performed independently by two reviewers. The quality of the selected studies was assessed using the Cochrane Handbook tool. Thirteen studies were included. Most of the included studies employed periapical, near-infrared light transillumination, and bitewing radiography. The image databases ranged from 87 to 3000 images, with a mean of 669 images. Seven of the included studies labeled the dental caries in each image by experienced dentists. Not all of the studies detailed how caries was defined, and not all detailed the type of carious lesion detected. Each study included in this review used a different neural network and different outcome metrics. All this variability complicates the conclusions that can be made about the reliability or not of a neural network to detect and diagnose caries. A comparison between neural network and dentist results is also necessary.
Neural networks are increasingly being used in the field of dentistry. The aim of this literature review was to visualize the state of the art of artificial intelligence in dental applications, such as the detection of teeth, caries, filled teeth, crown, prosthesis, dental implants and endodontic treatment. A search was conducted in PubMed, the Institute of Electrical and Electronics Engineers (IEEE) Xplore and arXiv.org. Data extraction was performed independently by two reviewers. Eighteen studies were included. The variable teeth was the most analyzed (n = 9), followed by caries (n = 7). No studies detecting dental implants and filled teeth were found. Only two studies investigated endodontic applications. Panoramic radiographies were the most common image employed (n = 5), followed by periapical images (n = 3). Near-infrared light transillumination images were employed in two studies and bitewing and computed tomography (CT) were employed in one study. The included articles used a wide variety of neuronal networks to detect the described variables. In addition, the database used also had a great heterogeneity in the number of images. A standardized methodology should be used in order to increase the compatibility and robustness between studies because of the heterogeneity in the image database, type, neural architecture and results.
Dental radiography plays an important role in clinical diagnosis, treatment and making decisions. In recent years, efforts have been made on developing techniques to detect objects in images. The aim of this study was to detect the absence or presence of teeth using an effective convolutional neural network, which reduces calculation times and has success rates greater than 95%. A total of 8000 dental panoramic images were collected. Each image and each tooth was categorized, independently and manually, by two experts with more than three years of experience in general dentistry. The neural network used consists of two main layers: object detection and classification, which is the support of the previous one. A Matterport Mask RCNN was employed in the object detection. A ResNet (Atrous Convolution) was employed in the classification layer. The neural model achieved a total loss of 0.76% (accuracy of 99.24%). The architecture used in the present study returned an almost perfect accuracy in detecting teeth on images from different devices and different pathologies and ages.
Purpose Combination of Rivaroxaban plus Aspirin improved cardiovascular outcome in patients with stable cardiovascular disease. The aim was to determine if Rivaroxaban and acetylsalicylic acid alone or in combination may protect mitochondrial mitophagy in human coronary artery endothelial cells (HCAEC) exposed to D-glucose. Methods HCAEC were incubated under different conditions: 5 mmol/L glucose D-glucose (control), 30 mmol/L D-Glucose with and without 50 nmol/L Rivaroxaban (Rivaroxaban), 0.33 mmol/L ASA (ASA) or Rivaroxaban (12.5 nmol/L)+ASA (0.33 mmol/L; (Riva+ASA). Results HCAEC incubated with D-glucose showed an increased Factor Xa expression. The mitochondrial content of Pink-1 and Parkin were significantly reduced in high glucose-incubated HCAEC compared to control. Rivaroxaban+ASA significantly increased the mitochondrial content of Pink-1 and Parkin, and the mitochondrial membrane potential compared to D-Glucose group. Both ASA alone and Riva+ASA reduced reactive oxygen species (ROS) and tissue factor production induced by high glucose exposure. Conclusion Under high glucose condition combining Rivaroxaban+ASA increased the mitochondrial content of Pink-1 and Parkin, restored mitochondria membrane potential and reduced ROS and tissue factor expression in HCAEC. It suggests potential effects induced by dual use of Rivaroxaban and ASA on the coronary endothelium subjected to high glucose condition.
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