Are Panoramic Images a Good Tool to Detect Calcified Carotid Atheroma? A Systematic Review.

To investigate the reliability of panoramic dental images to detect calcified carotid atheroma, electronic databases (PubMed, IEEE/Xplore and Embase) were searched. Outcomes included cerebrovascular disease events, cardiovascular disease events, patient previous diseases, and combined endpoints. Risk of bias was evaluated using the Newcastle-Ottawa Scale. Hence, 15 studies were selected from 507 potential manuscripts. Five studies had a low risk of bias, while the remaining nine studies were found to have a moderate risk. Heterogeneous results were obtained but showed that patients with risk factors, such as obesity, diabetes mellitus, hypertension, and smoking, and with calcified carotid atheroma on panoramic images, have a higher prevalence than healthy patients. The evidence in the literature was found to be equivocal. However, the findings of this systematic review exhibit that panoramic radiographs can be used for dental diagnosis and treatment planning, as well as to detect calcified carotid artery atheroma.

A Convolutional Neural Network for Automatic Tooth Numbering in Panoramic Images.

Analysis of dental radiographs and images is an important and common part of the diagnostic process in daily clinical practice. During the diagnostic process, the dentist must interpret, among others, tooth numbering. This study is aimed at proposing a convolutional neural network (CNN) that performs this task automatically for panoramic radiographs. A total of 8,000 panoramic images were categorized by two experts with more than three years of experience in general dentistry. The neural network consists of two main layers: object detection and classification, which is the support of the previous one and a transfer learning to improve computing time and precision. A Matterport Mask RCNN was employed in the object detection. A ResNet101 was employed in the classification layer. The neural model achieved a total loss of 6.17% (accuracy of 93.83%). The architecture of the model achieved an accuracy of 99.24% in tooth detection and 93.83% in numbering teeth with different oral health conditions.

A Validation Employing Convolutional Neural Network for the Radiographic Detection of Absence or Presence of Teeth.

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.

Dental Caries Diagnosis and Detection Using Neural Networks: A Systematic Review.

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.

Finite element analysis comparing WaveOne, WaveOne Gold, Reciproc and Reciproc Blue responses with bending and torsion tests.

To evaluate the bending and torsional properties of four nickel-titanium endodontic files, we simulated and compared WaveOne® primary size 25 with 0.07 taper, WaveOne Gold® primary size 25 with 0.07 taper, Reciproc® primary size 25 with 0.08 taper, and Reciproc Blue® primary size 25 with 0.08 taper. Three-dimensional models were created using computer-aided design software and numerically analyzed in ANSYS® Workbench. Boundary conditions for the numerical analyses were based on the ISO 3630-1 specifications. The highest stress levels were recorded for WaveOne® and Reciproc®. Numerical results of the bending test showed that WaveOne Gold® is 86% more flexible than WaveOne® with a deflection of 3 mm. Reciproc Blue® was 42.31% more flexible than Reciproc® file with a deflection of 3 mm. The WaveOne® instrument withstood the highest stress under the torsion test, followed by Reciproc®, then Reciproc Blue® files. The stress under torsion in the WaveOne® and WaveOne Gold® files is reduced in a 51%. Regarding Reciproc® and Reciproc Blue® files, the stress under torsional moments remains very similar. Our results exposed a considerable difference in terms of stress tolerance between WaveOne® and WaveOne Gold®. However, Reciproc® files demonstrated a similar stress distribution. The results obtained through finite element analysis suggest that thermal treatment of files might improve their flexibility, increasing resistance during the preparation of highly curved canals. Also, the values obtained regarding the improvement of flexibility were in accordance with the manufacturer claims.

Five-year results of implants inserted into freeze-dried block allografts.

PURPOSE: The purpose of this study was to evaluate the outcome after 5 years of allografts as bone growth material and success of implants inserted in fresh-frozen allograft bone. MATERIALS AND METHODS: A total of 20 patients were operated and 41 onlay block freeze-dried allografts (calvarial and iliac crest) were inserted for bone augmentation. A total of 64 dental implants were performed in a two-stage procedure. Four patients had total edentulism and 16 had partial edentulism. RESULTS: A total of 41 onlay block allografts were used to augment atrophic maxillae and mandibles in 20 patients. In five cases, there were horizontal and vertical augmentations. Exposure of bone allograft occurred in three cases during the first 6 weeks; all of them located in the posterior area. There were no postoperative effects in any of the cases. Fracture was observed in one case, the remaining bone was adequate to place the implants. Sixty-two dental implants were placed at second stage surgery. There was no loss of implants. CONCLUSIONS: The use of fresh-frozen allogenic bone blocks can be considered as being reliable for reconstruction of maxillomandibular defects.