Department of Child and Adolescent Oral Health, Federal University of Minas Gerais - UFMG, Av. Antônio Carlos, 6627 - Pampulha, Belo Horizonte, MG 31270-901, Brazil. Electronic address:
Published: December 2020
AI Article Synopsis
Article Abstract
Aim: To test a chemical-mechanical method and antimicrobial photodynamic therapy (a-PDT) in combination to manage a deep dental carious lesion in a permanent molar pediatric patient.
Background: Due to specific characteristics of early-erupted teeth, as first permanent lower molars, techniques that provide less invasive tissue removal aiming keep healthy tooth parts, such as pulp tissue, are desired. Furthermore, especially to children, provide comfort, less noise and vibration are important issues that should be considered on the management of deep caries.
Case Description: The following case report presents an association of Papacarie Duo® (PD) and a-PDT used in a 9-year-old patient with a deep dental caries in the mandibular right first molar. After PD application, the carious tissue was carefully removed with the cavity stained for 1 min with rose bengal solution, irradiated with a high intensity LED constituting the a-PDT technique, and definitively restored with composite resin. At 6 months of follow-up, no signs of caries were verified, showing success to applied techniques. A microbiological evaluation attested the antimicrobial effects of the alternative procedures.
Conclusion: Although it reached optimal clinical and bacterial reduction outcomes, the approach demonstrated an excessive length of time taken for the management of carious lesion.
Clinical Significance: The use of new technologies should be encouraged, especially when treating deep caries aiming for a less invasive approach.
Background: The automated segmentation of individual teeth from 3D models of the human dental arch is challenging due to variations in tooth alignment, arch form and overall maxillofacial anatomy. Domain adaptation is a specialised technique in deep learning which allows models to adapt to data from different domains, such as varying tooth and dental arch forms, without requiring human annotations.
Purpose: This study aimed to segment individual teeth from various dental arch morphologies in 3D intraoral scans using domain adaptation.
Both the global and European energy sectors have been undergoing a deep transition for several years, associated with a reduction in the overall share of conventional coal-based energy in favor of new technologies, especially energy from renewable sources (RES). This transition is moving from centralized production towards distributed technologies and from providing only energy to end users towards combining innovative products and services with it. Electricity consumers are becoming prosumers connected to the grid and generating an increasing amount of energy.
Objective: Previous studies have reported that the noise generated by dental equipment can interfere with the auscultation of respiratory sounds during sedation. Therefore, this study aimed to identify whether positing the acoustic sensor on the chest or cervical position would be least susceptible to interference from dental suction device noise, a prominent noise noted during respiratory sound monitoring during dental sedation.
Methods: This prospective cohort study was conducted with 30 students.
Background: The sensitivity of reverse-transcription polymerase chain reaction (RT-PCR) is limited for diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Chest computed tomography (CT) is reported to have high sensitivity; however, given the limited availability of chest CT during a pandemic, the assessment of more readily available imaging, such as chest radiographs, augmented by artificial intelligence may substitute for the detection of the features of coronavirus disease 2019 (COVID-19) pneumonia.
Methods: We trained a deep convolutional neural network to detect SARS-CoV-2 pneumonia using publicly available chest radiography imaging data including 8,851 normal, 6,045 pneumonia, and 200 COVID-19 pneumonia radiographs.
This study introduces a novel deep learning approach for 3D teeth scan segmentation and labeling, designed to enhance accuracy in computer-aided design (CAD) systems. Our method is organized into three key stages: coarse localization, fine teeth segmentation, and labeling. In the teeth localization stage, we employ a Mask-RCNN model to detect teeth in a rendered three-channel 2D representation of the input scan.
