Anticuerpos monoclonales como terapéutica potente en odontología.

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Biomechanical Behavior of Bioactive Material in Dental Implant: A Three-Dimensional Finite Element Analysis

Dental implants are widely accepted for the rehabilitation of missing teeth due to their aesthetic compliance, functional ability, and great survival rate. The various components in implant design like thread design, thread angle, pitch, and material used for manufacturing play a critical role in its success. Understanding these influencing factors and implementing them properly in implant design can reduce cases of potential implant failure. Recently, finite element analysis (FEA) is being widely used in the field of health sciences to solve problems in designing medical devices. It provides valid and accurate assessment in the clinical and in vitro analysis. Hence, this study was conducted to evaluate the impact of thread design of the implant and 3 different bioactive materials, titanium alloy, graphene, and reduced graphene oxide (rGO) on stress, strain, and deformation in the implant system using FEA. In this study, the FEA model of the bones and the tissues are modeled as homogeneous, isotropic, and linearly elastic material with a titanium implant system with an assumption of it 100% osseointegrated into the bone. The titanium was functionalized with graphene and graphene oxide. A modeling software tool Catia® and Ansys Workbench® is used to perform the analysis and evaluate the von Mises stress distribution, strain, and deformation at the implant and implant-cortical bone interface. The results showed that the titanium implant with a surface coating of graphene oxide exhibited better mechanical behavior than graphene, with mean von Mises stress of 39.64 MPa in pitch 1, 23.65 MPa in pitch 2, and 37.23 MPa in pitch 3. It also revealed that functionalizing the titanium implant will help in reducing the stress at the implant system. Overall, the study emphasizes the use of FEA analysis methods in solving various biomechanical issues about medical and dental devices, which can further open up for invivo study and their practical uses

Artificial neural network for gender determination using mandibular morphometric parameters: A comparative retrospective study

Gender determination is of paramount importance in order to identify the diseased in cases of mass disasters and accidents and to resolve all medico-legal issues in cases of violence. Skeletal bones are the strongest bones in the body and they play a crucial role in identifying a person’s gender. ANN is a relatively new technology, is fast emerging as a better prediction model for gender when used with skeletal bones like the femur. Prior studies have extensively used discriminant analysis, logistic regression and other similar statistical tools to understand the role of the mandible and its efficacy in gender determination. This study uses Artificial Neural Networks (ANN) for gender determination and compares results thus obtained with logistic regression and discriminant analysis using mandibular parameters as inputs. Digital panoramic radiographs were used to measure the mandible of 509 individuals. Six linear parameters and one angular parameter of each individual were obtained. Logistic Regression, Discriminant Analysis, and ANN analysis were performed on these parameters. The discriminant analysis had an overall accuracy of 69.1%, logistic regression showed an accuracy of 69.9% and ANN exhibited a higher accuracy of 75%. The results revealed that ANN is a good gender prediction tool that can be applied in the field of forensic sciences for near accurate results. Its application is promising as it automates and eases the method of identifying unknown gender or age with minimal errors

Influence of Thermal and Thermomechanical Stimuli on Dental Restoration Geometry and Material Properties of Cervical Restoration: A 3D Finite Element Analysis

Cervical restoration of a premolar tooth is a challenging task as it involves structural modification to ensure the functional integrity of the tooth. The lack of retention in the cervical area, with the cavity margins on dentin and the nonavailability of enamel, makes it challenging for restoration. The high organic content of dentin, along with its tubular structure and outward flow of fluid, make dentin bonding difficult to attain. The objective of this study is to evaluate the impact of thermal and thermomechanical stimuli on the geometry of dental restorations in the cervical region. In the present study, a three-layered restorative material made of glass ionomer cement, hybrid layer, and composite resin is considered by varying the thickness of each layer. Group 1 of elliptical-shaped cavities generates von Mises stress of about 14.65 MPa (5 °C), 41.84 MPa (55 °C), 14.83 MPa (5 °C and 140 N), and 28.89 MPa (55 °C and 140 N), respectively, while the trapezoidal cavity showed higher stress of 36.27 MPa (5 °C), 74.44 MPa (55 °C), 34.14 MPa (5 °C and 140 N), and 75.57 MPa (55 °C and 140 N), which is comparable to the elliptical cavity. The result obtained from the analysis helps to identify the deformation and volume change that occurs due to various real-time conditions, such as temperature difference and thermal stress. The study provides insight into the behavior of novel restorative materials of varied thicknesses and temperature levels through simulation

Engineering and clinical use of artificial intelligence (AI) with machine learning and data science advancements: radiology leading the way for future

Over the years, many clinical and engineering methods have been adapted for testing and screening for the presence of diseases. The most commonly used methods for diagnosis and analysis are computed tomography (CT) and X-ray imaging. Manual interpretation of these images is the current gold standard but can be subject to human error, is tedious, and is time-consuming. To improve efficiency and productivity, incorporating machine learning (ML) and deep learning (DL) algorithms could expedite the process. This article aims to review the role of artificial intelligence (AI) and its contribution to data science as well as various learning algorithms in radiology. We will analyze and explore the potential applications in image interpretation and radiological advances for AI. Furthermore, we will discuss the usage, methodology implemented, future of these concepts in radiology, and their limitations and challenges

Factors Affecting the Usage of Wearable Device Technology for Healthcare among Indian Adults: A Cross-Sectional Study

Background: Wearable device technology has recently been involved in the healthcare industry substantially. India is the world’s third largest market for wearable devices and is projected to expand at a compound annual growth rate of ~26.33%. However, there is a paucity of literature analyzing the factors determining the acceptance of wearable healthcare device technology among low-middle-income countries. Methods: This cross-sectional, web-based survey aims to analyze the perceptions affecting the adoption and usage of wearable devices among the Indian population aged 16 years and above. Results: A total of 495 responses were obtained. In all, 50.3% were aged between 25–50 years and 51.3% belonged to the lower-income group. While 62.2% of the participants reported using wearable devices for managing their health, 29.3% were using them daily. technology and task fitness (TTF) showed a significant positive correlation with connectivity (r = 0.716), health care (r = 0.780), communication (r = 0.637), infotainment (r = 0.598), perceived usefulness (PU) (r = 0.792), and perceived ease of use (PEOU) (r = 0.800). Behavioral intention (BI) to use wearable devices positively correlated with PEOU (r = 0.644) and PU (r = 0.711). All factors affecting the use of wearable devices studied had higher mean scores among participants who were already using wearable devices. Male respondents had significantly higher mean scores for BI (p = 0.034) and PEOU (p = 0.009). Respondents older than 25 years of age had higher mean scores for BI (p = 0.027) and Infotainment (p = 0.032). Conclusions: This study found a significant correlation with the adoption and acceptance of wearable devices for healthcare management in the Indian context