Systematic Review and Meta-Analysis on the Accuracy of Artificial Intelligence Algorithms in Individuals Gender Detection Using Orthopantomograms.

The integration of artificial intelligence (AI) into dental imaging has led to significant advancements, particularly in the analysis of panoramic radiographs, also known as orthopantomograms (OPGs). One emerging application of AI is in determining gender from these radiographs, a task traditionally performed by forensic experts using manual methods. This systematic review and meta-analysis aim to evaluate the accuracy of AI algorithms in gender determination using OPGs, focusing on the reliability and potential clinical and forensic applications of these technologies.

The Role of Soft Skills in Dental Education: Challenges and Importance.

Soft skills encompass interpersonal abilities and values that enable individuals to adapt to diverse circumstances. In dentistry, a combination of soft and hard skills is crucial for successful practice and for achieving health care organization goals. However, dental schools face significant challenges in teaching and evaluating soft skills, including the subjective nature of assessment, variability in student engagement, and the lack of standardized curricula.

Comparative analysis of deep learning algorithms for dental caries detection and prediction from radiographic images: a comprehensive umbrella review.

Background: In recent years, artificial intelligence (AI) and deep learning (DL) have made a considerable impact in dentistry, specifically in advancing image processing algorithms for detecting caries from radiographical images. Despite this progress, there is still a lack of data on the effectiveness of these algorithms in accurately identifying caries. This study provides an overview aimed at evaluating and comparing reviews that focus on the detection of using DL algorithms from 2D radiographs.

Deep Learning Architecture to Infer Kennedy Classification of Partially Edentulous Arches Using Object Detection Techniques and Piecewise Annotations.

Objectives: Dental health is integral to overall well-being, with early detection of issues critical for prevention. This research work focuses on utilizing artificial intelligence and deep learning-based object detection techniques for automated detection of common dental issues in orthopantomography x-ray images, including broken roots, periodontally compromised teeth, and the Kennedy classification of partially edentulous arches.

Methods: An orthopantomography dataset has been used to train several models employing various object detection architectures, hyperparameters, and training techniques.

Blood-derived mitochondrial DNA copy number is associated with Alzheimer disease, Alzheimer-related biomarkers and serum metabolites.

Background: Blood-derived mitochondrial DNA copy number (mtDNA-CN) is a proxy measurement of mitochondrial function in the peripheral and central systems. Abnormal mtDNA-CN not only indicates impaired mtDNA replication and transcription machinery but also dysregulated biological processes such as energy and lipid metabolism. However, the relationship between mtDNA-CN and Alzheimer disease (AD) is unclear.