BACKGROUND: Early childhood caries (ECC) is a major oral health problem among preschool children that can significantly influence children's quality of life. Machine learning can accurately predict the treatment outcome but its use in ECC management ...
OBJECTIVES: Convolutional Neural Networks (CNNs) have long dominated image analysis in dentistry, reaching remarkable results in a range of different tasks. However, Transformer-based architectures, originally proposed for Natural Language Processing...
BACKGROUND: The aim of this study was to evaluate the efficacy of detecting dental caries under fixed dental prostheses (FDPs) through the analysis of panoramic radiographs utilizing convolutional neural network (CNN) based You Only Look Once (YOLO) ...
This empirical study assessed the potential of developing a machine-learning model to identify children and adolescents with poor oral health using only self-reported survey data. Such a model could enable scalable and cost-effective screening and ta...
BACKGROUND: In the last years, artificial intelligence (AI) has contributed to improving healthcare including dentistry. The objective of this study was to develop a machine learning (ML) model for early childhood caries (ECC) prediction by identifyi...
OBJECTIVES: Approximal caries diagnosis in children is difficult, and artificial intelligence-based research in pediatric dentistry is scarce. To create a convolutional neural network (CNN)-based diagnostic system for the prompt and efficient identif...
The journal of evidence-based dental practice
Jan 4, 2025
ARTICLE TITLE AND BIBLIOGRAPHIC INFORMATION: Diagnostic performance of artificial intelligence-aided caries detection on bitewing radiographs: A systematic review and meta-analysis. Ammar, N. & Kühnisch, J. Japanese Dental Science Review, 60(2024): 1...
The journal of evidence-based dental practice
Dec 12, 2024
OBJECTIVES: To assess Artificial Intelligence (AI) platforms, machine learning methodologies and associated accuracies used in detecting dental caries from clinical images and dental radiographs.
OBJECTIVES: The study aims to address the global burden of dental caries, a highly prevalent disease affecting billions of individuals, including both children and adults. Recognizing the significant health challenges posed by untreated dental caries...
OBJECTIVES: This systematic review aimed to assess the effectiveness of Artificial Intelligence (AI)-based Deep Learning (DL) models in the detection of dental caries on intraoral images.
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