BACKGROUND: Establishing accurate, reliable, and convenient methods for enamel segmentation and analysis is crucial for effectively planning endodontic, orthodontic, and restorative treatments, as well as exploring the evolutionary patterns of mammal...
BACKGROUND: Artificial intelligence (AI) and large language models (LLMs) like ChatGPT have transformed information retrieval, including in healthcare. ChatGPT, trained on diverse datasets, can provide medical advice but faces ethical and accuracy co...
BACKGROUND: Hypodontia is the absence of one or more teeth in the primary or permanent dentition during development, and radiographic imaging is the most common method of diagnosis. However, in recent years, artificial intelligence-based decision sup...
INTRODUCTION: The integration of artificial intelligence (AI) technologies in healthcare is revolutionizing the workflows of healthcare professionals, enabling faster and more accurate patient treatment. This study aims to evaluate the accuracy of re...
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...
BACKGROUND: A comprehensive analysis of the occlusal plane (OP) inclination in predicting anteroposterior mandibular position (APMP) changes is still lacking. This study aimed to analyse the relationships between inclinations of different OPs and APM...
INTRODUCTION: Artificial intelligence (AI) applications have increased dramatically across a wide range of domains. Dental students will undoubtedly be impacted by the emergence of AI in dentistry.
BACKGROUND: Periodontitis is among the most prevalent inflammatory conditions and greatly impacts oral health. This study aimed to elucidate the role of basement membrane-related genes in the pathogenesis and diagnosis of periodontitis.
OBJECTIVE: We aimed to develop an AI-based model that uses a portable electronic oral endoscope to capture intraoral images of patients for the detection of oral cancer.
BACKGROUND: This study aims to validate a machine learning algorithm previously developed in a training population on a different randomly chosen population (i.e., test set). The discrimination potential of the palatal intraoral scan-based geometric ...