OBJECTIVES: Conventional plaque assessment methods, such as clinical indices and planimetry, rely on plaque-disclosing agents and may overemphasize thin biofilm areas due to plaque thickness variations. This study introduces a digital 3D method to qu...
AIM: Evaluating dental plaque is a fundamental task for periodontal health care, but it is subjective, time-consuming, and cumbersome. Therefore, this study aimed to develop and validate a web-based deep learning application capable of objectively qu...
Development of dental caries is a dynamic process; yet, there is limited knowledge on microbial differences at various stages of caries at higher resolution. To investigate the shifting microbiome profiles across different caries stages, 30 children ...
BACKGROUND: Oral microbiota is a major etiological factor in the development of dental caries. Next-generation sequencing techniques have been widely used, generating vast amounts of data which is underexplored. The advancement of artificial intellig...
BACKGROUND: The integration of artificial intelligence (AI) into healthcare has led to promising advancements in clinical decision-making and diagnostic accuracy. In dentistry, automated methods to evaluate oral hygiene measures, such as dental plaqu...
AIM: This observational study aimed to verify and improve the predictive value of plaque microbiome of patients with dental implant for peri-implant diseases.
Early childhood caries (ECC) exhibits tooth specificity, highlighting the need for single-tooth-level prevention. We profiled 2,504 dental plaque microbiota samples from 89 preschoolers across two cohorts, tracking compositional changes with imputed ...
BACKGROUND: Periodontitis (PD) is a common chronic inflammatory disease affecting the gums and supporting tooth structures. It is often diagnosed only after significant irreversible tissue damage - such as gum recession and bone loss - has occurred, ...
OBJECTIVES: The selection of treatment for dental plaque is closely related to the condition of the plaque on different teeth. This study validated the ability of CNN models in assessing the dental plaque indices.
Nigerian journal of clinical practice
Jun 29, 2024
OBJECTIVES: This study aims to assess the diagnostic accuracy of an artificial intelligence (AI) system employing deep learning for identifying dental plaque, utilizing a dataset comprising photographs of permanent teeth.
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