Artificial plaque removal from interproximal tooth surfaces (maxillary premolar and molar) of a jaw model.

Oral Health Prev Dent

Department of Prosthodontics (Operative Dentistry), Division of Oral Functional Science and Rehabilitation, Asahi University, School of Dentistry, Mizuho-city, Gifu 501-0296, Japan.

Published: December 2009

Purpose: The aim of this study was to compare the ability of the bristles of newly developed toothbrushes in removing artificial plaque deposits from the interproximal areas of a jaw model.

Materials And Methods: Four toothbrushes were evaluated in this study: A, two differences in level patterns, combination of flat and extremely high-tapered filaments; B, one difference in level pattern, combination of flat and extremely high-tapered filaments; C, rippled pattern and high-tapered filaments; and D, rippled pattern and tapered filaments. The brushing simulator was adjusted to provide a horizontal brushing stroke of 20 mm at a rate of 190 strokes per minute for a duration of 1 min. A 200-g force was applied to the brush head. A plaque-like substrate was placed in the facial and the interproximal sides of the artificial teeth that had the cross-sectional dimensions of mesial face in the maxillary right first molar and distal face in the second premolar. The results were photographed, and the area of penetration and the cleaning effectiveness were calculated for each picture by computer digital image analysis. This test was repeated five times for the toothbrush for each design that was evaluated. The resulting data were analysed using ANOVA and the Scheffe test.

Results: The rate of plaque removal was the highest with toothbrush A that gave a significantly greater removal of the artificial plaque than the other three toothbrushes on the maxillary right first molar mesial surface (P < 0.05).

Conclusion: These results suggest that toothbrush A was more effective in plaque removal in this in vitro model used for determining the interproximal penetration of the four bristle designs.

Download full-text PDF

Source

Publication Analysis

Top Keywords

artificial plaque
12
plaque removal
12
high-tapered filaments
12
combination flat
8
flat extremely
8
extremely high-tapered
8
filaments rippled
8
rippled pattern
8
maxillary molar
8
artificial
4

Similar Publications

Background: Alzheimer's disease (AD) is a progressive neurodegenerative disease whose risk can be assessed in the AT(N) framework based on brain levels of Aβ and pathological tau with or without neuronal injury. This helps determine if a cognitively normal or mildly cognitively impaired (MCI) person has clear signs of AD pathogenesis. The AT(N) framework might be enhanced by also considering brain insulin resistance (BIR), which is a common feature in AD dementia (ADd).

View Article and Find Full Text PDF

Tech Bytes-Harnessing Artificial Intelligence for Pediatric Oral Health: A Scoping Review.

Int J Clin Pediatr Dent

November 2024

Department of Pediatric and Preventive Dentistry, Yenepoya Dental College, Mangaluru, Karnataka, India.

Aim And Background: The applications of artificial intelligence (AI) are escalating in all frontiers, specifically healthcare. It constitutes the umbrella term for a number of technologies that enable machines to independently solve problems they have not been programmed to address. With its aid, patient management, diagnostics, treatment planning, and interventions can be significantly improved.

View Article and Find Full Text PDF

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 identifying crucial health behaviours within mother-child pairs.

Methods: For the analysis, we utilized a representative sample of 724 mothers with children under six years in Bangladesh.

View Article and Find Full Text PDF

Artificial intelligence (AI) is a subfield of computer science with the goal of creating intelligent machines (1) Machine learning is a branch of artificial intelligence. In machine learning a datasets are used for training diagnostic algorithms. This review comprehensively explains the applications of AI in the diagnosis in paediatric dentistry.

View Article and Find Full Text PDF

Atherosclerosis (AS) is a major cause of cardiovascular disease. In particular, the unpredictable rupture of vulnerable atherosclerotic plaques (VASPs) can cause serious cardiovascular events such as myocardial infarction, stroke, and even sudden death. Therefore, early evaluation of the vulnerability of atherosclerotic plaques is of great importance.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!