[The assessment of the caries risk in young adults].

Dtsch Zahn Mund Kieferheilkd Zentralbl

Poliklinik für Parodontologie, Medizinische Fakultät, Universität Rostock.

Published: January 1993

Several factors for caries prediction have been proved in a one-year follow-up study on 73 patients. The white-spot lesions have proved to be a good criteria for caries prediction in this population. These lesions could be combined with the microbiological tests Dentocult SM and Oricult N or with the determination of the buffer capacity (Dentobuff), which did not much improve the prediction.

Download full-text PDF

Source

Publication Analysis

Top Keywords

caries prediction
8
[the assessment
4
assessment caries
4
caries risk
4
risk young
4
young adults]
4
adults] factors
4
factors caries
4
prediction proved
4
proved one-year
4

Similar Publications

Background: To evaluate the performance of different prediction models based on machine learning to predict the presence of early childhood caries.

Material And Methods: Cross-sectional analytical study. The sociodemographic and clinical data used came from a sample of 186 children aged 3 to 6 years and their respective parents or guardians treated at a Hospital in Ica, Peru.

View Article and Find Full Text PDF

World Health Organization invites the nations to progress towards universal health care coverage. This study evaluated preventive and curative dental services utilization among children aged 12 years and younger in Tehran, Iran, based on the Andersen behavioral model using a generalized structural equation modeling. A phone-based cross-sectional study was conducted in Tehran, Iran, on 886 children in 2023.

View Article and Find Full Text PDF

Prediction of oral diseases in care dependent older people.

BMC Oral Health

January 2025

Department of Odontology, Faculty of Health and Medical Sciences, University of Copenhagen, Nørre Allé 20, Copenhagen, 2200, Denmark.

Background: A large number of older people depend on others for help with their daily personal care, including oral health care. Nursing home and elder-care staff often face challenges identifying older people, who are exposed to or at an increased risk of oral diseases. Thus, the aim of this study was to identify risk factors that non-dental care staff can use to identify older people at risk of oral diseases and poor oral hygiene.

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

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!