Objective: THE AIMS OF THIS STUDY WERE TO: (1) evaluate the caries risk in young adults using Cariogram and (2) compare the efficiency of Cariogram with the regression risk models created using the same variables in Cariogram by examining the actual caries progression over a 2-year period.
Methods: This study included 100 subjects that were either twenty or twenty-one years-old. Data on general health, diet, oral hygiene and use of fluoride were obtained. Saliva analyses were performed, including mutans streptococci and lactobacilli counts, secretion rate and buffer capacity. DMFT and DMFS values were calculated by clinical examinations and radiographs. The participants were divided into 5 groups according to their Cariogram caries risk scores at baseline. Re-examination for caries was done after 2-years. The data were analyzed using Kruskall Wallis, Mann Whitney-U, and logistic regression analyses.
Results: Diet frequency, plaque amount and secretion rate were significantly associated with caries increment (P<.05). Cariogram and the regression risk models explained the caries formation at a higher rate than single-variables. However, the regression risk model developed by diet frequency, plaque amount and secretion rate explained the caries formation similar to Cariogram, while the other regression model developed by all variables used in Cariogram explained the caries formation at a higher rate than this computer program.
Conclusions: Cariogram is effective and can be used for caries risk assessment instead of single variables; however, it is possible to develop simplier models with regression analyses to determine caries risk.
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Indian J Dent Res
October 2024
Department of Pediatric and Preventive Dentistry, Centre for Dental Education and Research, All Indian Institute of Medical Sciences, New Delhi, Delhi, India.
Objective: To evaluate the feasibility of the International Caries Classification and Management System (ICCMS) protocol in a hospital-based setting in India.
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November 2024
Department of Pediatric and Preventive Dentistry, Yenepoya Dental College, Mangaluru, Karnataka, India.
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View Article and Find Full Text PDFBMC Oral Health
January 2025
Department of Clinical Dentistry, Faculty of Medicine, University of Bergen, Bergen, Norway.
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.
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