Prevention of Dental Disease: Caries and Periodontal Disease

Schoen, Max H.; Freed, John B.

doi:10.1146/annurev.pu.02.050181.000443

Cited by 13 publications

(10 citation statements)

References 45 publications

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“…Among the vulnerable, elder population, root caries tends to occur due to reduced upkeep of dental hygiene practices . While a large portion of the currently affected population can retain their teeth for a majority of their lifespan with simple individual‐ and population‐level interventions, such as water fluoridation and regular professional preventive dental care, socioeconomic components remain a large contributor to increased prevalence of poor oral health.…”

Section: Introductionmentioning

confidence: 99%

“…Among elders, surfaces with visible plaque, denture contact and more prominent gingival recession are areas that are likelier to get affected by root caries . However, generally with those older than 35 years, complexities arise in determining relationships with root caries as the presence of periodontal disease increases and becomes the primary culprit of tooth loss …”

Section: Introductionmentioning

confidence: 99%

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Application of machine learning for diagnostic prediction of root caries

et al. 2019

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Objective This study sought to utilise machine learning methods in artificial intelligence to select the most relevant variables in classifying the presence and absence of root caries and to evaluate the model performance. Background Dental caries is one of the most prevalent oral health problems. Artificial intelligence can be used to develop models for identification of root caries risk and to gain valuable insights, but it has not been applied in dentistry. Accurately identifying root caries may guide treatment decisions, leading to better oral health outcomes. Methods Data were obtained from the 2015‐2016 National Health and Nutrition Examination Survey and were randomly divided into training and test sets. Several supervised machine learning methods were applied to construct a tool that was capable of classifying variables into the presence and absence of root caries. Accuracy, sensitivity, specificity and area under the receiver operating curve were computed. Results Of the machine learning algorithms developed, support vector machine demonstrated the best performance with an accuracy of 97.1%, precision of 95.1%, sensitivity of 99.6% and specificity of 94.3% for identifying root caries. The area under the curve was 0.997. Age was the feature most strongly associated with root caries. Conclusion The machine learning algorithms developed in this study perform well and allow for clinical implementation and utilisation by dental and nondental professionals. Clinicians are encouraged to adopt the algorithms from this study for early intervention and treatment of root caries for the ageing population of the United States, and for attaining precision dental medicine.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of machine learning for diagnostic prediction of root caries

et al. 2019

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“…In developed areas few persons become adults without having had tooth decay in both their primary and permanent teeth (4,11). In developed areas few persons become adults without having had tooth decay in both their primary and permanent teeth (4,11).…”

Section: Methodsmentioning

confidence: 99%

Prevalence of dental caries in schoolchildren of the province of Granada (Spain)

1989

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A sample of 3,347 students between 5 and 14 years of age was selected from the entire student population in the province of Granada (Spain) to study the prevalence of dental caries and several risk factors for the disease. Decay was ascertained in the study population by means of the following indices: df, DMF, and DMFS. Risk factors considered were the level of oral hygiene, number of between-meal sugar-containing food, and flouride concentration in drinking water. The indices mentioned showed a clear relationship to the age of the participants in the study. The level of oral hygiene did not show a clear association with dental caries measured through the df and DMF indices. Curiously, a higher correlation was noted between the disease in deciduous teeth and fluoride concentration than between the disease in permanent teeth and the latter factor.

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“…9 Though these ailments are not life-threatening, the sequel of pain and annoyance they cause needs serious consideration. 10 The socioeconomic factor is the predisposing factor in the development of both these ailments. Furthermore, low income and poor education have found to influence the occurrence of both dental caries and periodontal disease.…”

Section: 5005/jp-journals-10024-2163mentioning

confidence: 99%

Prevalence of Dental Caries and Oral Hygiene Status among Juvenile Prisoners in the Kingdom of Saudi Arabia

Alkhadra

2017

The Journal of Contemporary Dental Practice

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Aim:The aim of the study was to determine the prevalence of dental caries and oral hygiene status among juvenile prisoners in Riyadh, Kingdom of Saudi Arabia. Materials and methods:A total of 105 prisoners aged between 12 and 17 years of age from the Juvenile Detention Center were included in the study. Any prisoner with systemic illness and being noncooperative were excluded from the study. Clinical examinations were performed under an artificial light source using a disposable mouth mirror and explorer while seated on a portable chair. The dental caries experience was measured as the number of decayed, missing, or filled permanent teeth (DMFT) using DMFT score. The oral hygiene index (OHI) was assessed by the Greene and Vermillion method. The data collected were analyzed using Statistical Package for the Social Sciences version 16. Results:The mean age of the sample was 16.33 years, with 74.3% of the prisoners in the 16 to 17-year-old group. The dental caries index measured by calculating the mean DMFT of the juvenile prisoners increased with age. The DMFT scores of 9.09 (4.388) for 78 juvenile prisoners aged 16 to 17 years followed by 9.00 (2.925) for 19 prisoners aged 14 to 15 years and 7.13 (1.808) for 8 prisoners aged 12 to 13 years were observed. The oral hygiene status of the prisoners showed that more than half of the juvenile prisoners included in the study had poor (53.3%) oral hygiene and less than half had fair (45.7%) oral hygiene and only one participant demonstrated good oral hygiene. Conclusion:The juvenile prisoners had high prevalence of dental caries and fair-to-bad oral hygiene, indicating poor oral health status. Furthermore, the study emphasizes the need for special attention from the concerned health authorities, voluntary organizations, and surrounding dental colleges in meeting the oral health needs of this group.

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Prevention of Dental Disease: Caries and Periodontal Disease

Cited by 13 publications

References 45 publications

Application of machine learning for diagnostic prediction of root caries

Application of machine learning for diagnostic prediction of root caries

Prevalence of dental caries in schoolchildren of the province of Granada (Spain)

Prevalence of Dental Caries and Oral Hygiene Status among Juvenile Prisoners in the Kingdom of Saudi Arabia

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