Salivary lead concentration in dental caries among normal and children with cerebral palsy

Vandal, Vidya B; Noorani, Hina; Shivaprakash, P K; Walikar, Basavaraj N.

doi:10.4103/jisppd.jisppd_200_16

Cited by 10 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another contributory factor is the sweetened medications (carbamazepine most commonly and sometimes even herbal formulations) given to the CP children to control seizures and other medical problems. 8 The DMFT values in the present study were lower in the study group than in the control group, but the results were not significant.…”

Section: Discussioncontrasting

confidence: 76%

Salivary Biomarker Levels and Oral Health Status of Children with Cerebral Palsy and Their Healthy Siblings: A Comparative Study

Anjugam¹,

Mathian²,

Gawthaman³

et al. 2021

Rambam Maimonides Med J

View full text Add to dashboard Cite

Background: The dental needs of cerebral palsy children are an area of study much in need of attention. The neglect of this aspect should be rectified, and simpler diagnostic methodologies should be established and used to serve this purpose. Aim: This study aimed to determine oral health status and salivary biomarkers (salivary flow rate, pH, buffering capacity) among children with cerebral palsy (CP), to compare their data with that of their healthy siblings, and to evaluate the relationship between salivary biomarkers and dental caries. Methods: A total of 30 CP children (study group) and 30 normal healthy siblings (controls) were selected between the ages of 5 and 12 years. Salivary biomarkers were assessed, and oral health status was examined. Statistical Analysis: Chi-square test was used for comparison of oral health status. “Unpaired t test was used to compare caries indexes (decay/filled teeth–primary dentition [dft] and decay/missing/filled teeth–permanent dentition [DMFT]) and salivary biomarkers between the groups. Pearson correlation was used to find the correlation between salivary biomarkers and caries. Results: The dft scores were significantly higher in the study group (P<0.05). The pH values and salivary flow rates were significantly lower in the study group (P<0.05 and P<0.001, respectively). There was a significant correlation between DMFT scores and salivary flow rate in the study group (P<0.05). Conclusion: Low pH and low salivary flow rate might be risk factors for dental caries in CP populations; moreover, the significant correlation between DMFT score and salivary flow rate suggests that salivary flow rate could be used as a screening tool for assessing at-risk subjects in such populations.

show abstract

Section: Discussioncontrasting

confidence: 76%

Salivary Biomarker Levels and Oral Health Status of Children with Cerebral Palsy and Their Healthy Siblings: A Comparative Study

Anjugam¹,

Mathian²,

Gawthaman³

et al. 2021

Rambam Maimonides Med J

View full text Add to dashboard Cite

show abstract

“…Support Vector Machine is a linear classification model which can solve linear and non-linear problems. In essence, SVM is an algorithm that takes data as input and classifies them, if possible, using a line or hyperplane [ 32 ].…”

Section: Methodsmentioning

confidence: 99%

Dental Caries Risk Assessment in Children 5 Years Old and under via Machine Learning

et al. 2022

View full text Add to dashboard Cite

Background: Dental caries is a prevalent, complex, chronic illness that is avoidable. Better dental health outcomes are achieved as a result of accurate and early caries risk prediction in children, which also helps to avoid additional expenses and repercussions. In recent years, artificial intelligence (AI) has been employed in the medical field to aid in the diagnosis and treatment of medical diseases. This technology is a critical tool for the early prediction of the risk of developing caries. Aim: Through the development of computational models and the use of machine learning classification techniques, we investigated the potential for dental caries factors and lifestyle among children under the age of five. Design: A total of 780 parents and their children under the age of five made up the sample. To build a classification model with high accuracy to predict caries risk in 0–5-year-old children, ten different machine learning modelling techniques (DT, XGBoost, KNN, LR, MLP, RF, SVM (linear, rbf, poly, sigmoid)) and two assessment methods (Leave-One-Out and K-fold) were utilised. The best classification model for caries risk prediction was chosen by analysing each classification model’s accuracy, specificity, and sensitivity. Results: Machine learning helped with the creation of computer algorithms that could take a variety of parameters into account, as well as the identification of risk factors for childhood caries. The performance of the classifier is almost unbiased, making it generalizable. Among all applied machine learning algorithms, Multilayer Perceptron and Random Forest had the best accuracy, with 97.4%. Support Vector Machine with RBF Kernel (with an accuracy of 97.4%) was better than Extreme Gradient Boosting (with 94.9% accuracy). Conclusion: The outcomes of this study show the potential of regular screening of children for caries risk by experts and finding the risk scores of dental caries for any individual. Therefore, in order to avoid dental caries, it is possible to concentrate on each individual by utilizing machine learning modelling.

show abstract

“…Of these, ten studies were from African countries including Nigeria [ 21 – 24 ], Egypt [ 25 , 26 ], Cameroon [ 27 ], South Africa [ 28 ], Uganda [ 29 ], and Sudan [ 30 ]. Other studies were from Asian countries including Bangladesh [ 31 ], Saudi Arabia [ 32 – 34 ], India [ 35 – 37 ], Thailand [ 38 ], China [ 39 ], United Arab Emirates [ 40 ], Sri Lanka [ 41 ], Turkey [ 42 ], Iran [ 43 ], Pakistan [ 44 ], and Jordan [ 45 ]. Studies from 2008 to 2022 were included.…”

Section: Resultsmentioning

confidence: 99%

“…In the random effects model analysis, the pooled mean DMFT of dental caries in children with cerebral palsy was 2.25 (95% CI: 1.86, 2.64). The highest weight among studies was observed from the studies conducted by Vandal et al [ 37 ], Aburahma et al [ 45 ], Nzomiwu et al [ 24 ]. In the pooled estimate of mean DMFT, there was statistically significant heterogeneity among studies (I 2 = 97.0%, p -value < 0.001) (Fig.…”

Section: Resultsmentioning

confidence: 99%

Dental caries and mean values of DMFT among children with cerebral palsy: a systematic review and meta-analysis

Zemene,

Dessie,

Anley

et al. 2024

BMC Oral Health

View full text Add to dashboard Cite

Introduction One of the most prevalent causes of physical disability in children is cerebral palsy (CP), which is a series of complicated neurological disorders. Children with cerebral palsy suffer from multiple problems and potential disabilities, including dental caries. Hence, this study aimed to determine the pooled prevalence of dental caries and mean DMFT (Decayed, Missed, and Filled Permanent Teeth) among children with cerebral palsy in Africa and Asia. Methods A comprehensive search of the literature was made to locate relevant studies in PubMed/Medline, HINARI, Web of Science, Science Direct, the Cochrane Library, the Worldwide Science Database, and Google Scholar. The data were extracted in Microsoft Excel and transferred to Stata version 17 software for further analysis. A random-effect model was employed to estimate the pooled prevalence of dental caries and the pooled mean value of DMFT among children with cerebral palsy in Africa and Asia. Heterogeneity between studies was checked using the Cochrane Q test and I2 test statistics. Sub-group analysis by continent was done, and sensitivity analysis was checked. A small study effect was checked using Egger’s statistical test at the 5% level of significance. Results In this study, 25 original studies conducted in 17 countries in Africa and Asia that fulfilled the eligibility criteria were included in the review. The overall pooled prevalence of dental caries in Africa and Asia among children with cerebral palsy was 55.6% (95% CI: 42.4, 68.8). The pooled prevalence of dental caries among children with cerebral palsy in Africa was 42.43% (95% CI: 30.39, 54.58), and it was slightly higher in Asia with 64% (95% CI: 48.32, 79.72). In the random effect model analysis, the pooled mean DMFT of dental caries in children with cerebral palsy was 2.25 (95% CI: 1.86, 2.64). The pooled mean DMFT in Africa was 1.47 (95% CI: 0.86, 2.09), and in Asia it was 3.01 (95% CI: 2.43, 3.60). Conclusion In this study, we found that children with cerebral palsy experienced an alarming rate of dental caries. In these settings, dental caries affected roughly more than half of the children with cerebral palsy. Hence, oral health promotion initiatives should target children with CP, and this group of children must receive early preventive dental care.

show abstract

Salivary lead concentration in dental caries among normal and children with cerebral palsy

Cited by 10 publications

References 0 publications

Salivary Biomarker Levels and Oral Health Status of Children with Cerebral Palsy and Their Healthy Siblings: A Comparative Study

Salivary Biomarker Levels and Oral Health Status of Children with Cerebral Palsy and Their Healthy Siblings: A Comparative Study

Dental Caries Risk Assessment in Children 5 Years Old and under via Machine Learning

Dental caries and mean values of DMFT among children with cerebral palsy: a systematic review and meta-analysis

Contact Info

Product

Resources

About