Prediction of Early Childhood Caries Based on Single Nucleotide Polymorphisms Using Neural Networks

Zaorska, Katarzyna; Szczapa, Tomasz; Borysewicz−Lewicka, Maria; Nowicki, Michał; Gerreth, Karolina

doi:10.3390/genes12040462

Cited by 18 publications

(22 citation statements)

References 69 publications

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“…Numerous environmental and individual risk factors are responsible for this disorder’s occurrence and development. These components are related e.g., to the genetic factors, the transmission of germs between hosts, bacterial flora, flow rate and composition of saliva, hygienic and dietary habits, socioeconomic status, parental education, and oral health care availability [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Clinical Consequences of Dental Caries, Parents’ Perception of Child’s Oral Health and Attitudes towards Dental Visits in a Population of 7-Year-Old Children

Opydo-Szymaczek

Borysewicz−Lewicka

Andrysiak

et al. 2021

IJERPH

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This cross-sectional study aimed to assess the occurrence of the consequences of dental caries and factors affecting dental service utilization in a population of 7-year-old children. The research included oral examination of 7-year-old schoolchildren and socio-medical study of their parents/legal caregivers. It was carried out in five primary schools of two provinces, i.e., Greater Poland and Lubusz. Dental examination was performed in accordance with World Health Organization (WHO) recommendations. The socio-medical study consisted of questionnaires with close-ended questions concerning socioeconomic characteristics of the family, reasons and time of the last visit at the dental office, consequences of child’s oral health problems, parents’ attitude towards dental visits, and parental opinion about their child’s teeth. Factors affecting utilization of dental services were statistically analyzed using univariate logistic regression assuming p < 0.05. The pufa index of examined children ranged from 0 to 7 (mean 0.80 ± 1.49), while the dmf index ranged from 0 to 14 (3.86 ± 3.32). Low financial burden of oral health expenditures and university education of at least one of the parents significantly increased the chance of visiting a dentist despite lack of pain (OR = 3.0 and 2.5, respectively). In spite of the availability of free dental care for children, socioeconomic factors still determine the utilization of dental services in Poland. Poor oral health status of examined population and negligence of regular dental check-ups emphasize a need to strengthen oral health literacy of parents and children, promoting proper attitudes towards dental care.

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Section: Introductionmentioning

confidence: 99%

Clinical Consequences of Dental Caries, Parents’ Perception of Child’s Oral Health and Attitudes towards Dental Visits in a Population of 7-Year-Old Children

Opydo-Szymaczek

Borysewicz−Lewicka

Andrysiak

et al. 2021

IJERPH

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“…Ref. [ 14 ] proposed a Generalized regression neural network model for dental caries prediction among the Geriatric residents of Liaoning (China). This study yielded an accuracy, sensitivity, specificity, and AUC-ROC values of 77.29%, 85.16%, 70.27% and 0.626, respectively, while our model displayed higher values, as described in the model evaluation result section.…”

Section: Resultsmentioning

confidence: 99%

“…Liu, L. et al [ 13 ] constructed a generalized regression neural network (GRNN) model to predict caries among residents of Liaoning, China. Using regression on some highly correlated data tends to cause spurious results and overfitting [ 14 ].…”

Section: Related Workmentioning

confidence: 99%

MMDCP: Multi-Modal Dental Caries Prediction for Decision Support System Using Deep Learning

Njimbouom

Lee

Kim

2022

IJERPH

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In recent years, healthcare has gained unprecedented attention from researchers in the field of Human health science and technology. Oral health, a subdomain of healthcare described as being very complex, is threatened by diseases like dental caries, gum disease, oral cancer, etc. The critical point is to propose an identification mechanism to prevent the population from being affected by these diseases. The large amount of online data allows scholars to perform tremendous research on health conditions, specifically oral health. Regardless of the high-performing dental consultation tools available in current healthcare, computer-based technology has shown the ability to complete some tasks in less time and cost less than when using similar healthcare tools to perform the same type of work. Machine learning has displayed a wide variety of advantages in oral healthcare, such as predicting dental caries in the population. Compared to the standard dental caries prediction previously proposed, this work emphasizes the importance of using multiple data sources, referred to as multi-modality, to extract more features and obtain accurate performances. The proposed prediction model constructed using multi-modal data demonstrated promising performances with an accuracy of 90%, F1-score of 89%, a recall of 90%, and a precision of 89%.

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“…Articles without complete texts, narrative reviews, scoping reviews, letters to the editor, opinion letters, case reports, brief communications, conference proceedings, and non-English language articles (84 articles) were all omitted ( Figure 1 ). Finally, only 25 papers met the qualifying requirements [ 3 , 7 , 10 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ].…”

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence Its Uses and Application in Pediatric Dentistry: A Review

et al. 2023

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In the global epidemic era, oral problems significantly impact a major population of children. The key to a child’s optimal health is early diagnosis, prevention, and treatment of these disorders. In recent years, the field of artificial intelligence (AI) has seen tremendous pace and progress. As a result, AI’s infiltration is witnessed even in those areas that were traditionally thought to be best left to human specialists. The ultimate ability to improve patient care and make precise diagnoses of illnesses has revolutionized the world of healthcare. In the field of dentistry, the competence to execute treatment measures while still providing appropriate patient behavior counseling is in high demand, particularly in the field of pediatric dental care. As a result, we decided to conduct this review specifically to examine the applications of AI models in pediatric dentistry. A comprehensive search of the subjects was done using a wide range of databases to look for studies that have been published in peer-reviewed journals from its inception until 31 December 2022. After the application of the criteria, only 25 of the 351 articles were taken into consideration for this review. According to the literature, AI is frequently used in pediatric dentistry for the purpose of making an accurate diagnosis and assisting clinicians, dentists, and pediatric dentists in clinical decision making, developing preventive strategies, and establishing an appropriate treatment plan.

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Prediction of Early Childhood Caries Based on Single Nucleotide Polymorphisms Using Neural Networks

Cited by 18 publications

References 69 publications

Clinical Consequences of Dental Caries, Parents’ Perception of Child’s Oral Health and Attitudes towards Dental Visits in a Population of 7-Year-Old Children

Clinical Consequences of Dental Caries, Parents’ Perception of Child’s Oral Health and Attitudes towards Dental Visits in a Population of 7-Year-Old Children

MMDCP: Multi-Modal Dental Caries Prediction for Decision Support System Using Deep Learning

Artificial Intelligence Its Uses and Application in Pediatric Dentistry: A Review

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