ArticleBioMed research international2020
Dental Caries Prediction Based on a Survey of the Oral Health Epidemiology among the Geriatric Residents of Liaoning, China.
Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 17 citations in OpenAlex.
- Potential interaction of sugar intake and tobacco exposure on dental caries in adults-A cross-sectional study from the National Health and Nutrition Examination Survey.Journal of dental sciences · 2024Article
- Artificial Intelligence in Adult and Pediatric Dentistry: A Narrative Review.Bioengineering (Basel, Switzerland) · 2024Review
- Comparison of traditional regression modeling vs. AI modeling for the prediction of dental caries: a secondary data analysis.Frontiers in oral health · 2024Article
- MMDCP: Multi-Modal Dental Caries Prediction for Decision Support System Using Deep Learning.International journal of environmental research and public health · 2022Article
- Clinical Oral Condition Analysis and the Influence of Highly Active Antiretroviral Therapy on Human Salivary Microbial Community Diversity in HIV-Infected/AIDS Patients.Frontiers in cellular and infection microbiology · 2022Article
- Optimizing acute stroke outcome prediction models: Comparison of generalized regression neural networks and logistic regressions.PloS one · 2022Article
- Scope and challenges of machine learning-based diagnosis and prognosis in clinical dentistry: A literature review.Journal of clinical and translational research · 2021Review
- Prediction of Early Childhood Caries Based on Single Nucleotide Polymorphisms Using Neural Networks.Genes · 2021Article
- Artificial intelligence based techniques for caries risk prediction and assessment: A scoping review.Journal of oral biology and craniofacial researchArticle
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7 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundDental caries is one of the most common chronic diseases observed in elderly patients. The development of preventive strategies for dental caries in elderly individuals is vital.
objectiveThe objective of the present study was to construct a generalized regression neural network (GRNN) prediction model for the risk assessment of dental caries among the geriatric residents of Liaoning, China.
methodsA stratified equal-capacity random sampling method was used to randomly select 1144 elderly (65-74 years) residents (gender ratio 1 : 1) of Liaoning, China. Data for the oral assessment, including caries characteristics, and questionnaire survey from each participant were collected. Multivariate logistic regression analysis was then performed to identify the independent predictors. GRNN was applied to establish a prediction model for dental caries. The accuracy of the unconditional logistic regression and the GRNN early warning model was compared.
resultsA total of 1144 patients fulfilled the requirements and completed the questionnaires. The caries rate was 68.5%, and the main associated factors were toothache history, residence area, smoking, and drinking. We randomly divided the data for the 1144 participants into a training set (915 cases) and a test set (229 cases). The optimal smoothing factor was 0.7, and the area under the receiver operating characteristic curve for the GRNN model was 0.626 (95% confidence interval, 0.544 to 0.708), with a
conclusionGeriatric (65-74 years) residents of Liaoning, China, have a high rate of dental caries. Residents with a history of toothache and smoking habits are more susceptible to the disease. The GRNN early warning model is an accurate and meaningful tool for screening, early diagnosis, and treatment planning for geriatric individuals with a high risk of caries.
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