Predictive data mining for diagnosing periodontal disease: the Korea National Health and Nutrition Examination Surveys (KNHANES V and VI) from 2010 to 2015

Data-driven prediction model for periodontal disease based on correlational feature analysis and clinical validation

Objectives

This study aimed to investigate the performance and reliability of data-driven models employing correlational feature analysis and clinical validation for predicting periodontal disease.

Methods

The 7th Korea National Health and Nutrition Examination Survey (n = 10,654) was used for correlation analysis to identify significant risk factors for periodontitis. Periodontal prediction models were developed with the selected factors and database, followed by internal validation with 5-fold cross-validation and 1000 bootstrap resampling. External validation was conducted with clinical data (n = 120) collected through self-reported questionnaires, clinical periodontal parameters, and radiographic image analysis. Predictive performance was assessed for logistics regression, support vector machine, random forest, XGBoost, and neural network algorithms using the area under the receiver operating characteristic curves (AUC) and other performance metrics.

Results

Correlation analysis identified 16 features from over 1000 potential risk factors for periodontitis. The best data-driven model (XGBoost) showed AUC values of 0.823 and 0.796 for internal and external validations, respectively. Modeling with clinical data revealed those same measures to be 0.836 and 0.649, respectively. In addition, the data-driven model could predict other clinical periodontal parameters including severe bone loss (AUC = 0.813), gingival bleeding (AUC = 0.694), and tooth loss (AUC = 0.734). A patient case study about prognostic predictions revealed that the probability of periodontitis can be reduced by 6.0 % (stop smoking) and 0.6 % (stop drinking) on average.

Conclusions

Data-driven models for predicting periodontitis and other periodontal parameters were developed from 16 risk factors, demonstrating enhanced prediction performance and reproducibility in internal-external validations.

Artificial Intelligence in Adult and Pediatric Dentistry: A Narrative Review

Artificial intelligence (AI) has been recently introduced into clinical dentistry, and it has assisted professionals in analyzing medical data with unprecedented speed and an accuracy level comparable to humans. With the help of AI, meaningful information can be extracted from dental databases, especially dental radiographs, to devise machine learning (a subset of AI) models. This study focuses on models that can diagnose and assist with clinical conditions such as oral cancers, early childhood caries, deciduous teeth numbering, periodontal bone loss, cysts, peri-implantitis, osteoporosis, locating minor apical foramen, orthodontic landmark identification, temporomandibular joint disorders, and more. The aim of the authors was to outline by means of a review the state-of-the-art applications of AI technologies in several dental subfields and to discuss the efficacy of machine learning algorithms, especially convolutional neural networks (CNNs), among different types of patients, such as pediatric cases, that were neglected by previous reviews. They performed an electronic search in PubMed, Google Scholar, Scopus, and Medline to locate relevant articles. They concluded that even though clinicians encounter challenges in implementing AI technologies, such as data management, limited processing capabilities, and biased outcomes, they have observed positive results, such as decreased diagnosis costs and time, as well as early cancer detection. Thus, further research and development should be considered to address the existing complications.

Root canal retreatment: a retrospective investigation using regression and data mining methods for the prediction of technical quality and periapical healing

Objectives

This study aimed to investigate patterns and risk factors related to the feasibility of achieving technical quality and periapical healing in root canal non-surgical retreatment, using regression and data mining methods.

Methodology

This retrospective observational study included 321 consecutive patients presenting for root canal retreatment. Patients were treated by graduate students, following standard protocols. Data on medical history, diagnosis, treatment, and follow-up visits variables were collected from physical records and periapical radiographs and transferred to an electronic chart database. Basic statistics were tabulated, and univariate and multivariate analytical methods were used to identify risk factors for technical quality and periapical healing. Decision trees were generated to predict technical quality and periapical healing patterns using the J48 algorithm in the Weka software.

Results

Technical outcome was satisfactory in 65.20%, and we observed periapical healing in 80.50% of the cases. Several factors were related to technical quality, including severity of root curvature and altered root canal morphology (p<0.05). Follow-up periods had a mean of 4.05 years. Periapical lesion area, tooth type, and apical resorption proved to be significantly associated with retreatment failure (p<0.05). Data mining analysis suggested that apical root resorption might prevent satisfactory technical outcomes even in teeth with straight root canals. Also, large periapical lesions and poor root filling quality in primary endodontic treatment might be related to healing failure.

Conclusion

Frequent patterns and factors affecting technical outcomes of endodontic retreatment included root canal morphological features and its alterations resulting from primary endodontic treatment. Healing outcomes were mainly associated with the extent of apical periodontitis pathological damages in dental and periapical tissues. To determine treatment predictability, we suggest patterns including clinical and radiographic features of apical periodontitis and technical quality of primary endodontic treatment.

A Population-Based Study on the Association between Periodontal Disease and Major Lifestyle-Related Comorbidities in South Korea: An Elderly Cohort Study from 2002-2015

This study determined the association between periodontal disease (PD) and major lifestyle-related comorbidities (LCs) using the database of the nationwide population-based National Health Insurance Service–Elderly Cohort 2002–2015. A nationwide representative sample comprising 558,147 participants, aged 60 years, was analyzed. Univariate and multivariate logistic regression analyses adjusted for sociodemographic and economic factors (sex, age, household income, insurance status, health status, and living area) and major LCs (hypertension, diabetes mellitus, rheumatoid arthritis, osteoporosis, cerebral infarction, angina pectoris, myocardial infarction, erectile dysfunction, lipoprotein disorder, and obesity) were used to determine the association between PD and major LCs. Elderly participants with PD had a higher risk of major LCs (hypertension: odds ratio (OR) = 1.40, diabetes mellitus: OR = 1.22, rheumatoid arthritis: OR = 1.16, osteoporosis: OR = 1.37, erectile dysfunction: OR = 1.73, lipoprotein disorder: OR = 1.50, and obesity: OR = 1.59). Our longitudinal cohort study provided evidence that PD was significantly associated with major LCs in elderly participants. In particular, the association between PD and erectile dysfunction had the highest OR in the multivariate analyses.

Using big data to promote precision oral health in the context of a learning healthcare system

There has been a call for evidence-based oral healthcare guidelines, to improve precision dentistry and oral healthcare delivery. The main challenges to this goal are the current lack of up-to-date evidence, the limited integrative analytical data sets, and the slow translations to routine care delivery. Overcoming these issues requires knowledge discovery pipelines based on big data and health analytics, intelligent integrative informatics approaches, and learning health systems. This article examines how this can be accomplished by utilizing big data. These data can be gathered from four major streams: patients, clinical data, biological data, and normative data sets. All these must then be uniformly combined for analysis and modelling and the meaningful findings can be implemented clinically. By executing data capture cycles and integrating the subsequent findings, practitioners are able to improve public oral health and care delivery.

Examining Bias and Reporting in Oral Health Prediction Modeling Studies

Recent efforts to improve the reliability and efficiency of scientific research have caught the attention of researchers conducting prediction modeling studies (PMSs). Use of prediction models in oral health has become more common over the past decades for predicting the risk of diseases and treatment outcomes. Risk of bias and insufficient reporting present challenges to the reproducibility and implementation of these models. A recent tool for bias assessment and a reporting guideline—PROBAST (Prediction Model Risk of Bias Assessment Tool) and TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis)—have been proposed to guide researchers in the development and reporting of PMSs, but their application has been limited. Following the standards proposed in these tools and a systematic review approach, a literature search was carried out in PubMed to identify oral health PMSs published in dental, epidemiologic, and biostatistical journals. Risk of bias and transparency of reporting were assessed with PROBAST and TRIPOD. Among 2,881 papers identified, 34 studies containing 58 models were included. The most investigated outcomes were periodontal diseases (42%) and oral cancers (30%). Seventy-five percent of the studies were susceptible to at least 4 of 20 sources of bias, including measurement error in predictors ( n = 12) and/or outcome ( n = 7), omitting samples with missing data ( n = 10), selecting variables based on univariate analyses ( n = 9), overfitting ( n = 13), and lack of model performance assessment ( n = 24). Based on TRIPOD, at least 5 of 31 items were inadequately reported in 95% of the studies. These items included sampling approaches ( n = 15), participant eligibility criteria ( n = 6), and model-building procedures ( n = 16). There was a general lack of transparent reporting and identification of bias across the studies. Application of the recommendations proposed in PROBAST and TRIPOD can benefit future research and improve the reproducibility and applicability of prediction models in oral health.

Severe periodontitis with tooth loss as a modifiable risk factor for the development of Alzheimer, vascular, and mixed dementia: National Health Insurance Service-National Health Screening Retrospective Cohort 2002-2015

Purpose

The purpose of this study was to evaluate severe periodontitis with tooth loss as a modifiable risk factor for Alzheimer dementia (AD), vascular dementia (VaD), and mixed dementia (MD) using the National Health Insurance Service-National Health Screening Retrospective Cohort database with long-term follow-up over 14 years.

Methods

Multivariate Cox hazards regression analysis was applied to a longitudinal retrospective database, which was updated in 2018, to evaluate the association between severe periodontitis with few remaining teeth and dementia after adjusting for potential risk factors, including sociodemographic factors and comorbid diseases.

Results

Among 514,866 individuals in South Korea, 237,940 (46.2%) participants satisfying the inclusion criteria were selected. A total of 10,115 age- and sex-matched participants with severe periodontitis and 10,115 periodontally healthy participants were randomly selected and evenly assigned. The results showed that the risks of AD (hazard ratio [HR], 1.08), VaD (HR, 1.24), and MD (HR, 1.16) were significantly higher in patients with severe periodontitis with 1–9 remaining teeth after adjustment for sociodemographic factors, anthropomorphic measurements, lifestyle factors, and comorbidities.

Conclusions

Severe periodontitis with few remaining teeth (1–9) may be considered a modifiable risk factor for the development of AD, VaD, and MD in Korean adults.

Association between health status and tooth loss in Korean adults: longitudinal results from the National Health Insurance Service-Health Examinee Cohort, 2002-2015

Purpose

This study investigated the association between health status and tooth loss based on data from the National Health Insurance Service-Health Examinee Cohort in 2002–2015.

Methods

Multivariate Cox proportional hazards regression analyses were applied to a longitudinal retrospective database, which was updated and newly released in 2018, to assess the association between health status and tooth loss while adjusting for potential confounders among sociodemographic and economic factors (sex, age, household income, insurance, and presence of disability), general and oral health status (body mass index [BMI], smoking and drinking status, periodic dental visits and scaling, and brushing before sleep), and comorbid disease (hypertension, diabetes mellitus [DM], and Charlson comorbidity index [CCI]).

Results

Among 514,866 participants from a South Korean population, 234,247 (45.5%) participants satisfying the inclusion criteria were analyzed. In the adjusted multivariate analysis, sex, age, household income, insurance, presence of disability, BMI, smoking and drinking status, periodic scaling, tooth brushing before sleep, DM, and CCI showed statistically significant associations with the loss of at least 1 tooth. The risk of experiencing a loss of ≥4 teeth was associated with an increase in age (in those 50–59 years of age: hazard ratio [HR], 1.98; 95% confidence interval [CI], 1.93–2.03; in those 60–69 years of age: HR, 2.93; 95% CI, 2.85–3.02; and in those 70–79 years of age: HR, 2.93; 95%, CI 2.81–3.05), smoking (HR, 1.69; 95% CI, 1.65–1.73), and DM (HR, 1.43; 95% CI, 1.38–1.48).

Conclusions

The results of this study showed that the risk of experiencing tooth loss was related to multiple determinants. DM and smoking were especially significantly associated with tooth loss.