Application of machine learning for diagnostic prediction of root caries.

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.

Protocol of the study: Multilevel community-based mental health intervention to address structural inequities and adverse disparate consequences of COVID-19 pandemic on Latinx Immigrants and African refugees.

The NIMH-funded Multilevel Community-Based Mental Health Intervention to Address Structural Inequities and Adverse Disparate Consequences of COVID-19 Pandemic on Latinx Immigrants and African Refugees study aims to advance the science of multilevel interventions to reduce the disparate, adverse mental health, behavioral, and socioeconomic consequences of the COVID-19 pandemic that are a result of complex interactions between underlying structural inequities and barriers to health care. The study tests three nested levels of intervention: 1) an efficacious 4-month advocacy and mutual learning model (Refugee and Immigrant Well-being Project, RIWP); 2) engagement with community-based organizations (CBOs); and 3) structural policy changes enacted in response to the pandemic. This community-based participatory research (CBPR) study builds on long-standing collaboration with five CBOs. By including 240 Latinx immigrants and 60 African refugees recruited from CBO partners who are randomly assigned to treatment-as-usual CBO involvement or the RIWP intervention and a comparison group comprised of a random sample of 300 Latinx immigrants, this mixed methods longitudinal waitlist control group design study with seven time points over 36 months tests the effectiveness of the RIWP intervention and engagement with CBOs to reduce psychological distress, daily stressors, and economic precarity and increase protective factors (social support, access to resources, English proficiency, cultural connectedness). The study also tests the ability of the RIWP intervention and engagement with CBOs to increase access to the direct benefits of structural interventions. This paper reports on the theoretical basis, design, qualitative and quantitative analysis plan, and power for the study.

Artificial intelligence in dentistry: Harnessing big data to predict oral cancer survival.

BACKGROUND: Oral cancer is the sixth most prevalent cancer worldwide. Public knowledge in oral cancer risk factors and survival is limited. AIM: To come up with machine learning (ML) algorithms to predict the length of survival for individuals diagnosed with oral cancer, and to explore the most important factors that were responsible for shortening or lengthening oral cancer survival. METHODS: We used the Surveillance, Epidemiology, and End Results database from the years 1975 to 2016 that consisted of a total of 257880 cases and 94 variables. Four ML techniques in the area of artificial intelligence were applied for model training and validation. Model accuracy was evaluated using mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), R 2 and adjusted R 2. RESULTS: The most important factors predictive of oral cancer survival time were age at diagnosis, primary cancer site, tumor size and year of diagnosis. Year of diagnosis referred to the year when the tumor was first diagnosed, implying that individuals with tumors that were diagnosed in the modern era tend to have longer survival than those diagnosed in the past. The extreme gradient boosting ML algorithms showed the best performance, with the MAE equaled to 13.55, MSE 486.55 and RMSE 22.06. CONCLUSION: Using artificial intelligence, we developed a tool that can be used for oral cancer survival prediction and for medical-decision making. The finding relating to the year of diagnosis represented an important new discovery in the literature. The results of this study have implications for cancer prevention and education for the public.

Exploring the Intersection between Social Determinants of Health and Unmet Dental Care Needs Using Deep Learning.

The goals of this study were to develop a risk prediction model in unmet dental care needs and to explore the intersection between social determinants of health and unmet dental care needs in the United States. Data from the 2016 Medical Expenditure Panel Survey were used for this study. A chi-squared test was used to examine the difference in social determinants of health between those with and without unmet dental needs. Machine learning was used to determine top predictors of unmet dental care needs and to build a risk prediction model to identify those with unmet dental care needs. Age was the most important predictor of unmet dental care needs. Other important predictors included income, family size, educational level, unmet medical needs, and emergency room visit charges. The risk prediction model of unmet dental care needs attained an accuracy of 82.6%, sensitivity of 77.8%, specificity of 87.4%, precision of 82.9%, and area under the curve of 0.918. Social determinants of health have a strong relationship with unmet dental care needs. The application of deep learning in artificial intelligence represents a significant innovation in dentistry and enables a major advancement in our understanding of unmet dental care needs on an individual level that has never been done before. This study presents promising findings and the results are expected to be useful in risk assessment of unmet dental care needs and can guide targeted intervention in the general population of the United States.

Using Machine Learning to Predict 30-Day Hospital Readmissions in Patients with Atrial Fibrillation Undergoing Catheter Ablation.

Atrial fibrillation (AF) cases are expected to increase over the next several decades, due to the rise in the elderly population. One promising treatment option for AF is catheter ablation, which is increasing in use. We investigated the hospital readmissions data for AF patients undergoing catheter ablation, and used machine learning models to explore the risk factors behind these readmissions. We analyzed data from the 2013 Nationwide Readmissions Database on cases with AF, and determined the relative importance of factors in predicting 30-day readmissions for AF with catheter ablation. Various machine learning methods, such as k-nearest neighbors, decision tree, and support vector machine were utilized to develop predictive models with their accuracy, precision, sensitivity, specificity, and area under the curve computed and compared. We found that the most important variables in predicting 30-day hospital readmissions in patients with AF undergoing catheter ablation were the age of the patient, the total number of discharges from a hospital, and the number of diagnoses on the patient's record, among others. Out of the methods used, k-nearest neighbor had the highest prediction accuracy of 85%, closely followed by decision tree, while support vector machine was less desirable for these data. Hospital readmissions for AF with catheter ablation can be predicted with relatively high accuracy, utilizing machine learning methods. As patient age, the total number of hospital discharges, and the total number of patient diagnoses increase, the risk of hospital readmissions increases.

Oral health as a gateway to overall health and well-being: Surveillance of the geriatric population in the United States.

AIMS: Little evidence exists to confirm that better oral health is associated with better overall health and well-being. The present study aimed to examine the impact of oral health on the overall health of the population greater than 65-year old in the entire United States. METHODS AND RESULTS: Data from National Health and Nutrition Examination Survey (NHANES) 2015-2016 were used. Variables included demographics and perceptions of oral health and overall health and well-being. Weighted prevalence estimates were calculated using mean, standard deviation, and percentage as appropriate. Chi-square tests and logistic regressions were performed to examine the association of oral health with physical health, mental health, general health, and systemic disease conditions. Analyses showed statistically significant relationships between oral health, physical, mental and general health, energy levels, work limitation, depression, and appetite. Out of the 10 systemic diseases being investigated, six of them were directly related to oral health outcome. CONCLUSION: This study provided strong empirical evidence that oral health is directly associated with different disease conditions and contributes largely to an individual's general health, particularly in the elderly. In the current landscape of patient-centered and value-based care, addressing the oral health needs of the elderly, who generally find themselves with limited access to care, should be a priority.