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.

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.

BackgroundRacial disparities in plastic surgery limit health care accessibility and quality. The aim of this study is to determine if racial disparities exist within patient-targeted advertising materials on academic plastic surgery practice (APSP) Web sites and if disparities are more pronounced in specific categories within plastic surgery.MethodsThroughout May 2021, 3 independent reviewers analyzed the Web sites for APSPs and identified all photos, videos, and graphics with visible skin. For each image, the Fitzpatrick skin tone scale was used to classify the skin tone as “White” (I–III) or “non-White” (IV–VI). The images were further categorized based on the type of procedure depicted. Comparisons were made to publish US census data using χ2 tests and linear mixed effects models.ResultsIn total, 4615 images were analyzed from 100 APSP Web sites. Seven hundred eighty (16.9%) portrayed non-White skin tone, which was significantly less than expected based on US census data (23.7% non-White race) (P < 0.001). Online representation had the starkest disparity in hand surgery (8.65% non-White) and adult craniofacial (9.74% non-White). The only categories that showed no significant difference between representation and demographics included implant-based breast reconstruction (P = 0.32) and pediatric craniofacial (P = 0.93). Overall, the marketing materials demonstrated significantly lower representation of non-White skin compared with the census demographics by an absolute difference of −4.71% (P < 0.001).ConclusionsNon-White patients are significantly underrepresented in advertising materials published by APSPs, indicating systemic racial biases. Patient-targeted advertising can be improved to promote equality in representation for patients seeking plastic and reconstructive surgery.

Resource mismanagement along with the underutilization of dental care has led to serious health and economic consequences. Artificial intelligence was applied to a national health database to develop recommendations for dental care. The data were obtained from the 2013-2014 National Health and Nutrition Examination Survey to perform machine learning. Feature selection was done using LASSO in R to determine the best regression model. Prediction models were developed using several supervised machine learning algorithms, including logistic regression, support vector machine, random forest, and classification and regression tree. Feature selection by LASSO along with the inclusion of additional clinically relevant variables identified 8 top features associated with recommendation for dental care. The top 3 features include gum health, number of prescription medications taken, and race. Gum health shows a significantly higher relative importance compared to other features. Demographics, healthcare access, and general health variables were identified as top features related to receiving additional dental care, consistent with prior research. Practicing dentists and other healthcare professionals can follow this model to enable precision dentistry through the incorporation of our algorithms into computerized screening tool or decision tree diagram to achieve more efficient and personalized preventive strategies and treatment protocols in dental care.

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.