Application of machine learning for diagnostic prediction of root caries

Abstract

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.

Machine Learning Approach to Predict Risk of 90-Day Hospital Readmissions in Patients With Atrial Fibrillation: Implications for Quality Improvement in Healthcare

“…Machine learning is relevant for organizations that want to gain more insights from their data to innovate and not do business as usual, and is useful for handling large and complex data when the relationships between the variables are not apparent. 35 Previous study demonstrated that older age and various comorbidities of patients who underwent AF ablation are factors independently associated with increased likelihood of 90-day readmissions, 36 which matched with our findings. Specifically, patients having 5 or more comorbidities were 2 times more likely to be readmitted within 90 days of initial hospital discharge.…”

“…Machine learning is relevant for organizations that want to gain more insights from their data to innovate and not do business as usual, and is useful for handling large and complex data when the relationships between the variables are not apparent. 35 …”

Developments, application, and performance of artificial intelligence in dentistry – A systematic review

“… 36 Hung et al. 46 2019 CNNs AI based model for predicting root caries 5135 Root caries Data set Accuracy of 97.1%, precision of 95.1%, sensitivity of 99.6% and specificity of 94.3% AUC of 0.997 Trained medical personnel (+)Effective This model perform well and can be allowed for clinical implementation Can be utilized by both dental and non-dental professionals 37 Kim et al. 47 2019 CNNs AI based (CNNs) for diagnosing maxillary sinusitis 200 Maxillary sinusitis Waters' view radiographs AUC of 0.93 for the temporal and 0.88 for geographic external 5 Radiologists (+)Effective AI based (CNNs) demonstrated statistically significantly higher AUC than radiologist in both test sets None 38 Schwendicke et al.…”

Predicting all-cause 90-day hospital readmission for dental patients using machine learning methods

“…As an example, Mahajana et al applied two ensemble schemes of ML models to predict the risk of readmission for heart failure using Electronic Health Records (EHR). 14 Similarly, Bayati et al constructed a predictive model for congestive heart failure readmissions with EHR data. They applied the least absolute shrinkage and selection operator technique to select the most predictive variables and employed the LACE index (length of stay, acuity of admission, Charlson comorbidity index, and number of emergency department visits in preceding 6 months) to build their prediction models.…”

“…As a result, there is an increasing interest in applying ML techniques to build prediction models related to dental care area. 14 Despite the importance of oral health few studies address hospital readmission linked to dental health, and no published study explored the application of ML methods to develop a predictive model related to oral healthcare associated hospital readmission.…”