A machine learning approach to investigate the materials science of enamel aging

Efficacy of the methods of age determination using artificial intelligence in panoramic radiographs - a systematic review

The aim of this systematic review is to analyze the literature to determine whether the methods of artificial intelligence are effective in determining age in panoramic radiographs. Searches without language and year limits were conducted in PubMed/Medline, Embase, Web of Science, and Scopus databases. Hand searches were also performed, and unpublished manuscripts were searched in specialized journals. Thirty-six articles were included in the analysis. Significant differences in terms of root mean square error and mean absolute error were found between manual methods and artificial intelligence techniques, favoring the use of artificial intelligence (p < 0.00001). Few articles compared deep learning methods with machine learning models or manual models. Although there are advantages of machine learning in data processing and deep learning in data collection and analysis, non-comparable data was a limitation of this study. More information is needed on the comparison of these techniques, with particular emphasis on time as a variable.

A New Landscape of Human Dental Aging: Causes, Consequences, and Intervention Avenues

Aging is accompanied by physical dysfunction and physiologic degeneration that occurs over an individual's lifetime. Human teeth, like many other organs, inevitably undergo chronological aging and age-related changes throughout the lifespan, resulting in a substantial need for preventive, restorative as well as periodontal dental care. This is particularly the case for seniors at 65 years of age and those older but economically disadvantaged. Dental aging not only interferes with normal chewing and digestion, but also affects daily appearance and interpersonal communications. Further dental aging can incur the case of multiple disorders such as oral cancer, encephalitis, and other systemic diseases. In the next decades or even hundreds of years, the proportion of the elderly in the global population will continue to rise, a tendency that attracts increasing attention across multiple scientific and medical disciplines. Dental aging will bring a variety of problems to the elderly themselves and poses serious challenges to the medical profession and social system. A reduced, but functional dentition comprising 20 teeth in occlusion has been proposed as a measurement index of successful dental aging. Healthy dental aging is critical to healthy aging, from both medical and social perspectives. To date, biomedical research on the causes, processes and regulatory mechanisms of dental aging is still in its infancy. In this article, updated insights into typical manifestations, associated pathologies, preventive strategies and molecular changes of dental aging are provided, with future research directions largely projected.

Machine Learning Analysis of Microtensile Bond Strength of Dental Adhesives

Dental adhesives provide retention to composite fillings in dental restorations. Microtensile bond strength (µTBS) test is the most used laboratory test to evaluate bonding performance of dental adhesives. The traditional approach for developing dental adhesives involves repetitive laboratory measurements, which consumes enormous time and resources. Machine learning (ML) is a promising tool for accelerating this process. This study aimed to develop ML models to predict the µTBS of dental adhesives using their chemical features and to identify important contributing factors for µTBS. Specifically, the chemical composition and µTBS information of 81 dental adhesives were collected from the manufacturers and the literature. The average µTBS value of each adhesive was labeled as either 0 (if <36 MPa) or 1 (if ≥36 MPa) to denote the low and high µTBS classes. The initial 9-feature data set comprised pH, HEMA, BisGMA, UDMA, MDP, PENTA, filler, fluoride, and organic solvent (OS) as input features. Nine ML algorithms, including logistic regression, k-nearest neighbor, support vector machine, decision trees and tree-based ensembles, and multilayer perceptron, were implemented for model development. Feature importance analysis identified MDP, pH, OS, and HEMA as the top 4 contributing features, which were used to construct a 4-feature data set. Grid search with stratified 10-fold cross-validation (CV) was employed for hyperparameter tunning and model performance evaluation using 2 metrics, the area under the receiver operating characteristic curve (AUC) and accuracy. The 4-feature data set generated slightly better performance than the 9-feature data set, with the highest AUC score of 0.90 and accuracy of 0.81 based on stratified CV. In conclusion, ML is an effective tool for predicting dental adhesives with low and high µTBS values and for identifying important chemical features contributing to the µTBS. The ML-based data-driven approach has great potential to accelerate the discovery of new dental adhesives and other dental materials.

Surface and Structural Studies of Age-Related Changes in Dental Enamel: An Animal Model

In the animal kingdom, continuously erupting incisors provided an attractive model for studying the enamel matrix and mineral composition of teeth during development. Enamel, the hardest mineral tissue in the vertebrates, is a tissue sensitive to external conditions, reflecting various disturbances in its structure. The developing dental enamel was monitored in a series of incisor samples extending the first four weeks of postnatal life in the spiny mouse. The age-dependent changes in enamel surface morphology in the micrometre and nanometre-scale and a qualitative assessment of its mechanical features were examined by applying scanning electron microscopy (SEM) and atomic force microscopy (AFM). At the same time, structural studies using XRD and vibrational spectroscopy made it possible to assess crystallinity and carbonate content in enamel mineral composition. Finally, a model for predicting the maturation based on chemical composition and structural factors was constructed using artificial neural networks (ANNs). The research presented here can extend the existing knowledge by proposing a pattern of enamel development that could be used as a comparative material in environmental, nutritional, and pharmaceutical research.

Interpretable AI Explores Effective Components of CAD/CAM Resin Composites

High flexural strength of computer-aided manufacturing resin composite blocks (CAD/CAM RCBs) are required in clinical scenarios. However, the conventional in vitro approach of modifying materials’ composition by trial and error was not efficient to explore the effective components that contribute to the flexural strength. Machine learning (ML) is a powerful tool to achieve the above goals. Therefore, the aim of this study was to develop ML models to predict the flexural strength of CAD/CAM RCBs and explore the components that affect flexural strength as the first step. The composition of 12 commercially available products and flexural strength were collected from the manufacturers and literature. The initial data consisted of 16 attributes and 12 samples. Considering that the input data for each sample were recognized as a multidimensional vector, a fluctuation range of 0.1 was proposed for each vector and the number of samples was augmented to 120. Regression algorithms—that is, random forest (RF), extra trees, gradient boosting decision tree, light gradient boosting machine, and extreme gradient boosting—were used to develop 5 ML models to predict flexural strength. An exhaustive search and feature importance analysis were conducted to analyze the effective components that affected flexural strength. The R2 values for each model were 0.947, 0.997, 0.998, 0.983, and 0.927, respectively. The relative errors of all the algorithms were within 15%. Among the high predicted flexural strength group in the exhaustive search, urethane dimethacrylate was contained in all compositions. Filler content and triethylene glycol dimethacrylate were the top 2 features predicted by all models in the feature importance analysis. ZrSiO4 was the third important feature for all models, except the RF model. The ML models established in this study successfully predicted the flexural strength of CAD/CAM RCBs and identified the effective components that affected flexural strength based on the available data set.