Contributions to enamel durability with aging: An application of data science tools

Understanding aging of tooth tissues is the first step to developing robust treatments that support lifelong oral health. In this study selected nanomechanical, compositional and structural parameters of human enamel were characterized to assess the effects of aging on its durability in terms of the apparent fracture toughness (K_App) and brittleness (B). The interdependencies between aging and the enamel properties were assessed using a combination of traditional Pearson's correlation coefficient matrices and self-organizing maps (SOMs) via unsupervised machine learning. To consider age effects, the enamel of three age groups of donor teeth was studied, including primary (donor age ≤10), young (20 age ≤ age ≤50), and old (55 ≤ age) and differences in properties and correlations were identified. Results showed that K_App was negatively correlated to the E, H, degree of crystallinity, and fluoridation, but positively correlated with carbonate content; the opposite trends were observed in B. Interestingly, the SOMs showed that the outer enamel of the old group underwent a degradation in durability (decrease in K_App and increase in B) that was related to multiple contributions, whereas the inner enamel did not undergo this change. Application of K-means clustering on the trained SOMs offered novel insights into the contributions of enamel durability with aging, unique visualization of high-dimensional data onto 2D plots and identified new research directions that would not have otherwise been discovered. Overall, the findings demonstrate the opportunities for understanding aging of enamel using machine learning techniques to pursue age-targeted oral health care.