Understanding aging of tooth tissues is critical to the development of patient-centric oral healthcare. Yet, the traditional methods for analyzing the composition-structure-property relationships of hard tissues have limitations when considering aging and other factors.
Objective: To apply unsupervised machine learning tools to pursue an understanding of relationships between the composition and mechanical behavior of aging enamel.
Methods: Molar teeth were collected from primary (age ≤ 8), young adult (24 ≤ age ≤ 46) and old adult (55 ≤ age) donors. The hardness and elastic modulus were quantified using nanoindentation as a function of distance from the Dentin Enamel Junction (DEJ) within the cervical, cuspal and inter-cuspal regions of the enamel crown. Similarly, a co-located analysis of the chemical composition and structure was performed using Raman spectroscopy. A Self-Organizing Maps (SOMs) algorithm was implemented to identify multi-dimensional composition-property relationships.
Results: The hardness and elastic modulus are positively correlated to crystallinity and negatively correlated with carbonate substitution. Furthermore, the effects from fluoridation on the age-dependent properties of enamel is non-linear and depends on its location. The contributions of fluoridation to the enamel properties are different in the cervical and non-cervical regions and appear to be unique within primary and senior adult teeth.
Significance: Based on the findings, unsupervised learning methods can reveal complicated non-linear structure-property relationships in tooth tissues and help to understand the materials science of aging and its consequences.
Keywords: Aging; Crystallinity; Elastic modulus; Enamel; Hardness; Machine learning; Self-organizing maps.
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