About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Biological Materials Science
|
| Presentation Title |
Aging and Aesthetics of Tooth Enamel: Machine Learning Analysis of Contributions from Microstructure |
| Author(s) |
Cameron Renteria, Katie Tang, Jack R Grimm, Dwayne D Arola |
| On-Site Speaker (Planned) |
Cameron Renteria |
| Abstract Scope |
Tooth enamel, a mineralized tissue, must endure cyclic contact within an oral environment of diverse chemistry. While regarded a structural material, it also plays a principal role in the aesthetics of your smile. The enamel microstructure consists of a system of cylindrical rods composed of tightly packed hydroxyapatite crystallites with a small distribution of interfacial organic proteins. The composition and structure are responsible for its aesthetics, which evolve with aging. Despite substantial knowledge of its microstructure, a quantitative understanding of the materials science of enamel aesthetics is limited. We used a combination of characterization techniques to understand contributions from the structure of enamel to the overall tooth aesthetics. The effect of aging was elucidated by evaluating tissue from both young and senior adult groups in terms of the classical color space parameters. Using machine learning, we perform a novel multivariate analysis of enamel aesthetics. The results will make you smile. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Biomaterials, Machine Learning, Composites |