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Conference 13006 > Paper 13006-82
Paper 13006-82

Revelation of tooth structural integrity at the microcrack site using multi-modal imaging

On demand | Presented live 9 April 2024

Abstract

The aim of the study was to combine an X-ray micro-computed tomography (μCT), enhanced with convolutional neural network (CNN) assisted voxel classification and volume segmentation, with photoluminescence (PL) and micro-Raman spectroscopy (μ-RS) for tooth structural integrity evaluation at the microcrack (MC) site of the extracted human teeth. Four maxillary premolars with visible enamel MCs were first examined utilizing an X-ray μCT and segmented with CNN to identify enamel, dentin, and cracks. Secondly, buccal and palatal teeth surfaces with MCs and sound areas were used to obtain fluorescence spectra illuminated with laser exposure wavelengths of 325 nm (CW) and 266 nm (0.5 ns pulsed), spot diameter ~ 80 μm. Thirdly, chemical composition inside the crack and the difference from the sound area were determined utilizing μ-RS method with a 785 nm laser (CW), spot diameter ∼ 3 μm. The proposed approach, which sequentially integrates X-ray μCT in combination with CNN assisted segmentation, PL, and μ-RS, revealed variations in the material composition along the crack line compared to the sound enamel. This includes alterations in the hydroxyapatite crystals’ quantity and/or quality at the sites of cracks versus uncracked enamel, suggesting a potential compromise in the structural integrity of the tooth in the areas affected by MCs.

Presenter

Vilnius Univ. (Lithuania)
In 2020, Dr. Irma Dumbryte completed her PhD in dentistry at Vilnius University, Lithuania. She is working as an orthodontist in private practice and as a research fellow at the Institute of Odontology at Vilnius University. Her research focuses on the detailed examination of teeth microcracks using non-destructive three-dimensional techniques such as X-ray micro-computed tomography, photoluminescence spectroscopy, micro/nano-Raman spectroscopy, as well as the application of machine learning for data analysis.
Application tracks: AI/ML
Presenter/Author
Vilnius Univ. (Lithuania)
Author
Vilnius Univ. (Lithuania)
Author
Maria Androulidaki
Institute of Electronic Structure and Laser, Foundation for Research and Technology-Hellas (Greece)
Author
Elena Jasiuniene
Kaunas Univ. of Technology (Lithuania)
Author
Arturas Vailionis
Stanford Univ. (United States)
Author
Swinburne Univ. of Technology (Australia)
Author
Vilnius Univ. (Lithuania)