Teeth Microcracks Research: Towards Multi-Modal Imaging.

This perspective is an overview of the recent advances in teeth microcrack (MC) research, where there is a clear tendency towards a shift from two-dimensional (2D) to three-dimensional (3D) examination techniques, enhanced with artificial intelligence models for data processing and image acquisition. X-ray micro-computed tomography combined with machine learning allows 3D characterization of all spatially resolved cracks, despite the locations within the tooth in which they begin and extend, and the arrangement of MCs and their structural properties. With photoluminescence and micro-/nano-Raman spectroscopy, optical properties and chemical and elemental composition of the material can be evaluated, thus helping to assess the structural integrity of the tooth at the MC site. Approaching tooth samples having cracks from different perspectives and using complementary laboratory techniques, there is a natural progression from 3D to multi-modal imaging, where the volumetric (passive: dimensions) information of the tooth sample can be supplemented by dynamic (active: composition, interaction) image data. Revelation of tooth cracks clearly shows the need to re-assess the role of these MCs and their effect on the structural integrity and longevity of the tooth. This provides insight into the nature of cracks in natural hard materials and contributes to a better understanding of how bio-inspired structures could be designed to foresee crack propagation in biosolids.

Revelation of microcracks as tooth structural element by X-ray tomography and machine learning.

Although teeth microcracks (MCs) have long been considered more of an aesthetic problem, their exact role in the structure of a tooth and impact on its functionality is still unknown. The aim of this study was to reveal the possibilities of an X-ray micro-computed tomography ([Formula: see text]CT) in combination with convolutional neural network (CNN) assisted voxel classification and volume segmentation for three-dimensional (3D) qualitative analysis of tooth microstructure and verify this approach with four extracted human premolars. Samples were scanned using a [Formula: see text]CT instrument (Xradia 520 Versa; ZEISS) and segmented with CNN to identify enamel, dentin, and cracks. A new CNN image segmentation model was trained based on "Multiclass semantic segmentation using DeepLabV3+" example and was implemented with "TensorFlow". The technique which was used allowed 3D characterization of all MCs of a tooth, regardless of the volume of the tooth in which they begin and extend, and the evaluation of the arrangement of cracks and their structural features. The proposed method revealed an intricate star-shaped network of MCs covering most of the inner tooth, and the main crack planes in all samples were arranged radially in two almost perpendicular directions, suggesting that the cracks could be considered as a planar structure.