In vivo and in vitro diagnosis of cracked teeth: A review

Innovative Approaches in Dental Care: Electrical Impedance Analysis (EIA) for Early Caries Detection

(1) Background: Microcracks and structural fragility in teeth, often undetected by traditional methods until severe complications like fractures or pulp exposure occur, are evaluated in this study using electrical impedance analysis (EIA) as a non-invasive tool for early detection and assessment. (2) Methods: A total of 57 patients were recruited, including individuals with bruxism (n = 20), dental restorations (n = 18), and no significant dental history (control group, n = 19). Electrical impedance measurements were performed on all teeth using a portable device, with data collected from occlusal and proximal surfaces. Patients with abnormal values underwent additional imaging (standard radiographs) to confirm the presence of microcracks. Statistical analyses included ANOVA to compare impedance values between groups and logistic regression to assess the predictors of structural fragility. (3) Results: Teeth with microcracks confirmed by standard radiographs exhibited significantly lower impedance values (mean 50 kΩ) compared to healthy teeth (mean 120 kΩ, p < 0.01). Patients with bruxism showed the highest proportion of teeth with abnormal impedance (45%). Logistic regression identified bruxism as a significant predictor of reduced impedance values (p < 0.05). (4) Conclusions: Electrical impedance analysis demonstrates promise as a non-invasive method for detecting microcracks and assessing structural fragility in teeth. Its application in routine dental check-ups could enable early interventions, particularly for high-risk patients with bruxism or restorations.

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.

A perspective on the diagnosis of cracked tooth: imaging modalities evolve to AI-based analysis

Despite numerous clinical trials and pre-clinical developments, the diagnosis of cracked tooth, especially in the early stages, remains a challenge. Cracked tooth syndrome is often accompanied by dramatic painful responses from occlusion and temperature stimulation, which has become one of the leading causes for tooth loss in adults. Current clinical diagnostical approaches for cracked tooth have been widely investigated based on X-rays, optical light, ultrasound wave, etc. Advances in artificial intelligence (AI) development have unlocked the possibility of detecting the crack in a more intellectual and automotive way. This may lead to the possibility of further enhancement of the diagnostic accuracy for cracked tooth disease. In this review, various medical imaging technologies for diagnosing cracked tooth are overviewed. In particular, the imaging modality, effect and the advantages of each diagnostic technique are discussed. What's more, AI-based crack detection and classification methods, especially the convolutional neural network (CNN)-based algorithms, including image classification (AlexNet), object detection (YOLO, Faster-RCNN), semantic segmentation (U-Net, Segnet) are comprehensively reviewed. Finally, the future perspectives and challenges in the diagnosis of the cracked tooth are lighted.

Three-dimensional non-destructive visualization of teeth enamel microcracks using X-ray micro-computed tomography

Although the topic of tooth fractures has been extensively analyzed in the dental literature, there is still insufficient information about the potential effect of enamel microcracks (EMCs) on the underlying tooth structures. For a precise examination of the extent of the damage to the tooth structure in the area of EMCs, it is necessary to carry out their volumetric [(three-dimensional (3D)] evaluation. The aim of this study was to validate an X-ray micro-computed tomography ([Formula: see text]CT) as a technique suitable for 3D non-destructive visualization and qualitative analysis of teeth EMCs of different severity. Extracted human maxillary premolars were examined using a [Formula: see text]CT instrument ZEISS Xradia 520 Versa. In order to separate crack, dentin, and enamel volumes a Deep Learning (DL) algorithm, part of the Dragonfly's segmentation toolkit, was utilized. For segmentation needs we implemented Dragonfly's pre-built UNet neural network. The scanning technique which was used made it possible to recognize and detect not only EMCs that are visible on the outer surface but also those that are buried deep inside the tooth. The 3D visualization, combined with DL assisted segmentation, enabled the evaluation of the dynamics of an EMC and precise examination of its position with respect to the dentin-enamel junction.