Objectives. The study aimed to compare the accuracy of different methods of data acquisition and data reconstruction and to assess their usefulness for 3D printing of tooth replicas. Methods. 3-dimensional models of molar and canine teeth obtain utilizing CBCT examination with different protocols, and optical scanning was compared with models derived from micro-computed (micro-CT) examination using Geomagic Studio Qualify software. A pairwise comparison of 3D models with analysis of standard deviation and the value of the mean distance of given surfaces was performed. Results. Standard deviation and the value of the mean distance were lowest for optical scanning followed by CBC in high and standard resolution in all tested protocols. Models, obtained with high-resolution CBCT protocols, of teeth in and outside of alveolar bone showed similar average distance parameters, but standard deviation parameter was significantly lower for models of teeth scanned outside of the socket. Good surface representation on all models was seen at relatively smooth areas while in areas of high changes in the geometry CBCT based models performed inferiorly to those obtained from an optical scanner. Conclusions. In case of teeth of noncomplicated texture, independently from a position (within or outside the alveolar socket), the high-resolution CBCT seems to be a sufficient method to obtain data for 3D printed tooth replica. Optical scanning performs better when a detailed replica is necessary.

The anthropometric measurements of the skeletonized skull are crucial in the investigation of human remains, mostly for personal identification. The images of the skull are used if the superimposition or approximation of the facial appearance is to be performed. The aim of the study was to compare the accuracy of the anthropometric measurements of skeletonized skulls with corresponding measurements of their 3D reconstructions obtained by CT scanning. Ten skeletonized skulls and their 3D digital reconstructions were measured according to standard anthropometric procedures. The results were compared and statistical analysis was performed. The statistical analysis revealed that the 3D reconstruction is absolutely reliable for any measurements within the skull. The results of the measurements obtained from the CT image were accurate and comparable to the results of the anthropometric analysis of the skeletonized skull. The correlation between the anthropometry of the skeletonized skulls and the CT reconstructions was strong. It is relevant that 3D reconstructions of the skull can be used in forensic anthropology for personal identification.

The aim of this CBCT investigation on the detection of caries was to assess the influence of artefacts produced by the presence of amalgam fillings located in the vicinity. 102 non-cavitated pre-molar and molar teeth were placed in blocks of silicone with approximal contacts consisting of 3 sound or carious teeth and 1 mesial-occlusal-distal amalgam-filled tooth in-between. Radiographs of all the teeth were recorded using the CBCT system (NewTom™ 3G; QR Srl, Verona, Italy; field of view, 9 inches). Data from the CBCT unit were reconstructed and sectioned in the mesiodistal tooth plane. Images were evaluated twice by two observers, using a five-step confidence scale. After the CBCT examination, the teeth were individually sectioned in the mesiodistal direction with a diamond saw. Using a light microscope at ×40 magnification, the true morphological status of all approximal surfaces was established. Sensitivity of the CBCT for the detection of caries on surfaces located proximally and distally to an amalgam filing ranged from 0.27 to 0.30 for enamel and from 0.47 to 0.56 for dentin. Specificity values for enamel proximal and distal lesions were 0.48 and 0.53, respectively, for enamel and 0.33 to 0.38, respectively, for proximal and distal dentin cases. Intra-observer reliability was 0.84, and interobserver reliability was 0.49. Owing to its low specificity, scans from a CBCT examination should not be used to determine the presence of demineralization of the tooth surface when amalgam fillings are present in the region of interest.

Objectives. Fractal analysis of the radiographic pattern of bone has been used to evaluate its quantitative properties. However, the relation between initial implant stability and quality of bone remains unclear. The objective of this study was to evaluate RFA values in relation to the fractal dimension of bone where the implant was inserted. Material and Methods. A total of 50 two-stage dental implants were placed in the maxilla and mandible of 32 patients. After implant placement, an implant stability quotient (ISQ) was measured in two perpendicular planes. On intraoral digital periapical radiographs, three 35x35 pixels’ regions of interest (ROIs) were chosen covering the bone adjacent to the neck (ROI 1), middle (ROI 2), and apical (ROI 3) part of the implant, respectively. For every ROI, a fractal dimension (FD) was calculated. A linear correlation, as well as a logistic regression analysis, was used to identify a possible relation between the ISQ and FD values for every ROI in the maxilla and mandible. Results. The ISQ and FD values were found to be correlated at ROI 1 for the maxilla. There was no linear correlation between ISQ and FD values in any of the three ROIs in the mandible. However, logistic regression analysis showed that in ROI 1 and ROI 3 the values of FD and ISQ are statistically important and may be used to express the difference between maxilla and mandible. Conclusion. The fractal dimension of alveolar bone measured from intraoral digital radiographs alone may be an insufficient parameter to determine initial implant stability.

This study aimed to assess the asymmetry of the lower face and motor dysfunction of the masticatory system resulting from unilateral or asymmetrical bilateral temporomandibular joint (TMJ) involvement in juvenile idiopathic arthritis (JIA) patients. The study consisted of clinical examination and cone beam computed tomography (CBCT) of TMJs. Statistical analysis showed correlations between several factors: facial asymmetry and mandibular lateral deviation; the direction of mandibular deviation and the degree of radiological deformities in TMJs; the child’s age at the time of the onset and the range of lateral movement towards the healthy or less destructed joint. In addition, there was a significant difference in ranges of lateral movements; a significantly smaller range was observed for the joint with fewer condylar abnormalities compared to the range in the opposite direction. In JIA children, among the clinical markers of unilateral or asymmetrical TMJ involvement, the asymmetry of the lower face, deviation of the mandible on opening, and an uneven range of mandibular lateral movements deserve attention. The obtained results do not show a relationship between the degree of condylar changes and the asymmetry of the lower face and the presence and degree of mandibular motor dysfunction.

The inferior alveolar canal (IAC) is a fundamental mandibular structure. It is important to conduct a precise pre-surgical evaluation of the IAC to prevent complications. Recently, the use of artificial intelligence (AI) has demonstrated potential as a valuable tool for dentists, particularly in the field of oral and maxillofacial radiology. The aim of the study was to compare the segmentation time and accuracy of AI-based IAC segmentation with semi-automatic segmentation performed by a specialist. Thirty individual IACs from 15 anonymized cone-beam computed tomography (CBCT) scans of patients with at least 1 lower third molar were collected from the database of Poznan University of Medical Sciences, Poland. The IACs were segmented by a trainee in the field of oral and maxillofacial radiology using a semi-automatic method and automatically by an AI-based platform (Diagnocat). The resulting segmentations were overlapped with the use of Geomagic Studio, reverse engineering software, and then subjected to a statistical analysis. The AI-based segmentation closely matched the semi-automatic method, with an average deviation of 0.275 ±0.475 mm between the overlapped segmentations. The mean segmentation time for the AI-based method (175.00 s) was similar to that of the semi-automatic method (175.67 s). The results of the study indicate that AI-based tools may offer a reliable approach for the segmentation of the IAC in the context of dental pre-surgical planning. However, further comprehensive studies are required to compare the methods and consider their limitations more comprehensively.