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The utilization of advanced intraoral scanners to acquire 3D dental models has gained significant popularity in the fields of dentistry and orthodontics. Accurate segmentation and labeling of teeth on digitized 3D dental surface models are crucial for computer-aided treatment planning. At the same time, manual labeling of these models is a time-consuming task. Recent advances in geometric deep learning have demonstrated remarkable efficiency in surface segmentation when applied to raw 3D models. However, segmentation of the dental surface remains challenging due to the atypical and diverse appearance of the patients' teeth. Numerous deep learning methods have been proposed to automate dental surface segmentation. Nevertheless, they still show limitations, particularly in cases where teeth are missing or severely misaligned. To overcome these challenges, we introduce a network operator called dilated edge convolution, which enhances the network's ability to learn additional, more distant features by expanding its receptive field. This leads to improved segmentation results, particularly in complex and challenging cases. To validate the effectiveness of our proposed method, we performed extensive evaluations on the recently published benchmark data set for dental model segmentation Teeth3DS. We compared our approach with several other state-of-the-art methods using a quantitative and qualitative analysis. Through these evaluations, we demonstrate the superiority of our proposed method, showcasing its ability to outperform existing approaches in dental surface segmentation.

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In addition to their recognized value for obtaining 3D digital dental models, intraoral scanners (IOSs) have recently been proven to be promising tools for oral health diagnostics. In this work, the most recent literature on IOSs was reviewed with a focus on their applications as detection systems of oral cavity pathologies. Those applications of IOSs falling in the general area of detection systems for oral health diagnostics (e.g., caries, dental wear, periodontal diseases, oral cancer) were included, while excluding those works mainly focused on 3D dental model reconstruction for implantology, orthodontics, or prosthodontics. Three major scientific databases, namely Scopus, PubMed, and Web of Science, were searched and explored by three independent reviewers. The synthesis and analysis of the studies was carried out by considering the type and technical features of the IOS, the study objectives, and the specific diagnostic applications. From the synthesis of the twenty-five included studies, the main diagnostic fields where IOS technology applies were highlighted, ranging from the detection of tooth wear and caries to the diagnosis of plaques, periodontal defects, and other complications. This shows how additional diagnostic information can be obtained by combining the IOS technology with other radiographic techniques. Despite some promising results, the clinical evidence regarding the use of IOSs as oral health probes is still limited, and further efforts are needed to validate the diagnostic potential of IOSs over conventional tools.

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Este trabajo tuvo como objetivo conocer la fiabilidad de la impresora 3D (i3D) aditiva por Matriz de Proceso Digital de Luz (MDLP) Hellbot modelo Apolo®, a través de verificar la congruencia dimensional entre las mallas de modelos impresos (MMi) y su correspondiente archivo digital de origen (MMo), obtenido del software de planificación ortodontica Orchestrate 3D® (O3D). Para determinar su uso en odontología y sus posibilidades clínicas, fue comparada entre cinco i3D de manufactura aditiva, dos DLP, dos por estereolitografía (SLA) y una por Depósito de Material Fundido (FDM). La elección de las cinco i3D se fundamentó en su valor de mercado, intentando abarcar la mayor diversidad argentina disponible. Veinte modelos fueron impresos con cada i3D y escaneados con Escáner Intraoral (IOS) Carestream modelo 3600® (Cs3600). Las 120 MMi fueron importadas dentro del programa de ingeniería inversa Geomagic® Control X® (Cx) para su análisis 3D, consistiendo en la superposición de MMo con cada una de las MMi. Luego, una evaluación cualitativa de la desviación entre la MMi y MMo fue realizada. Un análisis estadístico cuidadoso fue realizado obteniendo como resultado comparaciones en 3d y 2d. Las coincidencias metrológicas en la superposición tridimensional permitieron un análisis exhaustivo y fácilmente reconocible a través de mapas colorimétricos. En el análisis bidimensional se plantearon planos referenciados dentariamente desde la MMo, para hacer coincidir las mediciones desde el mismo punto de partida dentaria. Los resultados fueron satisfactorios y muy alentadores. Las probabilidades de obtener rangos de variabilidad equivalentes a +/- 50µm fueron de un 40,35 % y de +/- 100µm un 71,04 %. Por lo tanto, te- niendo en cuenta las exigencias de congruencia dimensional clínicas de precisión y exactitud a las cuales es sometida nuestra profesión odontológica, se evitan problemas clínicos arrastrados por los errores dimensionales en la manufactura (Cam) (AU)

The objective of this study was to determine the reliability of the Hellbot Apollo® model additive 3D printer (i3D) by Matrix Digital Light Processing (MDLP) by verifying the dimensional congruence between the printed model meshes (MMi) and their corresponding digital source file (MMo), obtained from the Orchestrate 3D® (O3D) orthodontic planning software. A comparison was made between five i3D of additive manufacturing, two DLP, two by stereolithography (SLA), and one by Fused Material Deposition (FDM), to determine its use in dentistry and its clinical possibilities. The choice of the five i3D was based on their market value, trying to cover most of the Argentinean diversity available. Twenty models were printed with each i3D and scanned with Carestream Intraoral Scanner (IOS) model 3600® (Cs3600). The 120 MMi were imported into the reverse engineering program Geomagic® Control X® (Cx) for 3D analysis, consisting of overlaying MMo with each MMi. Then, a qualitative evaluation of the deviation between MMi and MMo. Also, a careful statistical analysis was performed, resulting in 3d and 2d comparisons. Metrological coincidences in three-dimensional overlay allowed a comprehensive and easily recognizable analysis through colorimetric maps. In the two-dimensional analysis, dentally referenced planes were proposed from the MMo, to match the measurements from the same dental starting point. The results were satisfactory and very encouraging. The probabilities of obtaining ranges of variability equivalent to +/- 50µm were 40.35 % and +/- 100µm 71.04 %. Therefore, considering the demands of clinical dimensional congruence, precision, and accuracy to which our dental profession it is subjected, clinical problems caused by dimensional errors in manufacturing (Cam) are avoided (AU)

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With increased access to smartphones, the selfie has gained immense popularity in the past decade. Selfie images could be a significant source of ante-mortem (AM) image data in cases where dental records are unavailable or considered insufficient. The application of 3D imaging and selfies has not been explored to its full potential in human dental identification. The present study aimed to evaluate the feasibility of using selfies as AM data and comparing with 3D post-mortem (PM) scans as an alternative to multiple 2D PM photographs in forensic dental identification. The study sample consisted of 18 selfies (12 matching and 6 non-matching) and 15 3D dental scans obtained from the study participants. The study sample was assessed by 6 experienced forensic odontologists (Raters) in two phases; Phase I-Visual comparison of 2D selfies to 3D scans and Phase II-2D selfies superimposed upon 3D scans. Each rater looked at 15 cases, and a total of 1620 comparisons were made by 6 raters (72 for the 12 matching cases and 1548 were for non-matching) and the opinions for each of the comparisons were analyzed. The results of the study show that use of the 3D superimposition method increased the certainty of the conclusions reached by the raters for the cases with correct matches. Furthermore, 94.2% of the 1548 non-matching comparisons were correctly excluded, compared with 77% for the 2D visual comparison method. The study demonstrated the applicability of this 3D method as a valuable tool in assisting the forensic odontologist with dental identification using selfies.

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Photographs of a person smiling may provide valuable information about their anterior dentition. These images can be an alternative ante-mortem (AM) dental source in cases with no dental records, which gives the forensic odontologist a significant opportunity for comparative dental analysis. There are no reported studies that have investigated the reliability of a superimposition technique using 2D photographs of a smile and 3D dental models in dental identification. The aim of this study was to explore novel odontological methods by combining 2D photographs with 3D dental models, simulating a dental identification scenario. The objective was to increase the accuracy of dental identification using an AM photograph with the aid of 3D imaging as an alternative to post-mortem (PM) photographs. The study comprised of 31 3D dental models (simulating PM information) and 35 digital photographs (simulating AM information). The data was analysed in two phases: Phase I- Visual Comparison of 2D-3D images and Phase II- 2D-3D superimposition after a wash out period. Both methods were analysed by the principal investigator. Further, one-third (ten) of the sample was evaluated by six raters (three experienced forensic odontologists and three forensic odontology MSc. students). The inter-rater agreement was assessed using intra-class correlation (ICC 2, 1, absolute). The results of the study suggest that the inter-rater and intra-rater reliability using 3D superimposition was highest (ICC ≈ 1.0). In summary, there was an increase in match rates and higher certainty among the opinions reached when using the 2D-3D superimposition method. The procedure attempted to reduce the limitations of previously existing 2D methods and is intended to assist forensic experts with an alternative method in dental identification when expressing conclusions on a case using photographs.

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