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Standardized tooth numbering is crucial in dentistry for accurate recordkeeping, targeted procedures, and effective communication in both clinical and forensic contexts. However, conventional manual methods are prone to errors, time-consuming, and susceptible to inconsistencies. This study presents an artificial intelligence (AI)-powered system that uses a deep learning-based object detection approach to automate tooth numbering in bitewing radiographs (BRs). The system follows the widely accepted FDI two-digit notation system and employs a state-of-the-art YOLO architecture. This one-stage model provides fast inference by simultaneously performing object detection and classification. A comprehensive dataset of 3000 adult digital BRs was used for training and evaluation, covering various scenarios to improve the robustness of the tooth numbering approach. Performance was assessed based on precision, recall, and mean average precision (mAP). The proposed method showcases the potential of AI-powered systems utilizing sophisticated YOLO architectures to automatically detect and label teeth in dental X-rays. It achieved impressive results, demonstrating a precision of 0.99 and 0.963, recall of 0.995 and 0.965, and mAP of 0.99 and 0.963 for tooth detecting and tooth numbering, respectively. With an average inference time of 303 ms per BR when using a central processing unit (CPU) and 9.1 ms when using a graphics processing unit (GPU), the system seamlessly integrates into clinical workflows without sacrificing efficiency. This results in significant time savings for dental professionals while maintaining productivity in fast-paced clinical environments., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: This study evaluated the accuracy of the CBCT reconstruction model compared to the natural tooth and the accuracy of the replica tooth compared to the natural tooth., Objective: The hypothesis was that a replica tooth could be used as a surgical guide in autotransplantation., Methods: Three teeth were chosen and a CBCT reconstruction model was formed from each tooth. STL-data was transferred to a milling machine and replica teeth were milled from PEEK. A digitized surface model was prepared from the natural and the replica teeth by a stereophotogrammetry scanner. The surface model from the optical scan of the natural tooth was compared to the CBCT reconstruction model and the surface model of the replica tooth. The models were matched on each other, and surface-based rigid registration was performed between the surface models. Distances were calculated and visualized by MATLAB., Results: The CBCT reconstruction model and the natural tooth were compared. The largest euclidean distance was found at the root tip in the premolar (0.93 mm) and at the furcation area in the molar (2.3 mm). When the natural tooth and the replica tooth were compared, the largest euclidean distance was found at the root tip in the premolar (1.5 mm) and at the furcation area in the molar (1.9 mm)., Conclusion: A CBCT scan maintains sufficient image quality for tooth autotransplantation planning. The replica tooth corresponded in size and shape to the natural tooth in terms of clinically expected need of precision., (© 2024 The Author(s). Clinical and Experimental Dental Research published by John Wiley & Sons Ltd.)

Accurately identifying periodontal landmarks via acoustic imaging is increasingly important. Here, we evaluated the accuracy of cementoenamel junction (CEJ) identification using ultrasound by comparing it to clinical methods in 153 extracted human teeth. The distance between the CEJ to a reference point was measured using two clinical methods (visual examination and tactile sensation) as well as ultrasound imaging. Statistical analyses were performed across all teeth and sub-groups, including tooth types (incisors, cuspids, and molars/premolars), and two classifications: A- vs. B- (visually detectable or undetectable CEJ, respectively); and CL-S vs. CL-D (shallow or deep cervical lesions). In A- or CL-S teeth, ultrasound measurements highly agreed with clinical measurements, showing a 1.72-mm 95% CI for A- and 1.99-mm 95% CI for CL-S compared to visual examination, and a 1.77-mm 95% CI for A- and a 2.10-mm 95% CI for CL-S compared to tactile sensation, respectively. For 80% of A- and 76% of CL-S teeth, the difference between ultrasound and visual examination was within ± 20%. For 81% of A- and 80% of CL-S teeth, the difference between ultrasound and tactile sensation was within ± 20%. The variance of ultrasound versus clinical CEJ identifications showed a significant correlation (r = 0.6607) to the cervical lesion depth. The errors between ultrasound and clinical measurements show no significant bias across different tooth types., Competing Interests: Competing interests J.V.J. serves as a co-founder of StyloSonic, LLC., (© 2024. The Author(s).)

Civil and criminal forensics utilize dental development to estimate age. The method of Nicodemo, Moraes, and Médici Filho (NMM) is a popular dental age estimation tool in South America; however, it lacks a scientific basis for applications in contemporary forensic practice. This research included the largest sample ever collected in Brazil for a similar purpose. The sample consisted of 3,271 panoramic radiographs of female (n = 1,634) and male (n = 1,637) individuals between six and 22.9 years old (mean 14.6 ± 4.9 years). The applied NMM method considered all maxillary and mandibular left permanent teeth (n = 16). The fit between the chronological age and estimated age intervals was assessed, and a correlation test with Lin's correlation coefficient was performed. The overall percentage of fit was 22.5%, without statistically significant differences based on sex (p > 0.05). The percentage of fit was greater in younger individuals, such as those aged 6-6.99 years (90%), and progressively decreased in older individuals, such as those aged 11-11.9 years (18.2%). After 12 years of age, the method could not provide correct classifications up to 25 years of age. Lin's correlation coefficient was predominantly low (ρ = 0.175; 0.367). NMM is considerably limited, and current forensic practice should not apply it to estimate dental age.

Traditional dental prosthetics require a significant amount of work, labor, and time. To simplify the process, a method to convert teeth scan images, scanned using an intraoral scanner, into 3D images for design was developed. Furthermore, several studies have used deep learning to automate dental prosthetic processes. Tooth images are required to train deep learning models, but they are difficult to use in research because they contain personal patient information. Therefore, we propose a method for generating virtual tooth images using image-to-image translation (pix2pix) and contextual reconstruction fill (CR-Fill). Various virtual images can be generated using pix2pix, and the images are used as training images for CR-Fill to compare the real image with the virtual image to ensure that the teeth are well-shaped and meaningful. The experimental results demonstrate that the images generated by the proposed method are similar to actual images. In addition, only using virtual images as training data did not perform well; however, using both real and virtual images as training data yielded nearly identical results to using only real images as training data., (© 2024. The Author(s).)

Objectives: Cracked tooth syndrome (CTS) is one of the major causes of tooth loss, presents the problem of early microcrack symptoms that are difficult to distinguish. This paper aims to investigate the practicality and feasibility of an improved object detection algorithm for automatically detecting cracks in dental optical images., Methods: A total of 286 teeth were obtained from Sun Yat-sen University and Guangdong University of Technology, and simulated cracks were generated using thermal expansion and contraction. Over 3000 images of cracked teeth were collected, including 360 real clinical images. To make the model more lightweight and better suited for deployment on embedded devices, this paper improves the YOLOv8 model for detecting tooth cracks through model pruning and backbone replacement. Additionally, the impact of image enhancement modules and coordinate attention modules on optimizing our model was analyzed., Results: Through experimental validation, we conclude that that model pruning reduction maintains performance better than replacing a lightweight backbone network on a tooth crack detection task. This approach achieved a reduction in parameters and GFLOPs by 16.8 % and 24.3 %, respectively, with minimal impact on performance. These results affirm the effectiveness of the proposed method in identifying and labeling tooth fractures. In addition, this paper demonstrated that the impact of image enhancement modules and coordinate attention mechanisms on YOLOv8's performance in the task of tooth crack detection was minimal., Conclusions: An improved object detection algorithm has been proposed to reduce model parameters. This lightweight model is easier to deploy and holds potential for assisting dentists in identifying cracks on tooth surfaces., Competing Interests: Declaration of competing interest The authors declared that they have no conflicts of interest to this work., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Tooth instance segmentation of dental panoramic X-ray images is of significant clinical importance. Teeth exhibit symmetry within the upper and lower jawbones and are arranged in a specific order. However, previous studies frequently overlook this crucial spatial prior information, resulting in the misidentifications of tooth categories, especially for adjacent or similarly shaped teeth. In this paper, we propose SPGTNet, a spatial prior-guided transformer method, designed to both the extracted tooth positional features from CNNs and the long-range contextual information from vision transformers, specifically for dental panoramic X-ray image segmentation. Initially, a center-based spatial prior perception module is employed to identify the centroid of each tooth, thereby enhancing the spatial prior information for the CNN sequence features. Subsequently, a bi-directional cross-attention module is designed to facilitate the interaction between the spatial prior information of the CNN sequence features and the long-range contextual features of the vision transformer sequence features. Finally, an instance identification head is employed to generate the tooth segmentation results. Extensive experiments on three public benchmark datasets demonstrate the effectiveness and superiority of our proposed method compared to other state-of-the-art approaches. The proposed method accurately identifies and analyzes tooth structures, thereby providing crucial information for dental diagnosis, treatment planning, and research.

Objectives: To evaluate the effects of repeated cut-off and rescan procedures on the trueness of three intraoral scanners (IOS)., Methods: A tooth model (#16) with a standard class II cavity was prepared, and the complete-arch was scanned using a laboratory scanner (D2000, 3Shape A/S) to obtain a reference scan. Then the typodont was scanned with three IOSs (3Shape TRIOS 3, CEREC Omnicam, and Medit i500) under two rescanning strategies (full-cut and partial-cut), with varying numbers of repeated cut-off and rescanning procedures (0, 1, 3, 5, 7, or 10). The trueness discrepancy between the reference and experimental digital scan was estimated using root mean square (RMS) calculations. Three regions of interest were selected to represent the rescanning, identification, and non-rescan area. And the discrepancies were analyzed using a linear mixed model (α=0.05)., Results: Cut-off and rescanning procedures significantly decreased the trueness of digital scans in all test conditions compared to the reference. However, no progressive increase in discrepancy was observed under any rescan conditions. Significant influences on trueness were found based on the IOS used, with the 3Shape system exhibiting lower RMS values. The partial-cut strategy showed lower RMS values compared to the full-cut strategy, albeit without statistical significance., Conclusions: While repeated cut-off and rescanning procedures led to a decline in the quality of digital impressions, they did not result in discrepancy accumulation with repeated rescanning., Clinical Significance: To ensure high scanning accuracy in dental practice, it is advisable to minimize the rescanning area when correcting imperfections in digital scans. Additionally, selecting an appropriate scanner can help mitigate the negative effects of the rescanning technique., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

The timing of dental development in ethnic Finns and Somalis, who were born and living in Finland, was compared, with efforts to minimize environmental bias. The developmental status of seven lower left permanent teeth were staged according to Demirjian et al., using panoramic radiographs from 2,100 Finnish and 808 Somali females and males, aged 2 to 23 years. For each tooth, a continuation-ratio model was constructed to analyze the allocated stages as a function of sex and ethnic origin. Several statistically significant differences in mean age of certain tooth developmental stage transitions were revealed. While Somalis generally displayed stage transitions at younger age, none of the seven teeth consistently showed earlier stage transitions in Somalis compared to Finns. Within each tooth, the lowest (or highest) mean age of stage transition varied without any discernible pattern between the two ethnic groups. Overall, the observed differences in mean age of stage transition between the groups was minimal, suggesting a low impact on clinical and forensic age assessment practice. In conclusion, the studied ethnic Finn and Somali groups with equal nutritional and /or environmental conditions exhibit similar timing in the development of all lower left permanent teeth., (© 2024. The Author(s).)

Objectives: Accurately identification and tooth numbering on radiographs is essential for any clinicians. The aim of the present study was to validate the hypothesis that Yolov5, a type of artificial intelligence model, can be trained to detect and number teeth in periapical radiographs., Materials and Methods: Six thousand four hundred forty six anonymized periapical radiographs without motion-related artifacts were randomly selected from the database. All periapical radiographs in which all boundaries of any tooth could be distinguished were included in the study. The radiographic images used were randomly divided into three groups: 80% training, 10% validation, and 10% testing. The confusion matrix was used to examine model success., Results: During the test phase, 2578 labelings were performed on 644 periapical radiographs. The number of true positive was 2434 (94.4%), false positive was 115 (4.4%), and false negative was 29 (1.2%). The recall, precision, and F1 scores were 0.9882, 0.9548, and 0.9712, respectively. Moreover, the model yielded an area under curve (AUC) of 0.603 on the receiver operating characteristic curve (ROC)., Conclusions: This study showed us that YOLOv5 is nearly perfect for numbering teeth on periapical radiography. Although high success rates were achieved as a result of the study, it should not be forgotten that artificial intelligence currently only can be guides dentists for accurate and rapid diagnosis., Clinical Relevance: It is thought that dentists can accelerate the radiographic examination time and inexperienced dentists can reduce the error rate by using YOLOv5. Additionally, YOLOv5 can also be used in the education of dentistry students., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Aim: The aim of the present study was to evaluate the segmentation accuracy of the dentition by testing four open-source semi-automatic software programs., Materials and Methods: Twenty CBCT scans were selected to perform semi-automatic segmentation of the maxillary and mandibular dentition. The software programs tested were InVesalius, ITK-SNAP, 3D Slicer, and Seg3D. In addition, each tooth model was manually segmented using Mimics software; this was set as the gold standard (GS) reference of the investigation. A specific 3D imaging technology was used to perform the superimposition between the tooth models obtained with the semi-automatic software and the GS model as well as to perform the surface-to-surface matching analysis. The accuracy of semi-automatic segmentation was evaluated, calculating the volumetric mean differences (mean bias and limits of agreement) and the percentage of matching of the tooth models compared with the manual segmentation (GS). Qualitative assessments were performed using color-coded maps. All data were statistically analyzed to perform comparisons between the investigated software programs., Results: Statistically significant differences were found in the volumetric and matching percentage data (P 0.05). InVesalius was the most accurate software program for 3D rendering of the dentition, with a volumetric bias (Mimics software) ranging from 4.59 to 85.79 mm3, while ITK-SNAP showed the highest volumetric bias, ranging from 30.22 to 319.83 mm3. The mismatched area was mainly located at the radicular tooth region. The volumetric data showed excellent inter-software reliability, with coefficient values ranging from 0.951 to 0.997., Conclusion: Different semi-automatic software algorithms could generate different patterns of inaccuracy error in the segmentation of teeth.

This study aimed to explore the clinical applicability of the shade-matching function in intraoral scanners. This study measured the tooth colors of maxillary anterior dentitions of 83 adults using visual matching, a spectrophotometer, and a scanner according to two color systems: VITA Classical (VC) and VITA 3D-Master (V3D). Agreement between each method was assessed by weighted Cohen's kappa coefficient (K W , α = 0.05). For V3D, the overall agreement between the scanner and spectrophotometer (K W = 0.498) was higher than that between the scanner and visual matching (K W = 0.473). Similarly, the agreement between the scanner and spectrophotometer (K W = 0.283) was higher than that between a scanner and visual matching (K W = 0.140) for VC. Regarding tooth position, the highest agreement between the scanner and spectrophotometer was observed on the right central incisor (K W = 0.542) for V3D. Tooth color measurement with a scanner was comparable to that with a spectrophotometer, especially on the central incisors when using the VITA 3D-Master system. A scanner could serve as an alternative to a spectrophotometer for shade selection. However, color matching should still be visually verified., (© 2024. The Author(s).)

Background: Recently, deep learning has been increasingly applied in the field of dentistry. The aim of this study is to develop a model for the automatic segmentation, numbering, and state assessment of teeth on panoramic radiographs., Methods: We created a dual-labeled dataset on panoramic radiographs for training, incorporating both numbering and state labels. We then developed a fusion model that combines a YOLOv9-e instance segmentation model with an EfficientNetv2-l classification model. The instance segmentation model is used for tooth segmentation and numbering, whereas the classification model is used for state evaluation. The final prediction results integrate tooth position, numbering, and state information. The model's output includes result visualization and automatic report generation., Results: Precision, Recall, mAP50 (mean Average Precision), and mAP50-95 for the tooth instance segmentation task are 0.989, 0.955, 0.975, and 0.840, respectively. Precision, Recall, Specificity, and F1 Score for the tooth classification task are 0.943, 0.933, 0.985, and 0.936, respectively., Conclusions: This fusion model is the first to integrate automatic dental segmentation, numbering, and state assessment. It provides highly accurate results, including detailed visualizations and automated report generation., (© 2024. The Author(s).)

Objective: The purpose of the present study was to verify the diagnostic performance of an AI system for the automatic detection of teeth, caries, implants, restorations, and fixed prosthesis on panoramic radiography., Methods: This is a cross-sectional study. A dataset comprising 1000 panoramic radiographs collected from 500 adult patients was analyzed by an AI system and compared with annotations provided by two oral and maxillofacial radiologists., Results: A strong correlation (R > 0.5) was observed between AI perception and observers 1 and 2 in carious teeth (0.691-0.878), implants (0.770-0.952), restored teeth (0.773-0.834), teeth with fixed prostheses (0.972-0.980), and missing teeth (0.956-0.988)., Discussion: Panoramic radiographs are commonly used for diagnosis and treatment planning. However, they often suffer from artifacts, distortions, and superimpositions, leading to potential misinterpretations. Thus, an automated detection system is required to tackle these challenges. Artificial intelligence (AI) has revolutionized various fields, including dentistry, by enabling the development of intelligent systems that can assist in complex tasks such as diagnosis and treatment planning., Conclusion: The automatic detection by the AI system was comparable to oral radiologists and may be useful for automatic identifications in panoramic radiographs. These findings signify the potential for AI systems to enhance diagnostic accuracy and efficiency in dental practices, potentially reducing the likelihood of diagnostic errors caused by unexperienced professionals., Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Objectives: Comparison of maxillary, mandibular, dental crown and root mineral density in human skeletons identified in historical excavations with today's human maxillary, mandibular dental crown and root., Methods: After the skull images were obtained, four groups were created: maxilla 1, mandible 1 from the old period, maxilla 2 from the images taken from today's patients, and mandible 2 from the images taken from today's patients. Seventeen skeletons were previously classified as young age, middle age, and older age. Among the archive tomography images, 17 images from young (15-35), middle (36-56) and older (57 and over) age images were included in the study. HU value of the desired region was calculated automatically by the device using Region of interest (ROI)., Results: In this study, 34 jaws and 68 teeth were evaluated, including 7 mandibles and 10 maxillae from the late Roman period, and 8 mandibles and 9 maxillae from today's people. The average HU value of the crowns of mandibular anterior teeth from the late Roman period was measured as 2406.0. The average HU value of the crowns of maxillary anterior teeth was found to be 3383.8. In the posterior aspect, the average crown HU value measured in the maxilla was found to be higher than that in the mandible., Conclusions: The finding showed that the density of dental tissues of ancient people, such as enamel and dentin forming the crown, and cementum and dentin forming the root, was significantly higher than today's people., Competing Interests: Conflict of interests The authors declare that they have no conflict of interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Tooth-supported surgical guides have demonstrated superior accuracy compared with bone-supported guides. This study aimed to modify the fabrication of tooth-supported guides for compatibility with tumor resection procedures and investigate their accuracy., Methods: Patients with tumors who underwent osteotomy with the assistance of modified tooth- or bone-supported surgical guides were included. Virtual surgical planning (VSP) was employed to align three dimensional (3D) models extracted from intraoperative computed tomography (CT) images. The distances and angular deviations between the actual osteotomy plane and preoperative plane were recorded. A comparative analysis of osteotomy discrepancies between tooth-supported and bone-supported guides, as well as among tooth-supported guides based on CT, cone-beam CT (CBCT), or intraoral scanner (IOS) was conducted. The factors influencing the precision of the guides were analyzed., Results: Sixty patients with 81 resection planes were included in this study. In the tooth-supported group, the mean deviations in the osteotomy plane and angle were 1.39 mm and 4.30°, respectively, whereas those of the bone-supported group were 2.16 mm and 4.95°. In the tooth-supported isotype guide groups, the mean deviations of the osteotomy plane were 1.39 mm, 1.47 mm, 1.23 mm across CT, CBCT, and IOS, respectively. The accuracy of the modified tooth-supported guides remained consistent regardless of number and position of the teeth supporting the guide and location of the osteotomy lines., Conclusions: The findings indicate that the modified tooth-supported surgical guides demonstrated high accuracy in the maxillofacial region, contributing to a reduction in the amount of surgically detached soft tissue., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

Dental radiography is widely used in dental practices and offers a valuable resource for the development of AI technology. Consequently, many researchers have been drawn to explore its application in different areas. The current systematic review was undertaken to critically appraise developments and performance of artificial intelligence (AI) models designed for tooth numbering and detection using dento-maxillofacial radiographic images. In order to maintain the integrity of their methodology, the authors of this systematic review followed the diagnostic test accuracy criteria outlined in PRISMA-DTA. Electronic search was done by navigating through various databases such as PubMed, Scopus, Embase, Cochrane, Web of Science, Google Scholar, and the Saudi Digital Library for the articles published from 2018 to 2023. Sixteen articles that met the inclusion exclusion criteria were subjected to risk of bias assessment using QUADAS-2 and certainty of evidence was assessed using GRADE approach.AI technology has been mainly applied for automated tooth detection and numbering, to detect teeth in CBCT images, to identify dental treatment patterns and approaches. The AI models utilised in the studies included exhibited a highest precision of 99.4% for tooth detection and 98% for tooth numbering. The use of AI as a supplementary diagnostic tool in the field of dental radiology holds great potential., Competing Interests: Conflict of interest None disclosed., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Objectives: The fluorescence-aided identification technique (FIT) is based on the fluorescence properties of dental materials, specifically the intensity of their fluorescence compared to the autofluorescence of hard dental substances; this creates a perceived contrast between dental material and tooth. However, no studies to date have determined the extent to which the fluorescence intensity of tooth-colored dental materials must differ from that of natural autofluorescence to ensure reliable visual detection. The aim of this study was therefore to determine, for the first time, how pronounced the difference between fluorescence intensity and autofluorescence must be to reliably identify tooth-colored material., Methods: Ten dentists assessed six different resin-based composite (RBC) samples of varying fluorescence intensity placed in the cavities of ten extracted teeth under standardized fluorescence-exciting illumination. The outcome variable was fluorescence perceptibility. Their assessments of the outcome variable were compared with measurements of the fluorescence intensities of the RBCs and the surrounding dental hard tissues, which were expressed as a fluorescence intensity ratio. Demographic data of the participants, including age, gender, and professional experience, were also recorded., Results: No significant differences were found for visual fluorescence perceptibility in relation to the explanatory variables of gender (p = 0.14), age (p = 0.13), and professional experience (p = 0.34). In contrast, the fluorescence intensity ratio was significantly different (p < 0.0001)., Significance: For both clinicians and manufacturers, fluorescence intensity levels are important when selecting or developing FIT-compatible materials. Our results suggest that the fluorescence intensity levels of dental materials should be no more than 75% and no less than 200% of tooth natural autofluorescence to ensure reliable detection of tooth-colored materials., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Although the measurement of tooth quality is necessary for precise prediction of caries formation, typical measurement methods include tooth-hardness measurements and absorption spectroscopy, which generally use infrared light irradiation. These methods are destructive or invasive, and obtaining two-dimensional information in the oral cavity is difficult. Mid-infrared emissions from the surface of an object reflect intrinsic vibrations of molecules in the object. In this study, a mid-infrared passive spectroscopic imaging system was developed using an inexpensive uncooled microbolometer array sensor with an optimized multi-slit, which eliminated the cancellation of interference intensities between two adjacent emission points, to obtain two-dimensional information from an object without external infrared light irradiation. First, the feasibility of obtaining two-dimensional information on tooth quality using the proposed system was examined, and emission spectra attributed to phosphate ions in hydroxyapatite (HAp), the main component of enamel, were successfully obtained from bovine teeth. Further, the hardness of bovine teeth was measured, and a correlation ( R 2 = 0.8067) between the Vickers hardness and peak area ratio of phosphate ions assigned to the crystalline and amorphous phases of a tooth was established. Additionally, tooth-hardness visualization in a non-contact manner was demonstrated as two-dimensional information using the obtained regression equation.

Infrared thermal imaging (IRT) remotely and contactless maps the temperature on the examined surface, recording the distribution of infrared radiation emitted by each body whose temperature is higher than absolute zero. The aim of the study was to evaluate the usefulness of thermography in the assessment of asymptomatic infection foci in patients with high systemic infection. The 150 cases diagnosed based on roentgenograms, divided into 6 groups of diagnosed odontogenic lesions, along with a control group. Thermal imaging was performed with a FLIR Systems T1020 thermal camera. Thermal image analysis was performed using ThermaCAM Researcher Pro 2.10, MS Office Excel 2022 and Statistica 10. The periapical areas of selected dead teeth were selected as areas of interest. The Mann Whitney's U test showed statistically significant (p < 0.001) differences in average temperature between each patient's and healthy group. Depper's analysis showed statistical significance also between the ZM and BZ groups (p = 0.004). Moreover, obtained results may also suggest that thermal imaging can be useful in identify odontogenic infection foci. The thermal asymmetry of periapical tissues of teeth differentiates dead from living teeth, as well as individual pathologies related to the process of gangrenous pulp decay. Thermographic mapping is a promising diagnostic technique that can detect asymptomatic inflammations that carry the risk of infection of the entire body., (© 2024. The Author(s).)

Background: This study aims to evaluate the performance of a deep learning system for the evaluation of tooth development stages on images obtained from panoramic radiographs from child patients., Methods: The study collected a total of 1500 images obtained from panoramic radiographs from child patients between the ages of 5 and 14 years. YOLOv5, a convolutional neural network (CNN)-based object detection model, was used to automatically detect the calcification states of teeth. Images obtained from panoramic radiographs from child patients were trained and tested in the YOLOv5 algorithm. True-positive (TP), false-positive (FP), and false-negative (FN) ratios were calculated. A confusion matrix was used to evaluate the performance of the model., Results: Among the 146 test group images with 1022 labels, there were 828 TPs, 308 FPs, and 1 FN. The sensitivity, precision, and F1-score values of the detection model of the tooth stage development model were 0.99, 0.72, and 0.84, respectively., Conclusions: In conclusion, utilizing a deep learning-based approach for the detection of dental development on pediatric panoramic radiographs may facilitate a precise evaluation of the chronological correlation between tooth development stages and age. This can help clinicians make treatment decisions and aid dentists in finding more accurate treatment options., (© 2024. The Author(s).)

Introduction: The accuracy of tooth segmentation in intraoral scans is crucial for performing virtual setups and appliance fabrication. Hence, the objective of this study was to estimate and compare the accuracy of automated tooth segmentation generated by the artificial intelligence of dentOne software (DIORCO Co, Ltd, Yongin, South Korea) and Medit Ortho Simulation software (Medit Corp, Seoul, South Korea)., Methods: Twelve maxillary and mandibular pretreatment dental scan sets comprising 286 teeth were collected for this investigation from the archives of the Department of Orthodontics, Faculty of Dentistry, Alexandria University. The scans were imported as standard tessellation language files into both dentOne and Medit Ortho Simulation software. Automatic segmentation was run on each software. The number of successfully segmented teeth vs failed segmentations was recorded to determine the success rate of automated segmentation of each program. Evaluation of success and/or failure was based on the software's identification of the teeth and the quality of the segmentation. The mesiodistal tooth width measurements after segmentation using both tested software programs were compared with those measured on the unsegmented scan using Meshmixer software (Autodesk, San Rafael, Calif). The unsegmented scans served as the reference standard., Results: A total of 288 teeth were examined. Successful identification rates were 99% and 98.3% for Medit and dentOne, respectively. Success rates of segmenting the lingual surfaces of incisors were significantly higher in Medit than in dentOne (93.7% vs 66.7%, respectively; P <0.001). DentOne overestimated the mesiodistal width of canines (0.11 mm, P = 0.032), premolars (0.22 mm, P < 0.001), and molars (0.14 mm, P = 0.043) compared with the reference standard, whereas Medit overestimated the mesiodistal width of premolars only (0.13 mm, P = 0.006). Bland-Altman plots showed that mesiodistal tooth width agreement limits exceeded 0.2 mm between each software and the reference standard., Conclusions: Both artificial intelligence-segmentation software demonstrated acceptable accuracy in tooth segmentation. There is a need for improvement in segmenting incisor lingual tooth surfaces in dentOne. Both software programs tended to overestimate the mesiodistal widths of segmented teeth, particularly the premolars. Artificial intelligence-segmentation needs to be manually adjusted by the operator to ensure accuracy. However, this still does not solve the problem of proximal surface reconstruction by the software., (Copyright © 2024 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.)

Dental anatomy is an essential skill for human identification in forensic odontology. With the advent of technology enabling virtual autopsies, there is scope for virtual consultation by forensic odontologists especially when the expertise is unavailable but needed in zones of conflict or disasters. This study aimed to investigate potential benefits and challenges of identifying intact and damaged teeth from 3D scanned digital models. Ten 3D tooth models - nine permanent and deciduous human teeth and one animal tooth were uploaded on a hosting platform. A 3-part survey was circulated among 60 forensic odontologists with questions about demography (P1), tooth identification of the scanned 3D models (P2) and the perceived usefulness of 3D models for tooth identification (P3). This was the first time that a survey required the identification of individual human teeth (intact or not) and animal tooth combined. The response rate for study participation was 58%. Substantial agreement among participants was seen in the determination of tooth classification (i.e., molars, premolars) or non-human and tooth within the same tooth class (i.e., lateral incisors, second molar) (both k=0.61). The least agreement (k=0.21) was seen in identification of tooth according to the FDI notation with a mean accuracy of 0.34. While most responders correctly identified the animal tooth, most incorrect responses were seen in the identification of the intact third molar. While 3D-scanned teeth have the potential to be identified virtually, forensic odontologists should continuously test their skills in tooth morphology and dental anatomy of humans (damaged or not) and animals., (Copyright © 2024 The Author(s). Published by Elsevier Masson SAS.. All rights reserved.)

Objective: The imbalanced nature of real-world datasets is an ongoing challenge in the field of machine and deep learning. In medicine and in dentistry, most data samples represent patients not affected by pathologies, and on imagery, pathologic image areas are often smaller than healthy ones. Selecting suitable loss functions during deep learning is essential and may help to overcome the resulting imbalance. We assessed six different loss functions for one exemplary task, tooth structure segmentation on bitewing radiographs, for their performance., Methods: Six different loss functions (Focal Loss, Dice Loss, Tversky Loss and hybrid losses of Cross-Entropy and Dice Loss, Focal and Dice Loss, Focal and Generalized Dice Loss) were compared on a tooth structure segmentation task of 1,625 bitewing radiographs. Training was performed using three different model architectures (U-Net, Linknet, DeepLavbV3+) over a 5-fold cross-validation. Tooth structures consisted of the classes (occurrence in% of samples/captures areas measured on pixel level) enamel (100 %/25 %), dentin (100 %/50 %), root canal (100 %/10 %), filling (81 %/8 %) and crown (28 %/5 %)., Results: Hybrid loss functions significantly outperformed standalone ones and provided robust results over the different architectures for the classes enamel, dentin, root canal and filling. Specifically, the Dice Focal loss reached high performance to conquer both image level and pixel level class imbalance, respectively., Clinical Significance: In dental use cases it is often important to predict minority classes such as pathologies accurately. Using specific loss function may be an effective strategy to overcome data imbalance when training deep learning models., Competing Interests: Declaration of competing interest FS and JK are co-founders of the startup dentalXrai GmbH. dentalXrai GmbH did not have any role in conceiving, conducting or reporting this study. The authors are solely responsible for the contents of this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Dental identification is a pillar of disaster victim identification (DVI). Dental identification is accurate, efficient, inexpensive, and accepted in courts of law. The (known) antemortem (AM) dental charts and radiographic images acquired from the dentist of the missing person are evaluated, processed, and compared to post mortem (PM) findings present in the dentition or fragments of the dentition of the deceased individual. These comparisons evaluate and assess individuating restorative dental work, dental anatomical areas of concordance, spatial relationships of teeth one to another, and occasionally calculate the degree of "uniqueness" of either or both of the AM and PM dentition compared to known population databases. In a multiple fatality incident, odontologists may utilize age stratification to assist other means of identification. Computer comparison algorithms using recorded data can indicate possible matches between AM and PM data sets. Following clinical assessment, collection of post mortem tooth specimens for DNA profiling generation may be undertaken. This paper will highlight modern and efficient use of these tools. The framework for how dental identification in these incidents is currently managed is presented. The authors propose a change to this approach that moves away from interpretive subjective assessment toward comparisons based largely on objective data. The aim of this paper is to highlight the benefits of minimizing subjective decisions and maximizing objective data in the dental DVI process while simultaneously reducing risk to clinical personnel and minimizing costs by reducing the number of clinicians required onsite., (© 2023 American Academy of Forensic Sciences.)

Objective: To propose a novel neural network to achieve tooth instance segmentation and recognition based on cone-beam computed tomography (CBCT) voxel data., Methods: The proposed methods included three different convolutional neural network models. The architecture was based on the Resnet module and built according to the structure of "Encoder-Decoder" and U-Net. The CBCT image was de-sampled and a fixed-size region of interest (ROI) containing all the teeth was determined. ROI would first through a two-branch "encoder and decoder" structure of the network, the network could predict each voxel in the input data of the spatial embedding. The post-processing algorithm would cluster the prediction results of the relevant spatial location information according to the two-branch network to realize the tooth instance segmentation. The tooth position identification was realized by another U-Net model based on the multi-classification segmentation task. According to the predicted results of the network, the post-processing algorithm would classify the tooth position according to the voting results of each tooth instance segmentation. At the original spatial resolution, a U-Net network model for the fine-tooth segmentation was trained using the region corresponding to each tooth as the input. According to the results of instance segmentation and tooth position identification, the model would process the correspon-ding positions on the high-resolution CBCT images to obtain the high-resolution tooth segmentation results. In this study, CBCT data of 59 cases with simple crown prostheses and implants were collected for manual labeling as the database, and statistical indicators were evaluated for the prediction results of the algorithm. To assess the performance of tooth segmentation and classification, instance Dice similarity coefficient (IDSC) and the average Dice similarity coefficient (ADSC) were calculated., Results: The experimental results showed that the IDSC was 89.35%, and the ADSC was 84. 74%. After eliminating the data with prostheses artifacts, the database of 43 samples was generated, and the performance of the training network was better, with 90.34% for IDSC and 87.88% for ADSC. The framework achieved excellent performance on tooth segmentation and identification. Voxels near intercuspation surfaces and fuzzy boundaries could be separated into correct instances by this framework., Conclusions: The results show that this method can not only successfully achieve 3D tooth instance segmentation but also identify all teeth notation numbers accurately, which has clinical practicability.

Purpose: The established automatic AI tooth segmentation algorithm was used to achieve rapid and automatic tooth segmentation from CBCT images. The three-dimensional data obtained by oral scanning of real isolated teeth were used as the gold standard to verify the accuracy of the algorithm., Methods: Thirty sets of CBCT data and corresponding 59 isolated teeth were collected from Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine. The three-dimensional tooth data in CBCT images were segmented by the algorithm. The digital information obtained by scanning the extracted teeth after processing was used as the gold standard. In order to compare the difference between the segmentation results and the scanning results of the algorithm. The Dice coefficient(Dice), sensitivity (Sen) and average symmetric surface distance (ASSD) were selected to evaluate the segmentation accuracy of the algorithm. The intra-class correlation coefficient(ICC) was used to evaluate the differences in length, area, and volume between the single tooth obtained by the AI system and the digital isolated tooth. Due to the existence of CBCT with different resolution, ANOVA was used to analyze the differences between groups with different resolution, and SNK method was used to compare them between two groups. SPSS 25.0 software package was used to analyze the data., Results: After comparing the segmentation results with the in vitro dental scanning results, the average Dice value was (94.7±1.88)%, the average Sen was (95.8±2.02)%, and the average ASSD was (0.49±0.12) mm. By comparing the length, area and volume of a single tooth obtained by the digital isolated tooth and the AI system, the ICC values of the intra-group correlation coefficients were 0.734, 0.719 and 0.885, respectively. The single tooth divided by the AI system has a good consistency with the digital model in evaluating the length, area and volume, but the segmentation results were still different from the real situation in terms of specific values. The smaller the voxel of CBCT, the higher the resolution, the better the segmentation results., Conclusions: The CBCT tooth segmentation algorithm established in this study can accurately achieve the tooth segmentation of the whole dentition in CBCT at all resolutions. The improvement of CBCT resolution ratio can make the algorithm more accurate. Compared with the current segmentation algorithms, our algorithm has better performance. Compared with the real situation, there are still some differences, and the algorithm needs to be further improved and verified.

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., (© 2024. The Author(s).)

Objectives: Segmentation of anatomical structures on dento-maxillo-facial (DMF) computed tomography (CT) or cone beam computed tomography (CBCT) scans is increasingly needed in digital dentistry. The main aim of this research was to propose and evaluate a novel open source tool called DentalSegmentator for fully automatic segmentation of five anatomical structures on DMF CT and CBCT scans: maxilla/upper skull, mandible, upper teeth, lower teeth, and the mandibular canal., Methods: A retrospective sample of 470 CT and CBCT scans was used as a training/validation set. The performance and generalizability of the tool was evaluated by comparing segmentations provided by experts and automatic segmentations in two hold-out test datasets: an internal dataset of 133 CT and CBCT scans acquired before orthognathic surgery and an external dataset of 123 CBCT scans randomly sampled from routine examinations in 5 institutions., Results: The mean overall results in the internal test dataset (n = 133) were a Dice similarity coefficient (DSC) of 92.2 ± 6.3 % and a normalised surface distance (NSD) of 98.2 ± 2.2 %. The mean overall results on the external test dataset (n = 123) were a DSC of 94.2 ± 7.4 % and a NSD of 98.4 ± 3.6 %., Conclusions: The results obtained from this highly diverse dataset demonstrate that this tool can provide fully automatic and robust multiclass segmentation for DMF CT and CBCT scans. To encourage the clinical deployment of DentalSegmentator, the pre-trained nnU-Net model has been made publicly available along with an extension for the 3D Slicer software., Clinical Significance: DentalSegmentator open source 3D Slicer extension provides a free, robust, and easy-to-use approach to obtaining patient-specific three-dimensional models from CT and CBCT scans. These models serve various purposes in a digital dentistry workflow, such as visualization, treatment planning, intervention, and follow-up., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests G. Dubois and T. Schouman declared relationships with the following company: Materialise. The other authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article, (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Computer vision falls under the broad umbrella of artificial intelligence that mimics human vision and plays a vital role in dental imaging. Dental practitioners visualize and interpret teeth, and the structure surrounding the teeth and detect abnormalities by manually examining various dental imaging modalities. Due to the complexity and cognitive difficulty of comprehending medical data, human error makes correct diagnosis difficult. Automated diagnosis may be able to help alleviate delays, hasten practitioners' interpretation of positive cases, and lighten their workload. Several medical imaging modalities like X-rays, CT scans, color images, etc. that are employed in dentistry are briefly described in this survey. Dentists employ dental imaging as a diagnostic tool in several specialties, including orthodontics, endodontics, periodontics, etc. In the discipline of dentistry, computer vision has progressed from classic image processing to machine learning with mathematical approaches and robust deep learning techniques. Here conventional image processing techniques solely as well as in conjunction with intelligent machine learning algorithms, and sophisticated architectures of dental radiograph analysis employ deep learning techniques. This study provides a detailed summary of several tasks, including anatomical segmentation, identification, and categorization of different dental anomalies with their shortfalls as well as future perspectives in this field., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Age estimation methods through developmental atlases are simplified by image comparison using radiographs. Atlas-based methods have been utilized to create population-specific charts addressing population diversity. To tackle the absence of a dedicated atlas for the Brazilian population, the objective of the present study was to create a new atlas for dental development in Brazil based on the London Atlas chart. The new atlas (RP Atlas) is based on the median of tooth development, eruption, and resorption in individuals aged 5 to 23. The sample (567 individuals) was divided between females (n = 290) and males (n = 277). The RP Atlas was handcrafted, scanned, colored, and clustered in two atlases divided by sex. The development of a specific atlas for the Brazilian population has the potential to enhance the accuracy of age estimation in forensic scenarios, notably in a mixed country like Brazil. Further research is needed to assess the suitability of the newly developed atlas for the Brazilian population., (© 2024 Wiley Periodicals LLC.)

Orthodontic treatment is a lengthy process that requires regular in-person dental monitoring, making remote dental monitoring a viable alternative when face-to-face consultation is not possible. In this study, we propose an improved 3D teeth reconstruction framework that automatically restores the shape, arrangement, and dental occlusion of upper and lower teeth from five intra-oral photographs to aid orthodontists in visualizing the condition of patients in virtual consultations. The framework comprises a parametric model that leverages statistical shape modeling to describe the shape and arrangement of teeth, a modified U-net that extracts teeth contours from intra-oral images, and an iterative process that alternates between finding point correspondences and optimizing a compound loss function to fit the parametric teeth model to predicted teeth contours. We perform a five-fold cross-validation on a dataset of 95 orthodontic cases and report an average Chamfer distance of 1.0121 mm 2 and an average Dice similarity coefficient of 0.7672 on all the test samples in the cross-validation, demonstrating a significant improvement compared with the previous work. Our teeth reconstruction framework provides a feasible solution for visualizing 3D teeth models in remote orthodontic consultations.

To the trained eye, every tooth is different. An in-depth understanding of dental anatomy in cross-sectional images is a fundamental basic skill required when interpreting cone beam computed tomography (CBCT) scans. The conventional orthogonal planes of axial, coronal, and sagittal are not always the best planes in which to assess teeth because every tooth is at a slightly different angle to all these planes. In this chapter, the author demonstrates the characteristic anatomy of upper and lower incisors, canines, premolars, and molars in longitudinal and transverse sections, using carefully uprighted images. Some of these teeth are also shown in transverse section at various levels in the crown and root. The chronology of permanent teeth is shown. Developing teeth are discussed and incremental growth lines are mentioned.The author describes three well-known dental notation systems (Palmer, FDI, hybrid).There are four videos accompanying this chapter, including one fascinating video showing a scroll-through of all the cross-arch sections of a synthetic panoramic (curved multiplanar reconstruction) image that includes all the upper and lower teeth in a 16-year-old., (© 2024. Springer Nature Switzerland AG 2023. Republished 2024.)

The incorporation of automatic segmentation methodologies into dental X-ray images refined the paradigms of clinical diagnostics and therapeutic planning by facilitating meticulous, pixel-level articulation of both dental structures and proximate tissues. This underpins the pillars of early pathological detection and meticulous disease progression monitoring. Nonetheless, conventional segmentation frameworks often encounter significant setbacks attributable to the intrinsic limitations of X-ray imaging, including compromised image fidelity, obscured delineation of structural boundaries, and the intricate anatomical structures of dental constituents such as pulp, enamel, and dentin. To surmount these impediments, we propose the Deformable Convolution and Mamba Integration Network, an innovative 2D dental X-ray image segmentation architecture, which amalgamates a Coalescent Structural Deformable Encoder, a Cognitively-Optimized Semantic Enhance Module, and a Hierarchical Convergence Decoder. Collectively, these components bolster the management of multi-scale global features, fortify the stability of feature representation, and refine the amalgamation of feature vectors. A comparative assessment against 14 baselines underscores its efficacy, registering a 0.95% enhancement in the Dice Coefficient and a diminution of the 95th percentile Hausdorff Distance to 7.494.

Background: Tooth segmentation on intraoral scanned (IOS) data is a prerequisite for clinical applications in digital workflows. Current state-of-the-art methods lack the robustness to handle variability in dental conditions. This study aims to propose and evaluate the performance of a convolutional neural network (CNN) model for automatic tooth segmentation on IOS images., Methods: A dataset of 761 IOS images (380 upper jaws, 381 lower jaws) was acquired using an intraoral scanner. The inclusion criteria included a full set of permanent teeth, teeth with orthodontic brackets, and partially edentulous dentition. A multi-step 3D U-Net pipeline was designed for automated tooth segmentation on IOS images. The model's performance was assessed in terms of time and accuracy. Additionally, the model was deployed on an online cloud-based platform, where a separate subsample of 18 IOS images was used to test the clinical applicability of the model by comparing three modes of segmentation: automated artificial intelligence-driven (A-AI), refined (R-AI), and semi-automatic (SA) segmentation., Results: The average time for automated segmentation was 31.7 ± 8.1 s per jaw. The CNN model achieved an Intersection over Union (IoU) score of 91%, with the full set of teeth achieving the highest performance and the partially edentulous group scoring the lowest. In terms of clinical applicability, SA took an average of 860.4 s per case, whereas R-AI showed a 2.6-fold decrease in time (328.5 s). Furthermore, R-AI offered higher performance and reliability compared to SA, regardless of the dentition group., Conclusions: The 3D U-Net pipeline was accurate, efficient, and consistent for automatic tooth segmentation on IOS images. The online cloud-based platform could serve as a viable alternative for IOS segmentation., (© 2024. The Author(s).)

Objectives: In the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic detection, segmentation, and numbering of deciduous and permanent teeth in mixed dentition pediatric patients based on PRs., Methods: A total of 3854 mixed pediatric patients PRs were labelled for deciduous and permanent teeth using the CranioCatch labeling program. The dataset was divided into three subsets: training (n = 3093, 80% of the total), validation (n = 387, 10% of the total) and test (n = 385, 10% of the total). An artificial intelligence (AI) algorithm using YOLO-v5 models were developed., Results: The sensitivity, precision, F-1 score, and mean average precision-0.5 (mAP-0.5) values were 0.99, 0.99, 0.99, and 0.98 respectively, to teeth detection. The sensitivity, precision, F-1 score, and mAP-0.5 values were 0.98, 0.98, 0.98, and 0.98, respectively, to teeth segmentation., Conclusions: YOLO-v5 based models can have the potential to detect and enable the accurate segmentation of deciduous and permanent teeth using PRs of pediatric patients with mixed dentition., (© 2024. The Author(s).)

Objectives: The aim of this study was to investigate the impact of birth weight on tooth development in children aged 7-8 years., Materials and Methods: This retrospective cohort study comprised 75 children born at Bint Al-Huda Hospital, Bojnurd, in 2013-2014. The children were categorized into three groups based on their birth weight: Normal Birth Weight (NBW), Low Birth Weight (LBW), and Very Low Birth Weight (VLBW). Panoramic radiographs were taken for orthodontic examination, and Demirjian's 8-teeth method was employed to determine dental age. The study compared dental and chronological age within each group. Data analysis utilized SPSS software version 26, employing One-way ANOVA and chi-square tests. Statistical significance was set at P ≤ 0.05., Results: The mean difference in dental and chronological age for Very Low Birth Weight (VLBW) children was 0.22 ± 0.44 years, for Low Birth Weight (LBW) children it was 0.19 ± 0.45 years, and for Normal Birth Weight (NBW) children, it was 0.08 ± 0.46 years. Although the mean difference decreased with increasing birth weight, this trend did not achieve statistical significance (P = 0.55). Furthermore, no significant differences were observed between the weight groups (P = 0.529) or genders (P = 0.191)., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: The stage of tooth formation is one of the most reliable indicators for predicting a patient's developmental age by radiographs. This study compared the accuracy of three distinct dental age estimation methods (Demirjian, Nolla, and Willems) in children aged 3-17 in the northern Iranian population. Methods: This cross-sectional study examined panoramic radiographs of 434 children aged 3-17 from Mazandaran Province, Iran, who had teeth 31-37 present on the left mandible. This study employed the Demirjian, Nolla, and Willems methods to estimate the dental age of the sample and compare it with the chronological age. The data were analyzed using SPSS v16. A paired t -test was used to compare chronological and dental ages. The Pearson correlation was used to correlate the chronological and dental ages. The errors of different methods were compared using the Wilcoxon test. P values < 0.05 were considered significant for all tests except Wilcoxon. For Wilcoxon, a P value < 0.017 was considered significant. Results: The three methods presented differing mean estimated ages. The Demirjian method delivered the highest mean, and all three methods differed significantly when compared in pairs. The results showed that the Demirjian method overestimated chronological age by 0.25 years ( P < 0.001) in girls and 0.09 years ( P = 0.28) in boys. The Willems method underestimated chronological age by 0.05 years ( P = 0.47) in girls and 0.12 years ( P = 0.13) in boys. The Nolla method underestimated chronological age by 0.41 years ( P < 0.001) in girls and 0.40 years ( P < 0.001) in boys. The accuracy of each method varied with the patient's age. Conclusion: According to the findings, the Willems method outperformed the Demirjian method, and the Demirjian method exceeded the Nolla method for estimating dental age in Iranian children aged 3-17. Overall, the Demirjian method overestimated the age of the study population, whereas the other two underestimated it., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2024 Benjamin Pliska et al.)

To the trained eye, every tooth is different. An in-depth understanding of dental anatomy in cross-sectional images is a fundamental basic skill required when interpreting cone beam computed tomography (CBCT) scans. The conventional orthogonal planes of axial, coronal, and sagittal are not always the best planes in which to assess teeth because every tooth is at a slightly different angle to all these planes. In this chapter, the author demonstrates the characteristic anatomy of upper and lower incisors, canines, premolars, and molars in longitudinal and transverse sections, using carefully uprighted images. Some of these teeth are also shown in transverse section at various levels in the crown and root. The chronology of permanent teeth is shown. Developing teeth are discussed and incremental growth lines are mentioned.The author describes three well-known dental notation systems (Palmer, FDI, hybrid).There are four videos accompanying this chapter, including one fascinating video showing a scroll-through of all the cross-arch sections of a synthetic panoramic (curved multiplanar reconstruction) image that includes all the upper and lower teeth in a 16-year-old., (© 2024. Springer Nature Switzerland AG 2023. Republished.)

This study evaluated 10-year secular changes in dental maturity and dental development among Korean children. A retrospective analysis of panoramic radiograph samples from Korean children (4-16 years old) taken in 2010 and 2020 was conducted. The 2010 group consisted of 3491 radiographs (1970 boys and 1521 girls), and the 2020 group included 5133 radiographs (2825 boys and 2308 girls). Using Demirjian's method, dental maturity scores and dental developmental stages were assessed. For intra-observer reliability, Weighted Cohen's kappa was used, and Mann-Whitney U tests were performed to compare the 2020 and 2010 groups. A slight acceleration in dental maturity was observed in both boys and girls, with the difference being more noticeable in boys at an earlier age. Statistically significant differences were noted at ages 4, 5 and 7 for boys, and at age 6 for girls. Despite these differences, the individual dental development stages of 2020 and 2010 showed inconsistent trends with limited differences. Generally, girls demonstrate more advanced dental maturity than boys. A slight acceleration in Korean children's dental maturity was observed over a 10-year period when comparing the 2020 groups to the 2010 groups., Competing Interests: The authors declare no conflict of interest. Hong-Keun Hyun is serving as one of the Editorial Board members of this journal. We declare that Hong-Keun Hyun had no involvement in the peer review of this article and has no access to information regarding its peer review. Full responsibility for the editorial process for this article was delegated to LAAA., (©2024 The Author(s). Published by MRE Press.)

Objectives: This systematic review and meta-analysis aimed to assess the current performance of artificial intelligence (AI)-based methods for tooth segmentation in three-dimensional cone-beam computed tomography (CBCT) images, with a focus on their accuracy and efficiency compared to those of manual segmentation techniques., Data: The data analyzed in this review consisted of a wide range of research studies utilizing AI algorithms for tooth segmentation in CBCT images. Meta-analysis was performed, focusing on the evaluation of the segmentation results using the dice similarity coefficient (DSC)., Sources: PubMed, Embase, Scopus, Web of Science, and IEEE Explore were comprehensively searched to identify relevant studies. The initial search yielded 5642 entries, and subsequent screening and selection processes led to the inclusion of 35 studies in the systematic review. Among the various segmentation methods employed, convolutional neural networks, particularly the U-net model, are the most commonly utilized. The pooled effect of the DSC score for tooth segmentation was 0.95 (95 %CI 0.94 to 0.96). Furthermore, seven papers provided insights into the time required for segmentation, which ranged from 1.5 s to 3.4 min when utilizing AI techniques., Conclusions: AI models demonstrated favorable accuracy in automatically segmenting teeth from CBCT images while reducing the time required for the process. Nevertheless, correction methods for metal artifacts and tooth structure segmentation using different imaging modalities should be addressed in future studies., Clinical Significance: AI algorithms have great potential for precise tooth measurements, orthodontic treatment planning, dental implant placement, and other dental procedures that require accurate tooth delineation. These advances have contributed to improved clinical outcomes and patient care in dental practice., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The segmentation of dental models is a crucial step in computer-aided diagnosis and treatment systems for oral healthcare. To address the issues of poor universality and under-segmentation in tooth segmentation techniques, an intelligent tooth segmentation method combining multiple seed region growth and boundary extension is proposed. This method utilized the distribution characteristics of negative curvature meshes in teeth to obtain new seed points and effectively adapted to the structural differences between the top and sides of teeth through differential region growth. Additionally, the boundaries of the initial segmentation were extended based on geometric features, which was effectively compensated for under-segmentation issues in region growth. Ablation experiments and comparative experiments with current state-of-the-art algorithms demonstrated that the proposed method achieved better segmentation of crowded dental models and exhibited strong algorithm universality, thus possessing the capability to meet the practical segmentation needs in oral healthcare.

In this study, we present the development of a fine structural human phantom designed specifically for applications in dentistry. This research focused on assessing the viability of applying medical computer vision techniques to the task of segmenting individual teeth within a phantom. Using a virtual cone-beam computed tomography (CBCT) system, we generated over 170,000 training datasets. These datasets were produced by varying the elemental densities and tooth sizes within the human phantom, as well as varying the X-ray spectrum, noise intensity, and projection cutoff intensity in the virtual CBCT system. The deep-learning (DL) based tooth segmentation model was trained using the generated datasets. The results demonstrate an agreement with manual contouring when applied to clinical CBCT data. Specifically, the Dice similarity coefficient exceeded 0.87, indicating the robust performance of the developed segmentation model even when virtual imaging was used. The present results show the practical utility of virtual imaging techniques in dentistry and highlight the potential of medical computer vision for enhancing precision and efficiency in dental imaging processes., (© 2024. The Author(s).)

Country-specific systematic reviews and meta-analyses have been proposed to compile the available literature and rank methods based on their performance for a target population. India is a country with a vast scientific literature on dental age estimation. This systematic review aimed to provide evidence to help the decision of experts regarding the method of choice for dental age estimation in India. The research protocol was registered in Open Science Framework. Literature Search was performed in Embase, LILACS, MedLine (via PubMed), SciELO, Scopus and Web of Science. Grey Literature was searched in Google Scholar and ProQuest. Observational cross-sectional studies that compared chronological and estimated ages using Demirjian (original [DEM] and Chaillet's modification [modified-DEM]) and Acharya (ACH) methods were included. JBI tool was used to assess the risk of bias. The search detected 9799 studies, out of which 56 were eligible (n=13,107 panoramic radiographs of Indian individuals). Low risk of bias was registered for 48 studies, while 8 presented a moderate risk of bias. The meta-analysis showed a standardized mean difference between chronological and estimated ages of -0.11 (95%CI: -0.29; 0.07), 0.74 (95%CI: 0.39; 1.09), and -0.01 (95%CI: -0.23; 0.22) years for DEM, modified-DEM and ACH, respectively. High heterogeneity (I 2 =88-93%) was observed across studies for all the methods, including subgroup analyses based on sex. This study ranked ACH, DEM and modified-DEM (from the best to the worse) performances in the Indian population., (Copyright © 2023 Elsevier Masson SAS. All rights reserved.)

Introduction: It is important to generate predictable statistical models by increasing the number of variables on the human skeletal and soft tissue structures on the face to increase the accuracy of human facial reconstructions. The purpose of this study was to determine mouth width 3-dimensionally based on statistical regression model., Material and Methods: Cone-beam computed tomography scan data from 130 individuals were used to measure the horizontal and vertical dimensions of orbital and nasal structures and intercanine width. The correlation between these hard tissue variables and the mouth width was evaluated using the statistical regression model., Results: Orbital width, nasal width, and intercanine width were found to be strong predictors of the mouth width determination and were used to generate the regression formulae to find the most approximate position of the mouth., Conclusion: These specific variables may contribute to improving the accuracy of mouth width determination for oral and maxillofacial reconstructions., Competing Interests: The authors report no conflicts of interest., (Copyright © 2024 by Mutaz B. Habal, MD.)

Objective: The aim of this study is to assess the efficacy of employing a deep learning methodology for the automated identification and enumeration of permanent teeth in bitewing radiographs. The experimental procedures and techniques employed in this study are described in the following section., Study Design: A total of 1248 bitewing radiography images were annotated using the CranioCatch labeling program, developed in Eskişehir, Turkey. The dataset has been partitioned into 3 subsets: training (n = 1000, 80% of the total), validation (n = 124, 10% of the total), and test (n = 124, 10% of the total) sets. The images were subjected to a 3 × 3 clash operation in order to enhance the clarity of the labeled regions., Results: The F1, sensitivity and precision results of the artificial intelligence model obtained using the Yolov5 architecture in the test dataset were found to be 0.9913, 0.9954, and 0.9873, respectively., Conclusion: The utilization of numerical identification for teeth within deep learning-based artificial intelligence algorithms applied to bitewing radiographs has demonstrated notable efficacy. The utilization of clinical decision support system software, which is augmented by artificial intelligence, has the potential to enhance the efficiency and effectiveness of dental practitioners., Competing Interests: Declaration of interests There is no conflict of interest., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objectives: To measure the impact of superimposition methods and the designated comparison area on accuracy analyses of dentate models using an ISO-recommended 3-dimensional (3D) metrology-grade inspection software (Geomagic Control X; 3D Systems; Rock Hill, South Carolina; USA)., Materials and Methods: A dentate maxillary typodont scanned with a desktop scanner (E4; 3 Shape; Copenhagen; Denmark) and an intraoral scanner (Trios 4; 3 Shape; Copenhagen; Denmark) was used as reference. Eight groups were created based on the core features of each superimposition method: landmark-based alignment (G1); partial area-based alignment (G2); entire tooth area-based alignment (G3); double alignment combining landmark-based alignment with entire tooth area-based alignment (G4); double alignment combining partial area-based alignment with entire tooth area-based alignment (G5); initial automated quick pre-alignment (G6); initial automated precise pre-alignment (G7); and entire model area-based alignment (G8). Diverse variations of each alignment and two regions for accuracy analyses (teeth surface or full model surface) were tested, resulting in a total of thirty-two subgroups (n = 18). The alignment accuracy between experimental and reference meshes was quantified using root mean square (RMS) error as trueness and its repeatability as precision. The descriptive statistics, a factorial repeated measures analysis of variance (ANOVA) and a post hoc Tuckey multiple comparison tests were used to analyze the trueness, and precision (α = 0.05)., Results: A total of 576 superimpositions were performed. The unique partial area-based superimposition method demonstrated the least precise alignment and was the sole group to exhibit a significant difference (p<.001). Automated initial pre-alignments demonstrated similar accuracy to other superimposition methods (p>.05). Double alignments did not result in accuracy improvement (p>.05). The designated comparison area displayed differences in both trueness (p<.001) and precision (p<.001), leading to an overall discrepancy of 8 ± 4 μm between selecting the teeth surface or full model surface., Conclusions: The superimposition method choice within the tested software did not impact accuracy analyses, except when the alignment relies on a unique and reduced area, such as the palatal rugae, a single tooth, or three adjacent teeth on one side., Clinical Significance: The superimposition method choice within the tested ISO-recommended 3D inspection software did not impact accuracy analyses., Competing Interests: Declaration of competing interest The authors have no conflict of interest to report pertaining to the conduction of this study., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

The potential of intraoral 3D photo scans in forensic odontology identification remains largely unexplored, even though the high degree of detail could allow automated comparison of ante mortem and post mortem dentitions. Differences in soft tissue conditions between ante- and post mortem intraoral 3D photo scans may cause ambiguous variation, burdening the potential automation of the matching process and underlining the need for limiting inclusion of soft tissue in dental comparison. The soft tissue removal must be able to handle dental arches with missing teeth, and intraoral 3D photo scans not originating from plaster models. To address these challenges, we have developed the grid-cutting method. The method is customisable, allowing fine-grained analysis using a small grid size and adaptation of how much of the soft tissues are excluded from the cropped dental scan. When tested on 66 dental scans, the grid-cutting method was able to limit the amount of soft tissue without removing any teeth in 63/66 dental scans. The remaining 3 dental scans had partly erupted third molars (wisdom teeth) which were removed by the grid-cutting method. Overall, the grid-cutting method represents an important step towards automating the matching process in forensic odontology identification using intraoral 3D photo scans., (© 2024. The Author(s).)

Background: Teeth identification has a pivotal role in the dental curriculum and provides one of the important foundations of clinical practice. Accurately identifying teeth is a vital aspect of dental education and clinical practice, but can be challenging due to the anatomical similarities between categories. In this study, we aim to explore the possibility of using a deep learning model to classify isolated tooth by a set of photographs., Methods: A collection of 5,100 photographs from 850 isolated human tooth specimens were assembled to serve as the dataset for this study. Each tooth was carefully labeled during the data collection phase through direct observation. We developed a deep learning model that incorporates the state-of-the-art feature extractor and attention mechanism to classify each tooth based on a set of 6 photographs captured from multiple angles. To increase the validity of model evaluation, a voting-based strategy was applied to refine the test set to generate a more reliable label, and the model was evaluated under different types of classification granularities., Results: This deep learning model achieved top-3 accuracies of over 90% in all classification types, with an average AUC of 0.95. The Cohen's Kappa demonstrated good agreement between model prediction and the test set., Conclusions: This deep learning model can achieve performance comparable to that of human experts and has the potential to become a valuable tool for dental education and various applications in accurately identifying isolated tooth., (© 2024. The Author(s).)

Accurate segmentation of oral surgery-related tissues from cone beam computed tomography (CBCT) images can significantly accelerate treatment planning and improve surgical accuracy. In this paper, we propose a fully automated tissue segmentation system for dental implant surgery. Specifically, we propose an image preprocessing method based on data distribution histograms, which can adaptively process CBCT images with different parameters. Based on this, we use the bone segmentation network to obtain the segmentation results of alveolar bone, teeth, and maxillary sinus. We use the tooth and mandibular regions as the ROI regions of tooth segmentation and mandibular nerve tube segmentation to achieve the corresponding tasks. The tooth segmentation results can obtain the order information of the dentition. The corresponding experimental results show that our method can achieve higher segmentation accuracy and efficiency compared to existing methods. Its average Dice scores on the tooth, alveolar bone, maxillary sinus, and mandibular canal segmentation tasks were 96.5%, 95.4%, 93.6%, and 94.8%, respectively. These results demonstrate that it can accelerate the development of digital dentistry., (© 2024. The Author(s).)