Search Results (162)

Objective: The accurate visualization of root canal systems on periapical radiographs is critical for successful endodontic treatment. This study aimed to evaluate and compare the effectiveness of several image enhancement algorithms—including a novel Total Variation–Contrast-Limited Adaptive Histogram Equalization (TV-CLAHE) technique—in improving the detectability of root canal configurations in mandibular incisors, using cone-beam computed tomography (CBCT) as the gold standard. A null hypothesis was tested, assuming that enhancement methods would not significantly improve root canal detection compared to original radiographs. Method: A retrospective analysis was conducted on 60 periapical radiographs of mandibular incisors, resulting in 420 images after applying seven enhancement techniques: Histogram Equalization (HE), Contrast-Limited Adaptive Histogram Equalization (CLAHE), CLAHE optimized with Pelican Optimization Algorithm (CLAHE-POA), Global CLAHE (G-CLAHE), k-Caputo Fractional Differential Operator (KCFDO), and the proposed TV-CLAHE. Four experienced observers (two radiologists and two dentists) independently assessed root canal visibility. Subjective evaluation was performed using an own scale inspired by a 5-point Likert scale, and the detection accuracy was compared to the CBCT findings. Quantitative metrics including Peak Signal-to-Noise Ratio (PSNR), Signal-to-Noise Ratio (SNR), image entropy, and Structural Similarity Index Measure (SSIM) were calculated to objectively assess image quality. Results: Root canal detection accuracy improved across all enhancement methods, with the proposed TV-CLAHE algorithm achieving the highest performance (93–98% accuracy), closely approaching CBCT-level visualization. G-CLAHE also showed substantial improvement (up to 92%). Statistical analysis confirmed significant inter-method differences (p < 0.001). TV-CLAHE outperformed all other techniques in subjective quality ratings and yielded superior SNR and entropy values. Conclusions: Advanced image enhancement methods, particularly TV-CLAHE, significantly improve root canal visibility in 2D radiographs and offer a practical, low-cost alternative to CBCT in routine dental diagnostics. These findings support the integration of optimized contrast enhancement techniques into endodontic imaging workflows to reduce the risk of missed canals and improve treatment outcomes. Full article

Background: The integration of digital imaging tools in dentistry has transformed clinical workflows, diagnostic accuracy, and patient outcomes. However, less attention has been given to how these tools influence dentists’ self-reported outcomes, including clinical confidence, efficiency, perceived treatment quality, communication, and professional satisfaction. This article aimed at assessing AI-powered digital tools in dentistry and their self-reported impact on dental practitioners’ activity and treatment outcomes. Methods: A comprehensive survey was distributed to 126 dental professionals of different genders, ages, years of experience, and types of dental practice to assess their experiences and attitudes towards AI applications in diagnostics and treatment planning, as well as how patients and dentists perceive the benefits and challenges associated with digital dentistry. Results: Digital photographs and CBCT were regarded as essential tools to have in clinical practice, in contrast with intraoral scanners. However, barriers like high initial costs, specialty differences, and lack of formal training may influence the results. Conclusion: These findings suggest that when used appropriately, AI digital tools can significantly elevate the quality of clinical practice and professional fulfillment and underscore the importance of tailored training programs and supportive infrastructures to facilitate the effective integration of digital technologies in dental practice. Full article

Dens invaginatus is a developmental dental anomaly characterized by the infolding of the enamel organ into the dental papilla during early odontogenesis. This process leads to a broad spectrum of anatomical variations, ranging from minor enamel-lined pits confined to the crown to deep invaginations extending through the root, occasionally communicating with periodontal or periapical tissues. The internal complexity of affected teeth presents diagnostic and therapeutic challenges, particularly in severe forms that mimic root canal systems or are associated with pulpal or periapical pathology. Maxillary lateral incisors are most frequently affected, likely due to their unique developmental timeline and morphological susceptibility. Although various classification systems have been proposed, Oehlers’ classification remains the most clinically relevant due to its simplicity and correlation with treatment complexity. Recent advances in diagnostic imaging, especially cone beam computed tomography (CBCT), have revolutionized the identification and classification of these anomalies. CBCT-based adaptations of Oehlers’ classification allow for the precise assessment of invagination extent and pulpal involvement, facilitating improved treatment planning. Contemporary therapeutic strategies now include calcium-silicate-based cement sealing materials, endodontic microsurgery for inaccessible anatomy, and regenerative endodontic procedures for immature teeth with necrotic pulps. Emerging developments in artificial intelligence, genetic research, and tissue engineering promise to further refine diagnostic capabilities and treatment options. Early detection remains critical to prevent complications such as pulpal necrosis or apical disease. A multidisciplinary, image-guided, and patient-centered approach is essential for optimizing clinical outcomes in cases of dens invaginatus. Full article

This study investigated the radiation attenuation characteristics of commonly used dental implant materials across an energy spectrum relevant to dental radiology. Two titanium implants were examined, with densities of 4.428 g/cm³ and 4.51 g/cm³, respectively. The first consisted of 90.39% titanium, 5.40% aluminum, and 4.21% vanadium, while the second comprised 58% titanium, 33% oxygen, 7% iron, 1% carbon, and 1% nitrogen. The third material was a zirconia implant (5Y form) composed of 94.75% zirconium dioxide, 5.00% yttrium oxide, and 0.25% aluminum oxide, exhibiting a higher density of 6.05 g/cm³. Monte Carlo simulations (MCNP6) and XCOM data were utilized to estimate photon source parameters, geometric configuration, and interactions with biological materials to calculate the half-value layer, mean free path, and tenth-value layer at varying photon energies. The results indicated that titanium alloys are well suited for low-energy imaging modalities such as CBCT and panoramic radiography due to their reduced artifact production. While zirconia implants demonstrated superior attenuation at higher energies (e.g., CT), their higher density may induce beam-hardening artifacts in low-energy systems. Future research should validate these simulation results through in vitro and clinical imaging and further explore the correlation between material-specific attenuation and CBCT image artifacts. Full article

Background/Objectives: Among the paranasal sinuses, the maxillary antrum holds unique clinical relevance due to its proximity to the alveolar process of the maxilla, which houses the teeth. This study aimed to evaluate the position of the root apices of the maxillary canines and posterior teeth relative to the maxillary sinus floor in Romanian subjects. Methods: Data for the study were retrospectively obtained from cone-beam computed tomography (CBCT) scans. The evaluation considered the pattern of proximity to the sinus floor for each tooth type, comparisons of the sinus relationships of teeth within the same dental hemiarch, as well as those of homologous teeth, and variation in root-to-sinus distance in relation to sex and age. Nonparametric tests were used for statistical analysis, and multiple comparisons were performed using Bonferroni post hoc correction. Results: The study included 70 individuals aged 20 to 60 years. The distance to the sinus floor decreased progressively from the first premolar to the second molar, with median values of 3.68 mm (first premolar), 1.45 mm (second premolar), 0.50 mm (first molar), and 0.34 mm (second molar) (p < 0.01). Stronger correlations were observed between adjacent teeth than between non-adjacent ones. The distances to the sinus floor were greater on the right side compared to the left; however, these differences were not statistically significant (p > 0.05 for all teeth). Concordance between left and right dental hemiarches regarding the closest tooth to the sinus floor was found in 70% of cases (n = 49), most frequently involving the second molars (n = 38; 54.3%). On average, the distance from the sinus floor was smaller in males compared to females, with statistically significant differences observed only for the second molar. Increased age was associated with a greater distance to the sinus floor. Conclusions: Of all the teeth investigated, the second molar showed the highest combined prevalence of penetrating and tangential relationships with the maxillary sinus. At the dental hemiarch level, the second molar was most frequently the closest tooth to the sinus floor, and in the majority of cases, at least one posterior tooth was located within 0.3 mm. Accurate preoperative assessment of tooth position relative to the sinus floor is essential when performing non-surgical or surgical root canal therapy and extractions of maxillary molars and premolars. CBCT provides essential three-dimensional imaging that improves diagnostic precision and supports safer treatment planning for procedures involving the posterior maxilla. Full article

Odontogenic abscesses are a frequent and challenging clinical issue in pet rabbits, often requiring a comprehensive diagnostic and therapeutic approach. This review collates current evidence on the etiology, diagnosis, and treatment of rabbit odontogenic abscesses, with a focus on imaging advances, microbial diversity, and local antimicrobial therapies. Predisposing factors include congenital conformation, inappropriate diet (insufficient abrasiveness, calcium or Vit D deficiencies, etc.), trauma, and neoplasia. Imaging techniques such as CT and cone-beam CT (CBCT) enable early detection and surgical planning, while traditional radiography remains useful in general practice. Treatment includes systemic antibiotics, surgical curettage, and the use of localized delivery systems such as antibiotic-impregnated polymethyl methacrylate (AIPMMA) beads. Adjunctive therapies like Manuka honey are also discussed. Two original heatmaps summarize bacterial prevalence and antimicrobial resistance from six peer-reviewed studies. These visualizations highlight the polymicrobial nature of these infections and the emergence of multidrug-resistant strains. Preventive strategies focus on optimal diet, regular dental checks, and owner education. The review also identifies key gaps in the literature, including the underreporting of anaerobes and lack of standardized treatment protocols. This article aims to support veterinary professionals in delivering evidence-based, individualized care to improve outcomes in rabbits with odontogenic abscesses. Full article

Background/Objectives: The infraorbital canal (IOC) is a critical anatomical structure that passes through the anterior surface of the maxilla and opens at the infraorbital foramen, containing the infraorbital nerve, artery, and vein. Accurate localization of this canal in maxillofacial, dental implant, and orbital surgeries is of great importance to preventing nerve damage, reducing complications, and enabling successful surgical planning. The aim of this study is to perform automatic segmentation of the infraorbital canal in cone-beam computed tomography (CBCT) images using an artificial intelligence (AI)-based model. Methods: A total of 220 CBCT images of the IOC from 110 patients were labeled using the 3D Slicer software (version 4.10.2; MIT, Cambridge, MA, USA). The dataset was split into training, validation, and test sets at a ratio of 8:1:1. The nnU-Net v2 architecture was applied to the training and test datasets to predict and generate appropriate algorithm weight factors. The confusion matrix was used to check the accuracy and performance of the model. As a result of the test, the Dice Coefficient (DC), Intersection over the Union (IoU), F1-score, and 95% Hausdorff distance (95% HD) metrics were calculated. Results: By testing the model, the DC, IoU, F1-score, and 95% HD metric values were found to be 0.7792, 0.6402, 0.787, and 0.7661, respectively. According to the data obtained, the receiver operating characteristic (ROC) curve was drawn, and the AUC value under the curve was determined to be 0.91. Conclusions: Accurate identification and preservation of the IOC during surgical procedures are of critical importance to maintaining a patient’s functional and sensory integrity. The findings of this study demonstrated that the IOC can be detected with high precision and accuracy using an AI-based automatic segmentation method in CBCT images. This approach has significant potential to reduce surgical risks and to enhance the safety of critical anatomical structures. Full article

Background: Artificial Intelligence (AI) is revolutionizing healthcare by enhancing diagnostic precision and risk assessment. In dentistry, AI has been increasingly integrated into Cone Beam Computed Tomography (CBCT) to improve image interpretation and pre-surgical planning. The lingual foramen (LF), a vital anatomical structure that transmits neurovascular elements, requires accurate evaluation during implant procedures. Traditional CBCT studies describe LF variations but lack a standardized risk classification. This study introduces a novel AI-based model for stratifying the surgical risk associated with LF using machine learning. Objectives: This study aimed to (1) assess the prevalence and anatomical variations of the lingual foramen (LF) using CBCT, (2) develop an AI-driven risk classification model based on LF characteristics, and (3) compare the AI model’s performance with that of traditional statistical methods. Materials and Methods: A retrospective analysis of 166 CBCT scans was conducted. K-means clustering and decision tree algorithms classified foramina into Low, Moderate, and High-Risk groups based on count, size, and proximity to the alveolar crest. The model performance was evaluated using confusion matrix analysis, heatmap correlations, and the elbow method. Traditional analyses (chi-square and logistic regression) were also performed. Results: The AI model categorized foramina into low (60%), moderate (30%), and high (10%) risk groups. The decision tree achieved a classification accuracy of 92.6 %, with 89.4% agreement with expert manual classification, confirming the model’s reliability. Conclusions: This study presents a validated AI-driven model for the risk assessment of the lingual foramen. Integrating AI into CBCT workflows offers a structured, objective, and automated method for enhancing surgical safety and precision in dental implant planning. Full article

Background/Objectives: Osteoporosis is a widespread and chronic systemic bone disease that affects the jaws and teeth and, therefore, also dentistry. Osteoporosis can be diagnosed by different radiological methods. Dental cone beam computed tomography (CBCT) plays an important role in dentistry imaging. The aim of our retrospective pilot study was to find criteria in CBCT that point to the possible existence of osteoporosis. Methods: Pilot study. The hard palate thickness (HPT) of the patients was measured at a defined location in the CBCT. Additionally, the CBCT images were presented to a radiologist for visual assessment. Both results were compared with the DXA measurements—as the “gold standard”—and patient history. Results: We found a consistent correlation between the visual assessments using established radiological criteria, including the new criterion of hard palate thickness (HPT), and the diagnosis of normal or pathological bone density. Secondly, for the HPT measurement all “pathologic” CBCT had an HPT of ≤0.9 mm, and all normal patients had an HPT of ≥0.9 mm. Conclusions: Despite the small sample size, this CBCT pilot study showed a correlation between HPT and systemic bone disease. Therefore, as our main result, we found a new CBCT diagnostic criterion, which quickly and uncomplicatedly points to the possible existence of bone disease, especially osteoporosis. We propose HPT as a new criterion in the evaluation of CBCT images. A threshold of <0.9 mm may be indicative for osteoporosis or osteopenia, indicating a need for further evaluation. Full article

Background: This systematic review evaluates articles investigating the use of iterative reconstruction (IR) algorithms and artificial intelligence (AI)-based noise reduction techniques to improve the quality of oral CBCT images. Materials and Methods: A detailed search was performed across PubMed, Scopus, Web of Science, ScienceDirect, and Embase databases. The inclusion criteria were prospective or retrospective studies with IR and AI for CBCT images, studies in which the image quality was statistically assessed, studies on humans, and studies published in peer-reviewed journals in English. Quality assessment was performed independently by two authors, and the conflicts were resolved by the third expert. For bias assessment, the Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool was used for bias assessment. Material: A total of eleven studies were included, analyzing a range of IR and AI methods designed to reduce noise and artifacts in CBCT images. Results: A statistically significant improvement in CBCT image quality parameters was achieved by the algorithms used in each of the articles we reviewed. The most commonly used image quality measures were peak signal-to-noise ratio (PSNR) and contrast-to-noise ratio (CNR). The most significant increase in PSNR was demonstrated by Ylisiurua et al. and Vestergaard et al., who reported an increase in this parameter of more than 30% for both deep learning (DL) techniques used. Another subcategory used to improve the quality of CBCT images is the reconstruction of synthetic computed tomography (sCT) images using AI. The use of sCT allowed an increase in PSNR ranging from 17% to 30%. For the more traditional methods, FBP and iterative reconstructions, there was an improvement in the PSNR parameter but not as high, ranging from 3% to 13%. Among the research papers evaluating the CNR parameter, an improvement of 17% to 29% was achieved. Conclusions: The use of AI and IR can significantly improve the quality of oral CBCT images by reducing image noise. Full article

Background and Clinical Significance: To describe the effectiveness of alveolar ridge preservation under the radiological and histological analysis of a customized resorbable scaffold three-dimensionally printed with polycaprolactone (PCL) reinforced with a coating of a copolymer of polycaprolactone-block-polyethylene glycol (PCL–PEG) by electrospray. Case Presentation: A 62-year-old male with vertical root fractures of teeth #14 and #15. From the cone beam CT (CBCT) image, the scaffold root replicas were designed with the shape of the roots and printed with PCL coated with PCL–PEG by electrospray. The scaffold was inserted into the alveolar bone and maintained with a tension-free flap closure. After six months, a CBCT of the surgical site and histological analysis of a bone sample at the dental implant installation site were performed. After 6 months, the wound in tooth #14 was closed, clinically proving no adverse reaction or complications. The histological analysis of the bone sample showed new bone formation with lamellar structure, Haversian canal structure, and osteocyte spaces. However, the scaffold in tooth #15 was exposed and not osseointegrated, and it was covered with membranous tissue. Histologically, the sample showed tissue compatible with lax connective tissue with mixed inflammatory infiltrate. In tooth #14, the dental implant presented an insertion torque >35 Ncm and was rehabilitated three months after its installation. Conclusions: Three-dimensional printed PCL scaffolds showed the ability to regenerate vital and functional bone with osseointegration capability for maxillary bone regeneration and oral rehabilitation based on dental implants. A case of inadequate scaffold osseointegration accompanied by lax connective tissue formation is shown. Full article

Lateral cephalometric analysis is commonly used in orthodontics for skeletal classification to ensure an accurate and reliable diagnosis for treatment planning. However, most current research depends on analyzing different type of radiographs, which requires more computational time than 3D analysis. Consequently, this study addresses fully automatic orthodontics tracing based on the usage of artificial intelligence (AI) applied to 2D and 3D images, by designing a cephalometric system that analyzes the significant landmarks and regions of interest (ROI) needed in orthodontics tracing, especially for the mandible and maxilla teeth. In this research, a computerized system is developed to automate the tasks of orthodontics evaluation during 2D and Cone-Beam Computed Tomography (CBCT or 3D) systems measurements. This work was tested on a dataset that contains images of males and females obtained from dental hospitals with patient-informed consent. The dataset consists of 2D lateral cephalometric, panorama and CBCT radiographs. Many scenarios were applied to test the proposed system in landmark prediction and detection. Moreover, this study integrates the Grad-CAM (Gradient-Weighted Class Activation Mapping) technique to generate heat maps, providing transparent visualization of the regions the model focuses on during its decision-making process. By enhancing the interpretability of deep learning predictions, Grad-CAM strengthens clinical confidence in the system’s outputs, ensuring that ROI detection aligns with orthodontic diagnostic standards. This explainability is crucial in medical AI applications, where understanding model behavior is as important as achieving high accuracy. The experimental results achieved an accuracy exceeding 98.9%. This research evaluates and differentiates between the two-dimensional and the three-dimensional tracing analyses applied to measurements based on the practices of the European Board of Orthodontics. The results demonstrate the proposed methodology’s robustness when applied to cephalometric images. Furthermore, the evaluation of 3D analysis usage provides a clear understanding of the significance of integrated deep-learning techniques in orthodontics. Full article

(1) Background: Advancements in dental imaging technologies have significantly transformed diagnostic and surgical practices. The integration of cone beam computed tomography (CBCT), artificial intelligence (AI), augmented reality (AR), and virtual reality (VR) is enhancing clinical precision, streamlining workflows, and redefining dental education. This review examines the evolution, applications, and future potential of these technologies in modern dental practice. (2) Methods: A narrative literature review was conducted, synthesizing findings from recent studies on digital radiography, CBCT, AI-assisted diagnostics, 3D imaging, and involving simulation tools (AR/VR). Peer-reviewed journal articles, systematic reviews, and clinical studies were analyzed to explore their impact on diagnosis, treatment planning, surgical execution, and training. (3) Results: Digital and 3D imaging modalities have improved diagnostic accuracy and reduced radiation exposure. AI applications enhance image interpretation, automate clinical tasks, and support treatment simulations. AR and VR technologies provide involved, competency-based surgical training and real-time intraoperative guidance. Integrating 3D printing and portable imaging expands accessibility and personalization in care delivery. (4) Conclusions: The integration of CBCT, AI, AR, and VR represents a paradigm shift in dentistry, elevating precision, efficiency, and patient outcomes. Continued research, standardization, and ethical practice will be essential for widespread adoption and maximizing clinical benefits. Full article

Interdisciplinary decision-making significantly influences both the therapeutic potential and clinical outcomes, shaping clinical attitudes and management strategies. As the integration between endodontic and restorative-prosthetic considerations becomes increasingly prevalent, it is essential to understand how different dental specialists, particularly general dental practitioners, prosthodontists and endodontists, approach clinical decision-making and collaborate to optimize patient care. Objectives: This study aims to identify practice disparities in post-endodontic crown placement to inform national policy reforms, including standardised timing protocols and interdisciplinary referral criteria. Methods: A structured questionnaire was distributed to dentists practicing in Romania, yielding 238 collected responses. Results: Substantial variability was found in clinical approaches: diagnostic imaging preferences indicated frequent use of periapical radiography (83.49%) and CBCT (53.67%). Over 70% expressed high confidence in CBCT’s diagnostic precision, significantly higher than periapical radiography (Wilcoxon Signed-Rank test, p < 0.00001). A statistically significant majority (69.3%, binomial test, p < 0.001) preferred delaying definitive crown placement until radiographic healing of periapical lesions. Logistic regression analysis showed endodontists were significantly less likely to choose invasive treatments compared to other specialists (p = 0.027). Although clinicians widely recognize the significance of prosthetic planning, its early integration into the overall treatment strategy has been inconsistent. Conclusions: This study points out the necessity for standardised guidelines that clearly integrate prosthetic planning into endodontic decision-making, enhancing predictability and tooth preservation. Full article

Background: Traumatic injuries to the alveolar process and primary teeth in early childhood can have long-term consequences on the development of permanent dentition and eruption pathways. Objective: This case report aims to illustrate the impact of early orofacial trauma on the eruption and development of permanent maxillary incisors and to emphasize the importance of timely interdisciplinary management. Case Presentation: An 8-year-old female patient presented to a pediatric dentistry clinic with delayed eruption of the maxillary anterior permanent teeth. In contrast, her monozygotic twin sister exhibited complete eruption of all permanent anterior teeth, raising parental concern regarding a possible pathological delay. Her medical history revealed orofacial trauma at the age of two, resulting in an alveolar process fracture, avulsion of the primary maxillary left central incisor (tooth 61), and luxation of the primary maxillary right central incisor (tooth 51). A clinical examination demonstrated sufficient arch space without signs of eruption and enamel defects on tooth 52. Radiographic evaluations, including panoramic imaging and cone beam computed tomography (CBCT), confirmed the presence of impacted permanent teeth with structural anomalies suggestive of trauma-related developmental disturbances. Results: The patient underwent a multidisciplinary treatment over a three-year period involving pediatric dentistry, oral surgery, and orthodontics. Management included surgical exposure of the impacted teeth followed by orthodontic traction to guide the eruption and treatment of enamel hypoplasia. Conclusions: This case highlights the long-term consequences of early traumatic dental injuries on permanent dentition development. It underscores the necessity of early diagnosis and a coordinated interdisciplinary approach to optimize outcomes and enhance the long-term oral health and quality of life of affected individuals. Full article