Search Results (41)

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

Background/Objectives: In dental medicine, the integration of various types of X-ray images, such as periapical (PA), bitewing (BW), and panoramic (PANO) radiographs, is crucial for comprehensive oral health assessment. These complementary imaging modalities provide diverse diagnostic perspectives and support the early detection of oral diseases, thereby enhancing treatment outcomes. However, there is currently no existing system that integrates multiple types of dental X-rays for both adults and children to perform tooth localization and numbering. Methods: Therefore, this study aimed to propose a system based on YOLOv8 that integrates multiple dental X-ray images and automatically detects and numbers both permanent and deciduous teeth. Through image preprocessing, various types of dental X-ray images were standardized and enhanced to improve the recognition accuracy of individual teeth. Results: With the implementation of a novel image preprocessing method, the system achieved a detection precision of 98.16% for permanent and deciduous teeth, representing a 3% improvement over models without image enhancement. In addition, the system attained an average tooth numbering accuracy of 98.5% for permanent teeth and 96.3% for deciduous teeth, surpassing existing methods by 5.6%. Conclusions: These results might highlight the innovation of the proposed image processing method and show its practical value in assisting clinicians with accurate diagnosis of tooth loss and the identification of missing teeth, ultimately contributing to improved diagnosis and treatment in dental care. Full article

Background: Bisphosphonate-related osteonecrosis of the jaw (BRONJ) represents a severe complication associated with bisphosphonate therapy commonly used in patients with osteoporosis and malignancies. Methods: This retrospective study evaluates the risk factors and clinical outcomes of BRONJ patients treated at the Oral and Maxillofacial Surgery Clinic in Iaşi, Romania, with the goal of optimizing preventive and therapeutic strategies. Data from 72 BRONJ patients treated between January 2013 and December 2023 were analyzed. Results: The majority (83.3%) of patients had underlying malignancies, predominantly breast and prostate cancers. The mandible was most affected, with tooth extraction identified as the primary triggering event. Systemic comorbidities, notably arterial hypertension, diabetes mellitus, and concurrent chemotherapy, were significantly associated with increased BRONJ severity. Surgical intervention was frequently required, with sequestrectomy being the predominant procedure, reflecting advanced disease at the time of diagnosis. Conclusions: The findings underline the critical importance of early identification, preventive dental management, and a collaborative multidisciplinary approach to improve patient prognosis. Full article

Background: Dental radiographs play a crucial role in diagnosis and treatment planning. With the rise in digital imaging, there is growing interest in leveraging artificial intelligence (AI) to support clinical decision-making. AI technologies can enhance diagnostic accuracy by automating tasks like identifying and locating dental structures. The aim of the current study was to assess and validate the accuracy of an AI-powered application in the detection and numbering of primary teeth on panoramic radiographs. Methods: This study examined 598 archived panoramic radiographs of subjects aged 4–14 years old. Images with poor diagnostic quality were excluded. Three experienced clinicians independently assessed each image to establish the ground truth for primary teeth identification. The same radiographs were then evaluated using EM2AI, an AI-based diagnostic software for the automatic detection and numbering of primary teeth. The AI’s performance was assessed by comparing its output to the ground truth using sensitivity, specificity, predictive values, accuracy, and the Kappa coefficient. Results: EM2AI demonstrated high overall performance in detecting and numbering primary teeth in mixed dentition, with an accuracy of 0.98, a sensitivity of 0.97, a specificity of 0.99, and a Kappa coefficient of 0.96. Detection accuracy for individual teeth ranged from 0.96 to 0.99. The highest sensitivity (0.99) was observed in detecting upper right canines and primary molars, while the lowest sensitivity (0.79–0.85) occurred in detecting lower incisors and the upper left first molar. Conclusions: The AI module demonstrated high accuracy in the automatic detection of primary teeth presence and numbering in panoramic images, with performance metrics exceeding 90%. With further validation, such systems could support automated dental charting, improve electronic dental records, and aid clinical decision-making. Full article

Background: Nanomaterial-based biosensors represent a transformative advancement in oral health diagnostics and therapeutics, offering superior sensitivity and selectivity for early disease detection compared to conventional methods. Their applications span prosthetic dentistry, where they enable the precise monitoring of dental implants, and theranostics for conditions such as dental caries, oral cancers, and periodontal diseases. These innovations promise to enhance proactive oral healthcare by integrating detection, treatment, and preventive strategies. Objectives: This review comprehensively examines the role of nanomaterial-based biosensors in dental theranostics, with a focus on prosthetic applications. It emphasizes their utility in dental implant surveillance, the early identification of prosthesis-related complications, and their broader implications for personalized treatment paradigms. Methods: A systematic literature search was conducted across PubMed, Scopus, and Web of Science for studies published between 2010 and early 2025. Keywords included combinations of “nanomaterials”, “biosensors”, “dentistry”, “oral health”, “diagnostics”, “therapeutics”, and “theranostics”. Articles were selected based on their relevance to nanomaterial applications in dental biosensors and their clinical translation. Results: The review identified diverse classes of nanomaterials—such as metallic nanoparticles, carbon-based structures, and quantum dots—whose unique physicochemical properties enhance biosensor performance. Key advancements include the ultra-sensitive detection of biomarkers in saliva and gingival crevicular fluid, the real-time monitoring of peri-implant inflammatory markers, and cost-effective diagnostic platforms. These systems demonstrate exceptional precision in detecting early-stage pathologies while improving operational efficiency in clinical settings. Conclusions: Nanomaterial-based biosensors hold significant promise for revolutionizing dental care through real-time implant monitoring and early complication detection. Despite challenges related to biocompatibility, scalable manufacturing, and rigorous clinical validation, these technologies may redefine oral healthcare by extending prosthetic device longevity, enabling personalized interventions, and reducing long-term treatment costs. Future research must address translational barriers to fully harness their potential in improving diagnostic accuracy and therapeutic outcomes. Full article

Introduction: Salivary biomarkers have been extensively studied in relation to oral disease, such as periodontal disease, oral cancer, and dental caries, as well as systemic conditions including diabetes, cardiovascular diseases, and neurological disorders. Literature Review: A systematic literature review was conducted, analyzing recent advancements in salivary biomarker research. Databases such as PubMed, Scopus, and Web of Science were searched for relevant studies published in the last decade. The selection criteria included studies focusing on the identification, validation, and clinical application of salivary biomarkers in diagnosing oral and systemic diseases. Various detection techniques, including enzyme-linked immunosorbent assay (ELISA), polymerase chain reaction (PCR), mass spectrometry, and biosensor technologies, were reviewed to assess their effectiveness in biomarker analysis. Specific biomarkers, such as inflammatory cytokines, oxidative stress markers, and microRNAs, have been identified as reliable indicators of disease progression. Current Trends and Future Perspectives: Advances in proteomics, genomics, and metabolomics have significantly enhanced the ability to analyze salivary biomarkers with high sensitivity and specificity. Despite the promising findings, challenges remain in standardizing sample collection, processing, and analysis to ensure reproducibility and clinical applicability. Conclusions: Future research should focus on developing point-of-care diagnostic tools and integrating artificial intelligence to improve the predictive accuracy of salivary biomarkers. Full article

Background/Objectives: Periodontitis is an inflammatory disease associated with many systemic disorders such as diabetes and cardiovascular disease. The aim was to evaluate the usefulness of the CDC/AAP and the EFP/AAP criteria to detect subclinical atherosclerosis in subjects with diabetes and severe periodontal disease. Methods: This was a cross-sectional study. Atheroma plaque was evaluated by high-resolution carotid and femoral ultrasonography. A dental examination protocol was implemented by a trained periodontist. A full-mouth periodontal clinical examination was carried out at six sites by automated computerized Florida Probe Periodontal Probing. Periodontal disease was defined by CDC/AAP and EFP/AAP criteria. Results: In total, 98 patients were included (60.2% women), of which 50% had diabetes. Subjects with diabetes showed a high prevalence of severe cases of periodontal disease. Both criteria were useful to detect the presence of atheroma plaque only in the presence of diabetes. However, the CDC/AAP criteria had higher correlation with atheroma plaques than EFP/AAP criteria (r = 0.522 vs. r = 0.369, p < 0.001). Conclusions: The CDC/AAP and the EFP/AAP criteria are a useful tool to identify subclinical atherosclerosis in subjects with severe periodontal disease and diabetes. These results show the potential role of the oral healthcare team in the dental office for the identification of subjects with diabetes at risk of developing cardiovascular disease. Full article

Background/Objectives: Dental caries is a widespread chronic infection, affecting a large segment of the population. Proximal caries, in particular, present a distinct obstacle for early identification owing to their position, which hinders clinical inspection. Radiographic assessments, particularly bitewing images (BRs), are frequently utilized to detect these carious lesions. Nonetheless, misinterpretations may obstruct precise diagnosis. This paper presents a deep-learning-based system to improve the evaluation process by detecting proximal dental caries from BRs and classifying their severity in accordance with ICCMS^TM guidelines. Methods: The system comprises three fundamental tasks: caries detection, tooth numbering, and describing caries location by identifying the tooth it belongs to and the surface, each built independently to enable reuse across many applications. We analyzed 1354 BRs annotated by a consultant of restorative dentistry to delineate the pertinent categories, concentrating on the detection and localization of caries tasks. A pre-trained YOLOv11-based instance segmentation model was employed, allocating 80% of the dataset for training, 10% for validation, and the remaining portion for evaluating the model on unseen data. Results: The system attained a precision of 0.844, recall of 0.864, F1-score of 0.851, and mAP of 0.888 for segmenting caries and classifying their severity, using an intersection over union (IoU) of 50% and a confidence threshold of 0.25. Concentrating on teeth that are entirely or three-quarters presented in BRs, the system attained 100% for identifying the affected teeth and surfaces. It achieved high sensitivity and accuracy in comparison to dentist evaluations. Conclusions: The results are encouraging, suggesting that the proposed system may effectively assist dentists in evaluating bitewing images, assessing lesion severity, and recommending suitable treatments. Full article

Introduction: Cleft lip and palate patients often present with unique anatomical challenges, making dental anomaly detection and numbering particularly complex. The accurate identification of teeth in these patients is crucial for effective treatment planning and long-term management. Artificial intelligence (AI) has emerged as a promising tool for enhancing diagnostic precision, yet its application in this specific patient population remains underexplored. Objectives: This study aimed to evaluate the performance of an AI-based software in detecting and numbering teeth in cleft lip and palate patients. The research focused on assessing the system’s sensitivity, precision, and specificity, while identifying potential limitations in specific anatomical regions and demographic groups. Methods: A total of 100 panoramic radiographs (52 males, 48 females) from patients aged 6 to 15 years were analyzed using AI software. Sensitivity, precision, and specificity were calculated, with ground truth annotations provided by four experienced orthodontists. The AI system’s performance was compared across age and gender groups, with particular attention to areas prone to misidentification. Results: The AI system demonstrated high overall sensitivity (0.98 ± 0.03) and precision (0.96 ± 0.04). No statistically significant differences were found between age groups (p > 0.05), but challenges were observed in the maxillary left region, which exhibited higher false positive and false negative rates. These findings were consistent with the prevalence of unilateral left clefts in the study population. Conclusions: The AI system was effective in detecting and numbering teeth in cleft lip and palate patients, but further refinement is required for improved accuracy in the cleft region, particularly on the left side. Addressing these limitations could enhance the clinical utility of AI in managing complex craniofacial cases. Full article

Backgrounds/Objectives: The finite element method (FEM) is an advanced numerical technique that can be applied in orthodontics to study tooth movements, stresses, and deformations that occur during orthodontic treatment. It is also useful for simulating and visualizing the biomechanical behavior of teeth, tissues, and orthodontic appliances in various clinical scenarios. The objective of this research was to analyze the mechanical behavior of teeth, tissues, and orthodontic appliances in various clinical scenarios. Materials and Methods: For this study, we utilized a model derived from a set of CBCT scans of a 26-year-old female patient who underwent fixed orthodontic treatment using the lingual technique. Through a series of programs based on reverse engineering, we constructed a three-dimensional reconstruction of the teeth and their internal structures. Using the finite element method (FEM), we obtained six simulations of an orthodontic system utilizing the fixed lingual technique, in which we employed brackets made of chrome–nickel or gold, and archwires made of nitinol, gold, or stainless steel. Results: The study reveals that although the deformation of the archwires during orthodontic treatment is the same, the forces generated by the three types of archwires on brackets differ. The variation in forces applied to the brackets in the fixed lingual orthodontic technique is essential for customizing orthodontic treatment, as these forces must be precisely controlled to ensure effective tooth movement and prevent overloading of the dental structures. Conclusions: The FEM analysis allows for the identification of ideal combinations between the materials used for orthodontic archwires and the materials used for brackets. This ensures that the optimal intensity of forces applied during the fixed lingual orthodontic technique results in desired tooth movements without causing damage to the enamel, dentin, or pulp of the teeth. Full article

Background: The use of 3D facial scans is becoming essential for dental practice. However, traditional scanners require labor-intensive procedures and are expensive, making them less accessible in routine clinical practice. In this context, high-performance smartphones and dedicated apps offer a more accessible alternative. This study aims to validate the accuracy of the EM3D app, which utilizes the iPhone’s TrueDepth camera technology, by comparing it to Cone Beam Computed Tomography (CBCT). Methods: Thirty patients requiring CBCT scans were recruited for the study. Facial scans obtained with the TrueDepth camera of the iPhone 13 Pro in conjunction with EM3D app were automatically superimposed onto the 3D models derived from the CBCTs through the implementation of a deep learning methodology. The approach enabled the automatic identification of fifteen landmarks to perform linear and angular measurements for quantitative assessment. A color map was created to highlight discrepancies between the overlaid meshes, and the overall surface differences between the models were automatically quantified. Results: The overall surface difference between the CBCT and EM3D scans was highly accurate, with a mean discrepancy of 0.387 ± 0.361 mm. The mean discrepancies of most measurements were lower than 1 mm (five out of six; 83.33%) between the groups, with no significant differences (p > 0.05). Conclusions: The combination of the iPhone’s TrueDepth camera and the EM3D app exhibited high accuracy for 3D facial modeling. This makes it a cost-effective alternative to professional scanning systems. Full article

Background: Osteoporosis, a systemic skeletal disorder, is expected to affect 60% of women over 50. While dual-energy X-ray absorptiometry (DXA) scans are the current gold standard for diagnosis, they are typically used only after fractures occur, highlighting the need for early detection tools. Initial studies have shown panoramic radiographs (PRs) to be a potential medium, but these have methodological flaws. This study aims to address these shortcomings by developing a robust AI application for accurate osteoporosis identification in PRs. Methods: A total of 348 PRs were used for development, 58 PRs for validation, and 51 PRs for hold-out testing. Initially, the YOLOv8 object detection model was employed to predict the regions of interest. Subsequently, the predicted regions of interest were extracted from the PRs and processed by the EfficientNet classification model. Results: The model for osteoporosis detection on a PR achieved an overall sensitivity of 0.83 and an F1-score of 0.53. The area under the curve (AUC) was 0.76. The lowest detection sensitivity was for the cropped angulus region (0.66), while the highest sensitivity was for the cropped mental foramen region (0.80). Conclusion: This research presents a proof-of-concept algorithm showing the potential of deep learning to identify osteoporosis in dental radiographs. Furthermore, our thorough evaluation of existing algorithms revealed that many optimistic outcomes lack credibility when subjected to rigorous methodological scrutiny. Full article

Periapical radiographs are routinely used in dental practice for diagnosis and treatment planning purposes. However, they often suffer from artifacts, distortions, and superimpositions, which can lead to potential misinterpretations. Thus, an automated detection system is required to overcome these challenges. Artificial intelligence (AI) has been revolutionizing various fields, including medicine and dentistry, by facilitating the development of intelligent systems that can aid in performing complex tasks such as diagnosis and treatment planning. 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 periapical radiographs. A dataset comprising 1000 periapical radiographs collected from 500 adult patients was analyzed by an AI system and compared with annotations provided by two oral and maxillofacial radiologists. A strong correlation (R > 0.5) was observed between AI perception and observers 1 and 2 in carious teeth (0.7–0.73), implants (0.97–0.98), restored teeth (0.85–0.89), teeth with fixed prosthesis (0.92–0.94), and missing teeth (0.82–0.85). The automatic detection by the AI system was comparable to the oral radiologists and may be useful for automatic identification in periapical radiographs. Full article

Background: Recent studies have shown an association between playing wind instruments and their impact on the orofacial system. However, they have not fully evaluated all aspects of the topic, leaving a gap in the overall understanding. Methods: A thorough search of the National Library of Medicine database was conducted using our research strategy, resulting in the identification of relevant studies. An expert perspective was obtained by conducting two in-depth expert interviews with a professor of horn-playing and a specialised dentist. Results: Thirty-seven relevant publications were included in the traditional literature review. The most common diseases among professional wind instrumentalists include the lip area, temporomandibular joint, oral mucosa, respiratory system, oral allergic reactions, and orofacial trauma. Special measures, preventive measures, and expert opinions were utilised to address and overcome the associated orofacial problems. Conclusions: Wind instruments affect the oral health and tooth movement of professional instrumentalists, and dentists should consider the impact of dental changes on embouchure and performance. Dental impressions and three-dimensional intra-oral scans are important for reconstruction. This research highlights the need for specialised dental care for professional wind instrumentalists, and further studies are necessary to fully explore this topic. Full article

Dental identification systems (DISs) encompass various techniques used for forensic identification, serving as alternatives or complements to genetic methods. Technologies such as microchip implants, prosthetic inscriptions, microSD cards, and identification plaques have been proposed to address limitations in comparative methods, offering streamlined processes for forensic experts. This study reviews current and potential DIS implementations, emphasizing cost-effectiveness and community benefits. Literature analysis from PubMed (2008–2024) yielded 17 relevant articles on implantable DISs, enabling direct subject identification via teeth or prostheses. The integration of DIS aims to enhance accuracy and speed in personal profiling and legal identification, promoting technology transfer in dentistry. It will be necessary to develop strict privacy regulations to protect patient data and establish ethical guidelines for their use. The study’s aim is to highlight that the universal adoption of DISs could mitigate healthcare disputes and facilitate data exchange in clinical settings, which is particularly beneficial for vulnerable populations. Full article