Search Results (552)

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/Objectives: The diagnosis of pediatric dental conditions from panoramic radiographs is uniquely challenging due to the dynamic nature of the mixed dentition phase, which can lead to subjective and inconsistent interpretations. This study aims to develop and rigorously validate an advanced deep learning model to enhance diagnostic accuracy and efficiency in pediatric dentistry, providing an objective tool to support clinical decision-making. Methods: An initial comparative study of four state-of-the-art YOLO variants (YOLOv8, v9, v10, and v11) was conducted to identify the optimal architecture for detecting four common findings: Dental Caries, Deciduous Tooth, Root Canal Treatment, and Pulpotomy. A stringent two-tiered validation strategy was employed: a primary public dataset (n = 644 images) was used for training and model selection, while a completely independent external dataset (n = 150 images) was used for final testing. All annotations were validated by a dual-expert team comprising a board-certified pediatric dentist and an experienced oral and maxillofacial radiologist. Results: Based on its leading performance on the internal validation set, YOLOv11x was selected as the optimal model, achieving a mean Average Precision (mAP50) of 0.91. When evaluated on the independent external test set, the model demonstrated robust generalization, achieving an overall F1-Score of 0.81 and a mAP50 of 0.82. It yielded clinically valuable recall rates for therapeutic interventions (Root Canal Treatment: 88%; Pulpotomy: 86%) and other conditions (Deciduous Tooth: 84%; Dental Caries: 79%). Conclusions: Validated through a rigorous dual-dataset and dual-expert process, the YOLOv11x model demonstrates its potential as an accurate and reliable tool for automated detection in pediatric panoramic radiographs. This work suggests that such AI-driven systems can serve as valuable assistive tools for clinicians by supporting diagnostic workflows and contributing to the consistent detection of common dental findings in pediatric patients. Full article

Background and Clinical Significance: Traumatic dental injuries, particularly complicated crown fractures of permanent incisors, are common in adolescents, with maxillary central incisors most frequently affected due to their prominent position. These injuries, often resulting from sports or accidents, require prompt management to prevent complications such as pulp necrosis or infection, which can compromise long-term prognosis. Fragment reattachment offers a conservative, esthetically favorable approach when the fractured segment is intact, with outcomes comparable to composite restorations. This case report underscores the importance of timely intervention and advanced restorative techniques in pediatric dentistry. Case Presentation: A 16-year-old male presented with a complicated crown fracture of the upper left central incisor sustained during a soccer game. The fracture extended subgingivally with pulp exposure. The patient preserved the fragment in saline. Treatment involved fragment reattachment using a dentin bonding agent and flowable composite resin, followed by single-visit root canal therapy due to delayed presentation (48 h). A glass fiber post was placed to reinforce the restoration due to significant coronal loss. Three years of follow-up visits (1, 3, 6, 12, 24, and 36 months) revealed no clinical or radiographic complications, with the tooth remaining asymptomatic and functional. Conclusions: This case underscores the effectiveness of fragment reattachment when combined with meticulous technique and long-term monitoring. Full article

Background: Periodontitis is a multifactorial disease leading to the loss of clinical attachment and alveolar bone. The diagnosis of periodontitis involves a clinical examination and radiographic evaluation, including panoramic images. Panoramic radiographs are cost-effective methods widely used in periodontitis classification. The remaining bone height (RBH) is a parameter used to assess the alveolar bone level. Large language models are widely utilized in the medical sciences. ChatGPT, the leading conversational model, has recently been extended to process visual data. The aim of this study was to assess the effectiveness of the ChatGPT models 4.5, o1, o3 and o4-mini-high in RBH measurement and tooth counts in relation to dental professionals’ evaluations. Methods: The analysis was based on 10 panoramic images, from which 252, 251, 246 and 271 approximal sites were qualified for the RBH measurement (using the models 4.5, o1, o3 and o4-mini-high, respectively). Three examiners were asked to independently evaluate the RBH in approximal sites, while the tooth count was achieved by consensus. Subsequently, the results were compared with the ChatGPT outputs. Results: ChatGPT 4.5, ChatGPT o3 and ChatGPT o4-mini-high achieved substantial agreement with clinicians in the assessment of tooth counts (κ = 0.65, κ = 0.66, κ = 0.69, respectively), while ChatGPT o1 achieved moderate agreement (κ = 0.52). In the context of RBH values, the ChatGPT models consistently exhibited a positive mean bias compared with the clinicians. ChatGPT 4.5 was reported to provide the lowest bias (+12 percentage points (pp) for the distal surfaces, width of the 95% CI for limits of agreement (LoAs) ~60 pp; +11 pp for the mesial surfaces, LoA width ~54 pp). Conclusions: ChatGPT 4.5 and ChatGPT o3 show potential in the assessment of tooth counts on a panoramic radiograph; however, their present level of accuracy is insufficient for clinical use. In the current stage of development, the ChatGPT models substantially overestimated the RBH values; therefore, they are not applicable for classifying periodontal disease. Full article

This study aimed to assess skeletal and upper airway modifications induced by the Twin Block appliance in patients with Class II malocclusion due to mandibular retrusion, using two-dimensional imaging techniques. A total of 11 patients (6 males, 5 females) were included and stratified into two groups (Pre-Peak and Peak) based on skeletal maturity evaluated through the cervical vertebral maturation (CVM) method. Lateral cephalometric radiographs were obtained at the beginning (T0) and end (T1) of treatment and analyzed using DeltaDent software. The appliance was worn full-time, except during meals and oral hygiene procedures, with monthly follow-ups. Significant changes were observed between T0 and T1 across the sample. Comparison between groups revealed statistically significant differences only in the H-C3a1 and vertical height (th) parameters (p < 0.05). In conclusion, the Twin Block appliance proved effective in enhancing mandibular and dental positioning, while also exerting a favorable influence on upper airway development. Full article

Background/Objectives: Amelogenesis imperfecta (AI) represents a heterogeneous group of inherited disorders affecting the quality and quantity of dental enamel, making clinical diagnosis challenging. This study aimed to identify genetic variants in Slovenian patients with non-syndromic AI and to evaluate enamel morphology using radiographic parameters. Methods: Whole exome sequencing (WES) was performed on 24 AI patients and their families. Panoramic radiographs (OPTs) were analyzed using Fiji ImageJ to assess crown dimensions, enamel angle (EA), dentine angle (DA), and enamel–dentine mineralization ratio (EDMR) in lower second molar buds, compared to matched controls (n = 24). Two observers independently assessed measurements, and non-parametric tests compared EA, DA, and EDMR in patients with and without disease-causing variants (DCVs). Statistical models, including bootstrap-validated random forest and logistic regression, assessed variable influences. Results: DCVs were identified in ENAM (40% of families), AMELX (15%), and MMP20 (10%), including four novel variants. AI patients showed significant enamel deviations with high reproducibility, particularly in hypomineralized and hypoplastic regions. DA and EDMR showed significant correlations with DCVs (p < 0.01). A bootstrap-validated random forest model yielded a 90% (84.0–98.0%) AUC-estimated predictive power. Conclusions: These findings highlight a novel and reproducible radiographic approach for detecting developmental enamel defects in AI and support its diagnostic potential. Full article

Background/Objectives: Supernumerary teeth, or hyperdontia, refer to a developmental anomaly defined by the presence of additional teeth beyond the normal dentition. Hyperdontia may result in clinical complications including delayed eruption, crowding, and malocclusion. Despite its prevalence having been studied in various populations, data from geographically isolated or peripheral groups remain limited. This study aimed to investigate the prevalence and distribution of supernumerary teeth in a sample of children and adolescents from the island of Lesvos, Greece. Methods: A retrospective cross-sectional study was conducted using panoramic radiographs from 621 Caucasian children aged 9–16 years who attended orthodontic or general/pediatric dental clinics in Mytilini, Lesvos island, Greece. Radiographs were examined for the presence, number, type, and location of supernumerary teeth. The analysis included data to explore gender and arch distribution. Results: Supernumerary teeth were identified in 15 individuals, corresponding to a prevalence of 2.4%. A slightly higher occurrence was observed in males (1.4%) than in females (1%). The majority of supernumerary teeth were situated in the maxillary arch (1.9%). Mesiodens represented the most frequently observed type, followed by supernumerary lateral incisors, paramolars, and a single supernumerary central incisor. Conclusions: The prevalence of supernumerary teeth in this population is consistent with reported findings. Mesiodens was the most frequently observed type, with a predominance in the maxillary arch. Early detection of supernumerary teeth is crucial for accurate diagnosis and effective management. Full article

Objectives: In this study, we aimed to evaluate the accuracy of four radiological methods for dental age estimation and to test which method is the most reliable in Montenegrin children. Methods: To determine dental age, we examined 351 panoramic radiographs of 190 female and 161 male children between the ages of 6 and 12 using the Demirjian method, Willems method, the European formula, and the BAF. The estimated dental age was compared with the chronological age, and the average, absolute, and standard deviations were calculated. Results: When comparing dental age to chronological age in females, overestimation occurred when using the Demirjian and Willems methods, whereas the European formula and the BAF resulted in underestimations. Only the European formula underestimated dental age in males, while the other three methods caused overestimations in comparison to chronological age. The European formula and the BAF revealed that, when all age groups were included, approximately 57.1–61% of patients deviated from their chronological age by less than six months. When using Demirjian’s method, nearly 30–35% of patients had dental ages that were either overestimated or underestimated by more than a year. Conclusions: The most accurate method for estimating the dental age of children under 13 in the Montenegrin population is the European formula. The Demirjian method had the lowest accuracy, whereas that in the BAF and the Willems method was similar. 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/Objectives: This study evaluates the diagnostic and referral skills of final-year dental students at Al-Iraqia University using a questionnaire based on malocclusion cases ranging from mild to severe. Methods: The questionnaire, featuring photos and radiographs of five selected treated cases from two textbooks, was answered by 165 students who were asked to assess each case and determine whether orthodontic or surgical treatment was necessary, as well as to identify factors contributing to an unesthetic profile, such as irregular teeth. Frequency distribution and the Chi-square test were used for statistical analysis. Results: The results indicated good overall clinical competence. The unesthetic profile and irregular teeth were the main reasons for referring both Class II and III cases for surgery, with mandibular retrusion being the most common factor in aesthetic concerns. Maxillary protrusion was less frequently selected as a key factor in Class II malocclusion cases. Conclusions: The findings suggest that students demonstrated a high level of diagnostic accuracy in identifying treatment needs for various malocclusion cases. Full article

Background: In modern dentistry, alveolar socket preservation after tooth extraction plays a critical role in maintaining the alveolar ridge for future dental implants. This retrospective clinical study evaluated bone-level changes 15 years after immediate implant placement, coupled with alveolar ridge preservation. Methods: Fifty non-smoking patients aged 25 to 75 (30 males and 20 females) who underwent single-implant rehabilitation in both anterior and posterior regions of the upper and lower jaws were included. The study examined bone levels and implant survival over time, using standardized intraoral radiographs at 1, 5, and 15 years post-loading. Implants were placed immediately after atraumatic extraction, and the residual gap was grafted with bovine hydroxyapatite and covered with a collagen membrane. The primary outcome was bone-level stability, while secondary outcomes included implant failure. No temporary crowns or removable dentures were provided during healing. Radiographs were digitized for detailed analysis. Results: The results for 50 patients with immediate implant placement showed that bone-resorption levels were significantly higher in the upper jaw than in the lower jaw. Conclusions: Posterior implants exhibited greater bone loss than anterior implants, particularly at 1 year and 15 years, while no implant failures occurred. Full article

Background: Autogenous tooth transplantation is a valuable option for dental rehabilitation, particularly in young patients. Template-guided approaches, using 3D-printed replicas of donor teeth, have recently emerged as a method to increase precision and reduce extraoral time—two critical factors in maintaining periodontal ligament (PDL) vitality, which is essential to improve long-term outcomes. Methods: This report presents the case of a 12-year-old patient who underwent autotransplantation of tooth 18 to the site of tooth 75, which exhibited ankylosis. Patients exhibiting unfavorable root anatomy and morphology, systemic conditions, or completed root development were not considered for this technique. A patient-specific donor tooth replica was digitally designed and 3D-printed via CAD/CAM manufacturing to preoperatively shape the recipient site. The transplanted tooth 18 was then inserted with an extraoral time of less than one minute and subsequently stabilized using a flexible titanium trauma splint (TTS). Results: Longitudinal clinical and radiographic follow-up over 12 months confirmed favorable healing without signs of complications. Conclusions: This case illustrates the practical advantages of a fully digital, template-guided workflow in managing anatomically complex cases. Full article

Background/Objectives: Different sized calcified masses called pulp stones are often detected in dental pulp and can impact dental procedures. The current research was conducted with the aim of measuring the ability of artificial intelligence algorithms to accurately diagnose pulp and pulp stone calcifications on panoramic radiographs. Methods: We used 713 panoramic radiographs, on which a minimum of one pulp stone was detected, identified retrospectively, and included in the study—4675 pulp stones and 5085 pulps were marked on these radiographs using CVAT v1.7.0 labeling software. Results: In the test dataset, the AI model segmented 462 panoramic radiographs for pulp stone and 220 panoramic radiographs for pulp. The dice coefficient and Intersection over Union (IoU) recorded for the Pulp Segmentation model were 0.84 and 0.758, respectively. Precision and recall were computed to be 0.858 and 0.827, respectively. The Pulp Stone Segmentation model achieved a dice coefficient of 0.759 and an IoU of 0.686, with precision and recall of 0.792 and 0.773, respectively. Conclusions: Pulp and pulp stones can successfully be identified using artificial intelligence algorithms. This study provides evidence that artificial intelligence software using deep learning algorithms can be valuable adjunct tools in aiding clinicians in radiographic diagnosis. Further research in which larger datasets are examined are needed to enhance the capability of artificial intelligence models to make accurate diagnoses. Full article

This study aimed to demonstrate the Convolutional Neural Network (CNN) algorithm’s efficiency in detecting alveolar bone loss using panoramic radiographs. The comparison was evaluated among 1874 pictures retrieved from an institution, from which the training set included 953 showing bone loss and 921 normal cases. A confusion matrix was performed for statistical analysis. The CNN method correctly identified 92 out of 100 bone loss cases and 89 out of 100 healthy cases. The model showed a sensitivity of 0.8327, a specificity of 0.8683, a precision of 0.8918, an accuracy of 0.8927, and an F1 score of 0.8615 in detecting bone loss. This study concluded that a faster CNN model may be used as an adjuvant technique to diagnose periodontal disease and alveolar bone loss using dental panoramic radiography images, thereby minimizing diagnostic effort, and saving assessment time. However, the execution of precisely detecting periodontal cases by fully automated AI models using panoramic radiographs appears imminent and needs clinical periodontal evaluation for definitive diagnosis. The suitability of this approach is supported by the sensitivity, specificity, accuracy, and F-measure, which showed satisfactory performance for classifying cases. Based on population and periodontal disease burden standpoint, the use of AI in diagnosing periodontal diseases may serve as an excellent surveillance method to classify alveolar bone loss. Monitoring a periodontal patient after treatment needs a wide area to cover by AI-based diagnostic modality. With AI as the future of dentistry, performance-based clinical usage of CNN models demands confirmed practical application by dentists. Full article

Background and Clinical Significance: Chronic nonbacterial osteomyelitis (CNO) of the jaw is a rare autoinflammatory bone disorder that primarily affects children and adolescents. Diagnosing CNO of the mandible can be challenging due to its rarity, and the clinical and radiographic findings overlap with those of other bone disorders. Case Presentation: This case series retrospectively presents four female pediatric patients (9–12 years old) diagnosed with mandibular CNO. The patients were treated at King Abdulaziz University Dental Hospital, Jeddah, Saudi Arabia, between 2018 and 2024. Clinical features and radiographic and histopathological findings were evaluated. All cases had mandibular swelling and pain. Radiographic features consistently revealed mixed sclerotic and radiolucent lesions with bone expansion and periosteal reactions. Histopathological findings revealed viable bone interspersed with varying degrees of fibrous tissue. No evidence of bacterial colonies or inflammation was observed. This case series highlights the radiographic and histopathological features of CNO in the mandible of pediatric patients. The mixed radiographic features and variability of histopathological findings combined with the refractory nature of the lesions contribute to diagnostic complexity. Diagnostic challenges include differentiating CNO from other inflammatory and fibro-osseous conditions. The presence of recurrent episodes of pain, the formation of subperiosteal bone, periostitis, lysis of the cortical layer, expansion of the mandibular canal, and sterile bone biopsies with nonspecific inflammatory changes were related mainly to CNO. Conclusions: These findings underscore the need for increased awareness and a multidisciplinary approach for accurate diagnosis and management of CNO. Conservative management, particularly in dental cases, avoids prolonged unnecessary use of antibiotics, and the prescription of nonsteroidal anti-inflammatory drugs should be followed. Full article