Morphological Variations of the Nasopalatine Canal in the North Cyprus Population: A Cone Beam Computed Tomography Study.

BACKGROUND This study aimed to evaluate the morphological characteristics of the anterior maxillary nasopalatine canal and the width of the buccal bone using cone-beam computed tomography (CBCT) in 150 adults in Northern Cyprus. MATERIAL AND METHODS The study included 150 participants, and their anterior maxillary morphometric measurements (eg, length of the nasopalatine canal and anteroposterior diameter of the nasal foramen) were taken using CBCT with the scanning parameters of 90 kvP, 24 s, 4 mA, voxel size 0.3 mm, and field of view 10×6 cm. The shapes of the nasopalatine canal (NPC) were categorized into 4 types: cylindrical, hourglass, funnel-shaped, and banana (54%, 20.6%, 18.6%, and 4%, respectively). RESULTS The findings showed a clear link between the shape of the NPC and the horizontal dimensions of the anterior maxilla's morphometric properties. In general, decreased horizontal bone dimensions were found in the premaxilla at the banana- and funnel-shaped type for the nasopalatine canal. Also, the anteroposterior diameter of a nasal foramen in the hourglass shape was significantly larger in diameter than all other shapes. Additionally, the morphology of the nasopalatine canal is influenced by its shape. The sagittal cross-section has shown significant correlations with the sizes of the incisive foramen, nasal foramen, and the length of the nasopalatine canal. CONCLUSIONS The study found a correlation between the shape of the NPC and the horizontal dimensions of the anterior maxilla's anatomy. The measurements of NPC in a North Cyprus population slightly differ from the established standards found in the existing literature. Conducting more extensive studies with a larger number of CBCT images will offer additional insights.

Maxillary Canine Impaction: Assessing the Influence of Maxillary Anatomy Using Cone Beam Computed Tomography.

BACKGROUND The purpose of this study was to evaluate the anatomical characteristics of patients with unilateral impacted canine teeth compared to a control group. Cone beam computed tomography (CBCT) records were retrospectively analyzed. MATERIAL AND METHODS CBCT records of 64 patients with unilateral impacted canine teeth (57.8% female and 42.2% male) and 64 controls (59.4% female and 40.6% male) were retrospectively analyzed. On the CBCT images, intermolar width, arch length, arch perimeter, palatal width in the molar region at cementoenamel junction, palatal width in the molar region at the crest, palatal width in the molar region measured from mid-root level, nasal cavity width, and palatal depth were evaluated. RESULTS In the palatal width measurement from the mid-root variable, the measurement of labially positioned canines was significantly lower than the control group (P<0.05). In terms of intermolar width, the labial positioned impacted canines' values were lower than in the control group. There was a significant difference in terms of the perimeter variable and both palatinally and labially positioned impacted canines were significantly lower than in the control group (P<0.05). All parameters were compared according to sex, and measurements of male patients were significantly higher than in female patients (P<0.05). CONCLUSIONS A labially impacted canine was strongly linked to a decrease in mid-root palatal and intermolar widths. Additionally, impacted canines positioned both palatally and labially were found to result in a reduced arch perimeter. Moreover, male patients with impacted canines exhibited notably greater anatomical measurements compared to female patients.

Gender-Based Variation in Alveolar Bone Thickness of Maxillary Incisor Teeth: A CBCT Retrospective Study.

BACKGROUND This retrospective study from a single center in Cyprus aimed to assess labial (buccal) and palatal bone thickness in 6 anterior maxillary teeth of 120 adults using cone-beam computed tomography (CBCT). MATERIAL AND METHODS The CBCT scans of 120 patients (720 teeth) were examined, with scanning parameters of 90 kvP, 24 s, 4 mA, voxel size 0.3 mm, and field of view of 10×6 cm. All maxillary incisors were categorized into 3 distinct points in terms of buccal (B) and palatal (P) points, with points B1 (buccal) and P1 (palatal) 4 mm below the cementoenamel junction; points B2 and P2 at the midpoint between the labial and palatal alveolar crest plane extending to the root apex; and points B3 and P3 at the root apex. Evaluation was done by measuring the distance from these points to the labial and palatal alveolar bone. RESULTS When the thicknesses were measured between all 6 points and labial and palatal bone, the thickness of point B3 of tooth 13 in men was significantly higher than that in women. At points P1, P2, and P3 for teeth 11 and 13, the palatal bone thickness of men was significantly higher than that of women. At points P2 and P3 of tooth 12, the palatal bone thickness of men was significantly higher than that of women. CONCLUSIONS The study found a correlation between alveolar bone thickness and patient sex in the North Cyprus population. Alveolar bone thickness in the anterior maxillary should be considered in implant treatment and orthodontic techniques.

Prediction of bone invasion of oral squamous cell carcinoma using a magnetic resonance imaging-based machine learning model.

OBJECTIVES: Radiomics, a recently developed image-processing technology, holds potential in medical diagnostics. This study aimed to propose a machine-learning (ML) model and evaluate its effectiveness in detecting oral squamous cell carcinoma (OSCC) and predicting bone metastasis using magnetic resonance imaging (MRI). MATERIALS-METHODS: MRI radiomic features were extracted and analyzed to identify malignant lesions. A total of 86 patients (44 with benign lesions without bone invasion and 42 with malignant lesions with bone invasion) were included. Data and clinical information were managed using the RadCloud Platform (Huiying Medical Technology Co., Ltd., Beijing, China). The study employed a hand-crafted radiomics model, with the dataset randomly split into training and validation sets in an 8:2 ratio using 815 random seeds. RESULTS: The results revealed that the ML method support vector machine (SVM) performed best for detecting bone invasion (AUC = 0.999) in the test set. Radiomics tumor features derived from MRI are useful to predicting bone invasion from oral squamous cell carcinoma with high accuracy. CONCLUSIONS: This study introduces an ML model utilizing SVM and radiomics to predict bone invasion in OSCC. Despite the promising results, the small sample size necessitates larger multicenter studies to validate and expand these findings.

Automatic Deep Learning Detection of Overhanging Restorations in Bitewing Radiographs.

OBJECTIVE: This study aimed to assess the effectiveness of deep convolutional neural network (CNN) algorithms in detecting and segmentation of overhanging dental restorations in bitewing radiographs. METHOD AND MATERIALS: A total of 1160 anonymized bitewing radiographs were used to progress the Artificial Intelligence (AI) system) for the detection and segmentation of overhanging restorations. The data was then divided into three groups: 80% for training (930 images, 2399 labels), 10% for validation (115 images, 273 labels), and 10% for testing (115 images, 306 labels). A CNN model known as you only look once (YOLOv5) was trained to detect overhanging restorations in bitewing radiographs. After utilizing the remaining 115 radiographs to evaluate the efficacy of the proposed CNN model, the accuracy, sensitivity, precision, F1 score, and area under the receiver operating characteristic curve (AUC) were computed. RESULTS: The model demonstrated a precision of 90.9%, a sensitivity of 85.3%, and an F1 score of 88.0%. Furthermore, the model achieved an AUC of 0.859 on the Receiver Operating Characteristic (ROC) curve. The mean average precision (mAP) at an intersection over a union (IoU) threshold of 0.5 was notably high at 0.87. CONCLUSION: The findings suggest that deep CNN algorithms are highly effective in the detection and diagnosis of overhanging dental restorations in bitewing radiographs. The high levels of precision, sensitivity, and F1 score, along with the significant AUC and mAP values, underscore the potential of these advanced deep learning techniques in revolutionizing dental diagnostic procedures.

Artificial intelligence system for automatic maxillary sinus segmentation on cone beam computed tomography images.

OBJECTIVES: The study aims to develop an artificial intelligence (AI) model based on nnU-Net v2 for automatic maxillary sinus (MS) segmentation in cone beam computed tomography (CBCT) volumes and to evaluate the performance of this model. METHODS: In 101 CBCT scans, MS were annotated using the CranioCatch labelling software (Eskisehir, Turkey) The dataset was divided into 3 parts: 80 CBCT scans for training the model, 11 CBCT scans for model validation, and 10 CBCT scans for testing the model. The model training was conducted using the nnU-Net v2 deep learning model with a learning rate of 0.00001 for 1000 epochs. The performance of the model to automatically segment the MS on CBCT scans was assessed by several parameters, including F1-score, accuracy, sensitivity, precision, area under curve (AUC), Dice coefficient (DC), 95% Hausdorff distance (95% HD), and Intersection over Union (IoU) values. RESULTS: F1-score, accuracy, sensitivity, precision values were found to be 0.96, 0.99, 0.96, 0.96, respectively for the successful segmentation of maxillary sinus in CBCT images. AUC, DC, 95% HD, IoU values were 0.97, 0.96, 1.19, 0.93, respectively. CONCLUSIONS: Models based on nnU-Net v2 demonstrate the ability to segment the MS autonomously and accurately in CBCT images.

The utilization of decision trees on orthopantomographic and lateral panoramic graphs for the diagnosis of unilateral anterior disc displacement of the temporomandibular joint.

BACKGROUND: Investigation is to utilize decision trees in conjunction with orthopantomography (OPT) and lateral panoramic graphy (LPG) to diagnose unilateral anterior disc displacement (ADD) of the temporomandibular joint. METHODS: In this study, 161 patients with images obtained through all three imaging methods, MRI, OPT, and LPG, were selected from the archives. The participants were categorized into two groups: the study group, comprising 89 patients with unilateral anterior disc displacement, and the control group, consisting of 72 healthy individuals. Measurements, including 2 angles (antero-posterior angle and superior-inferior angle) and 3 distance parameters (anterior joint space distance, superior joint space distance, and posterior joint space distance), were conducted on each imaging modality dataset. To assess the obtained measurement data within each patient, the differences from each measurement were calculated. Statistical analysis of the measurement differences between the control and study groups was carried out with independent t test, and decision trees were generated using the SPSS 25 decision tree module 5.0. RESULTS: In ADD patients, it was statistically significantly found that the APA increased while the SIA decreased for angle measurements. But for linear measurements, AS increased while the SS and PS decreased in MRI, OPT, and LPG. CONCLUSION: ADD can be diagnosed in OPT and LPG. The identification of the specific type of ADD that occurs in the temporomandibular joint is not feasible.

Investigation of the relationship between the mandibular third molar teeth and the inferior alveolar nerve using posteroanterior radiographs: a pilot study.

BACKGROUND: The most severe complication that can occur after mandibular third molar (MM3) surgery is inferior alveolar nerve (IAN) damage. It is crucial to have a comprehensive radiographic evaluation to reduce the possibility of nerve damage. The objective of this study is to assess the diagnostic accuracy of panoramic radiographs (PR) and posteroanterior (PA) radiographs in identifying the association between impacted MM3 roots and IAN. METHODS: This study included individuals who had PR, PA radiographs, and cone beam computed tomography (CBCT) and who had at least one impacted MM3. A total of 141 impacted MM3s were evaluated on CBCT images, and the findings were considered gold standard. The relationship between impacted MM3 roots and IAN was also evaluated on PR and PA radiographies. The data was analyzed using the McNemar and Chi-squared tests. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of PR and PA radiographies were determined. RESULTS: Considering CBCT the gold standard, the relationship between MM3 roots and IAN was found to be statistically significant between PR and CBCT (p = 0.00). However, there was no statistically significant relationship between PA radiography and CBCT (0.227). The study revealed that the most prevalent limitation of the PR in assessing the relationship between MM3 roots and IAN was the identification of false-positive relationship. CONCLUSIONS: PA radiography may be a good alternative in developing countries to find out if there is a contact between MM3 roots and IAN because it is easier to get to, cheaper, and uses less radiation.

Marginal adaptation of bulk-fill resin composites with different viscosities in class II restorations: a micro-CT evaluation.

BACKGROUND: The purpose of this study is to evaluate the marginal adaptation of bulk-fill resin composites with different viscosities (paste-like and flowable) in Class II restorations using micro-CT imaging. METHODS: Forty extracted human molars were used. Mesial and distal Class II box cavities (approximately 3 mm x 3 mm x 4 mm) were prepared for each tooth, with cavity floors located 1 mm below the enamel-cementum junction. Following adhesive application, teeth were restored using eight different groups: Group XB: X-tra Base Bulk-fill Flowable (VOCO), Group XF: X-tra Fill Bulk-fill (VOCO), Group FB: Filtek Bulk-fill Posterior (3 M ESPE), Group FF: Filtek Bulk-fill Flowable (3 M ESPE), Group BB: Beautifil-Bulk (SHOFU), Group BF: Beautifil-Bulk Flowable (SHOFU), and Group CO: "as a control group", Clearfil Majesty Posterior (KURARAY) and Group CF: "as a control group", Clearfil Majesty Flow + Clearfil Majesty Posterior (KURARAY). The restored teeth underwent an aging protocol involving 1000 cycles in a water bath fluctuating between 5 ± 1.0 °C and 55 ± 1.0 °C. Post-aging, teeth were immersed in 50% silver nitrate solution for 24 h and then in a film developer solution for 8 h. Microleakage analysis was performed using micro-CT, evaluated with 3D Slicer software. A two-way ANOVA was employed for statistical analysis. RESULTS: Two-way ANOVA results indicated significant effects of both viscosity (p < 0.0001) and composite type (p < 0.0001) on marginal adaptation. Viscosity analysis (comparing flowable and paste-like) revealed no significant differences in the FB-FF, XB-XF and BB-BF groups but significant differences in the and CO-CF group, with flowable type exhibiting less microleakage than paste-like type. CONCLUSIONS: The study suggests that while the viscosity of bulk-fill composites did not significantly affect marginal adaptation, the brand of bulk-fill composite did influence it.

Detection of periodontal bone loss patterns and furcation defects from panoramic radiographs using deep learning algorithm: a retrospective study.

BACKGROUND: This retrospective study aimed to develop a deep learning algorithm for the interpretation of panoramic radiographs and to examine the performance of this algorithm in the detection of periodontal bone losses and bone loss patterns. METHODS: A total of 1121 panoramic radiographs were used in this study. Bone losses in the maxilla and mandibula (total alveolar bone loss) (n = 2251), interdental bone losses (n = 25303), and furcation defects (n = 2815) were labeled using the segmentation method. In addition, interdental bone losses were divided into horizontal (n = 21839) and vertical (n = 3464) bone losses according to the defect patterns. A Convolutional Neural Network (CNN)-based artificial intelligence (AI) system was developed using U-Net architecture. The performance of the deep learning algorithm was statistically evaluated by the confusion matrix and ROC curve analysis. RESULTS: The system showed the highest diagnostic performance in the detection of total alveolar bone losses (AUC = 0.951) and the lowest in the detection of vertical bone losses (AUC = 0.733). The sensitivity, precision, F1 score, accuracy, and AUC values were found as 1, 0.995, 0.997, 0.994, 0.951 for total alveolar bone loss; found as 0.947, 0.939, 0.943, 0.892, 0.910 for horizontal bone losses; found as 0.558, 0.846, 0.673, 0.506, 0.733 for vertical bone losses and found as 0.892, 0.933, 0.912, 0.837, 0.868 for furcation defects (respectively). CONCLUSIONS: AI systems offer promising results in determining periodontal bone loss patterns and furcation defects from dental radiographs. This suggests that CNN algorithms can also be used to provide more detailed information such as automatic determination of periodontal disease severity and treatment planning in various dental radiographs.

The Effect of Gravity on Marginal Integrity of Different Flowable Bulk-Fill Resin Composites.

Background and Objectives: The aim of this quantitative research was to investigate the effect of gravitational forces on the marginal integrity of different bulk-fill composites by micro-CT imaging. Materials and Methods: Fifty caries-free human third molars extracted for prophylactic purposes were used in this study. Each tooth was prepared with two proximal box cavities, with dimensions of 3 mm × 3 mm × 5 mm. Five distinct groups, each comprising 20 cavities, thus totaling 100 cavities for this study: (1, Group CON): Clearfil Majesty Flow + Clearfil Majesty Esthetic (as the control); (2, Group FBR): Filtek Bulk-fill Flowable Restorative + Clearfil Majesty Esthetic; (3, Group XTB): Voco Extrabase + Clearfil Majesty Esthetic; (4, Group SDR): SDR + Clearfil Majesty Esthetic; and (5, Group SNC): Sonicfill. When restoring the mesial cavities, the occlusal surfaces of the teeth in the mold were positioned upwards, counteracting the force of gravity. In contrast, for the restoration of the distal cavities, the occlusal surfaces were aligned downwards, to be parallel with the gravitational pull. After restorative procedures, each tooth was treated with 5000 thermal cycles. A solution of ammoniacal silver nitrate (AgNO3) was employed as a tracing agent. The micro-CT scans were conducted and the total volume of silver nitrate and the total volume of restorations within the relevant region of interest were calculated in "mm3" with software. Two-way ANOVA and Tukey tests were performed at a significance level of p = 0.05 with Graphpad Prism v 8.2.1 software. Results: Both gravity effect and interaction showed no statistical differences (p > 0.05). Statistically significant differences were observed in the restorative materials (p < 0.05). Conclusions: Gravitational forces do not emerge as a major factor affecting the marginal integrity of flowable bulk-fill composites in class II restorations. The chemical composition of the composites plays a more crucial role, with the XTB composite showing higher microleakage ratios compared to the others.

Relationships between Interleukin 18 -607 C/A and -137 G/C, Osteopontin -9250 C/T Genetic Polymorphisms and Systemic Inflammatory Response Syndrome in Coronary Artery Bypass Graft Surgery.

Background and Objectives: Systemic inflammatory response syndrome (SIRS) is one of the most significant complications after on-pump heart surgery procedures. High cytokine levels have been shown after open-heart surgeries and a genetic predisposition seems to be an important underlying modulatory characteristic for SIRS. To investigate the association between interleukin 18 -607 C/A, interleukin 18 -137 G/C and osteopontin 9250 C/T genetic polymorphisms and SIRS in on-pump CABG patients. Materials and Methods: Two hundred consecutive elective on-pump CABG patients were recruited prospectively to the study. Genomic DNA was extracted from whole blood and genotyping was determined by sequence specific PCR or PCR-RFLP methods for related polymorphisms. Results: SIRS incidence was 60.2%, 38.1%, 18.9% on postoperative day 1, 2 and 3, respectively, in the whole study population. The SIRS rate on the second postoperative day was 13% and 43.4%, respectively, in osteopontin 9250 C/T T allele non-carriers and carriers (p = 0.004). WBC (White Blood Cell) counts were higher on day 2 and 3 in osteopontin 9250 C/T T allele carriers compared to non-carriers (day 2; 12.7 ± 4 vs. 10.5 ± 2.4 (p = 0.015), day 3; 11.8 ± 4 vs. 9.1 ± 4.7 (p = 0.035)). The average ICU stay was 3.1 ± 7.4, 1.28 ± 0.97 for IL 18-137 G/C C allele carriers and non-carriers, respectively (p = 0.003), and in the IL 18-137 G/C C allele carriers, SIRS developed in 42.2% by the second postoperative day whereas the rate was 57.8% in non-carriers (p = 0.025). Conclusions: The current research revealed a possible link between osteopontin 9250 C/T and IL18-137 G/C genetic polymorphism and SIRS and morbidity in on-pump CABG patients.

The micro CT evaluation of crown and root pulp volume versus dentin thickness in teeth in postmortem interval (PMI).

Determining the postmortem interval (PMI) is one of the main study subjects of forensic sciences. The main purpose of this prospective in vitro study that was the Micro-CT evaluation of teeth crown and root pulp volume versus dentin thickness in terms of PMI determination. The study involved 60 female Wistar rats, with weights ranging from 270 to 320 g. These rats were grouped into six different post-mortem period categories. Following the animals' sacrifice, they were subjected to a natural putrefaction period, with a control group, in the grounds of a sheltered garden. Hemi-mandible samples were then extracted and placed in glass tubes for Micro-CT evaluations, following the progression of putrefaction processes. The pulp volume and dentin thickness were assessed using Micro-CT, and the gathered data underwent statistical analysis. Micro-CT was employed to analyze sixty right mandibular second molar teeth in the hemi-mandible. The crown pulp volume exhibited a reduction in group 6, with a value of 0.239 mm3 after a three-month period of natural putrefaction (p < 0.001). There is statistically differences among groups in case of pairwise comparison (p < 0.05). However, the root pulp volume and dentin thickness variables did not display any statistically significant changes. Despite certain limitations associated with this study, the Micro-CT findings concerning teeth pulp volume can serve as an objective parameter, especially for late postmortem investigations and the estimation of time of death.

The diagnostic accuracy of cone beam computed tomography in detecting temporomandibular joint bony disorders: a systematic review.

Computed tomography (CT) has been recognized as a robust and dependable technique for delineating osseous alterations and anomalies within hard tissues. The necessity for accurate diagnosis and management of patients with temporomandibular disorders in dental practices has increasingly come to the forefront. There is ongoing scholarly debate regarding the equivalence of diagnostic outcomes yielded by cone beam computed tomography (CBCT), which offers greater accessibility in dental settings than traditional CT, in identifying bony changes within the temporomandibular joint (TMJ). Our principal aim was to conduct a systematic review of studies that compare the efficacy of CT and CBCT in the detailed assessment of bone conditions affecting the TMJ. An electronic search was conducted across databases: PubMed, Medline, Web of Science, Cochrane and Scopus. Two independent reviewers screened titles and abstracts against predefined inclusion criteria. The included articles underwent rigorous critical appraisal, during which relevant data were extracted and systematically presented in a tabular format. This systematic review incorporates 5 studies published between 2006 and 2015. In 3 studies, CBCT demonstrated comparable outcomes to CT, while 2 investigations revealed significantly enhanced accuracy for CBCT compared to CT, with reported accuracies of 0.95 ± 0.04, 0.77 ± 0.17, and 89-91% for CBCT. The aggregated evidence from the included studies indicates that CBCT offers comparable or superior accuracy in detecting osseous changes within TMJ structures. Owing to its lower radiation exposure and increased accessibility, CBCT emerges as the preferred choice over conventional CT for evaluating bony structures of the TMJ.

A potential novel technique for measurement of pulp volume on periapical radiography: A pilot study.

Pulp volume can be assessed during dental treatment. Three-dimensional imaging techniques are not routinely used for this purpose because of high radiation doses. This study aimed to develop a novel method to measure pulp volume using periapical radiography. In this study, cone-beam computed tomography (CBCT) was used as a reference method. Periapical radiography and CBCTs obtained from the same patients (n = 32) were recorded. Pulp volume was determined by observing the density differences between the pulp and peripheral structures using ImageJ. A method of graph and volume calculation was developed for each tooth. The Shapiro-Wilk test and Mann-Whitney U test were used to show normality and non-normal distributions. The Bland-Altman plot was used to show the scattering of the mean versus difference values of the measurements of the two methods used to calculate the pulp volume. Normality was evaluated using the Shapiro-Wilk test. CBCT measurements are normally distributed (p = 0.307), while ImageJ is not normally distributed (p = 0.027). Therefore, the mean difference between the two groups was analyzed using the nonparametric Mann-Whitney U test. There was a statistically significant difference between the CBCT and ImageJ measurements (p = 0.01). According to Spearman's correlation analysis, the results obtained from the novel method were moderately correlated with those obtained from the reference method (r = 0.444). The results of this study indicated that a novel method-based Java software can be used to calculate pulp volume using low-dose radiation containing periapical radiography.

Correction: Alan et al. Evaluation of the Breastfeeding Dynamics of Neonates with Ankyloglossia via a Novel Ultrasonographic Technique. Diagnostics 2023, 13, 3435.

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Prediction of extraction difficulty for impacted maxillary third molars with deep learning approach.

OBJECTIVE: The aim of this study is to determine if a deep learning (DL) model can predict the surgical difficulty for impacted maxillary third molar tooth using panoramic images before surgery. MATERIALS AND METHODS: The dataset consists of 708 panoramic radiographs of the patients who applied to the Oral and Maxillofacial Surgery Clinic for various reasons. Each maxillary third molar difficulty was scored based on dept (V), angulation (H), relation with maxillary sinus (S), and relation with ramus (R) on panoramic images. The YoloV5x architecture was used to perform automatic segmentation and classification. To prevent re-testing of images, participate in the training, the data set was subdivided as: 80 % training, 10 % validation, and 10 % test group. RESULTS: Impacted Upper Third Molar Segmentation model showed best success on sensitivity, precision and F1 score with 0,9705, 0,9428 and 0,9565, respectively. S-model had a lesser sensitivity, precision and F1 score than the other models with 0,8974, 0,6194, 0,7329, respectively. CONCLUSION: The results showed that the proposed DL model could be effective for predicting the surgical difficulty of an impacted maxillary third molar tooth using panoramic radiographs and this approach might help as a decision support mechanism for the clinicians in peri­surgical period.

Retrospective evaluation of ponticulus posticus prevalence, sella turcica types, and stylohyoid complex calcifications in a group of Turkish population.

The aim of this retrospective analysis was to assess the incidence of ponticulus posticus and stylohyoid ligament calcification and to evaluate the morphological variations of the sella turcica within the Turkish demographic using CBCT scans. Involving a retrospective review of 460 CBCT scans and utilizing the NewTom 3G system, the study analyzed high-quality CBCT images to investigate ponticulus posticus, stylohyoid ligament calcifications, and sella turcica morphology. The ponticulus posticus was examined for complete or partial formations, while the stylohyoid ligament was classified according to its elongation and calcification patterns. The sella turcica was categorized into six distinct morphological types, enhancing the understanding of structural variations in the context of the Turkish population. The calcification patterns of the styloid processes were examined on both sides of 380 individuals, revealing the highest prevalence in the 'd' and 'e' categories on the right, and similar findings on the left among 373 individuals. Symmetric calcification patterns were more common, with 68.4% symmetry observed. For the sella turcica, category 'a' was the most frequent among 363 individuals. Analysis of ponticulus posticus absence and presence showed a majority lacking this feature on both sides, with complete and partial forms less common. The study highlights the anatomical variability and bilateral symmetry of the styloid processes, sella turcica, and ponticulus posticus, illustrating that these structures do not significantly vary with gender or age. These results hold clinical significance for the diagnosis and treatment of related conditions, prompting further investigation into their impact on patient care.

The Detection of Pulp Stones with Automatic Deep Learning in Panoramic Radiographies: An AI Pilot Study.

This study aims to evaluate the effectiveness of employing a deep learning approach for the automated detection of pulp stones in panoramic imaging. A comprehensive dataset comprising 2409 panoramic radiography images (7564 labels) underwent labeling using the CranioCatch labeling program, developed in Eskisehir, Turkey. The dataset was stratified into three distinct subsets: training (n = 1929, 80% of the total), validation (n = 240, 10% of the total), and test (n = 240, 10% of the total) sets. To optimize the visual clarity of labeled regions, a 3 × 3 clash operation was applied to the images. The YOLOv5 architecture was employed for artificial intelligence modeling, yielding F1, sensitivity, and precision metrics of 0.7892, 0.8026, and 0.7762, respectively, during the evaluation of the test dataset. Among deep learning-based artificial intelligence algorithms applied to panoramic radiographs, the use of numerical identification for the detection of pulp stones has achieved remarkable success. It is expected that the success rates of training models will increase by using datasets consisting of a larger number of images. The use of artificial intelligence-supported clinical decision support system software has the potential to increase the efficiency and effectiveness of dentists.

A deep learning approach to detection of oral cancer lesions from intra oral patient images: A preliminary retrospective study.

INTRODUCTION: Oral squamous cell carcinomas (OSCC) seen in the oral cavity are a category of diseases for which dentists may diagnose and even cure. This study evaluated the performance of diagnostic computer software developed to detect oral cancer lesions in intra-oral retrospective patient images. MATERIALS AND METHODS: Oral cancer lesions were labeled with CranioCatch labeling program (CranioCatch, Eskisehir, Turkey) and polygonal type labeling method on a total of 65 anonymous retrospective intraoral patient images of oral mucosa that were diagnosed with oral cancer histopathologically by incisional biopsy from individuals in our clinic. All images have been rechecked and verified by experienced experts. This data set was divided into training (n = 53), validation (n = 6) and test (n = 6) sets. Artificial intelligence model was developed using YOLOv5 architecture, which is a deep learning approach. Model success was evaluated with confusion matrix. RESULTS: When the success rate in estimating the images reserved for the test not used in education was evaluated, the F1, sensitivity and precision results of the artificial intelligence model obtained using the YOLOv5 architecture were found to be 0.667, 0.667 and 0.667, respectively. CONCLUSIONS: Our study reveals that OCSCC lesions carry discriminative visual appearances, which can be identified by deep learning algorithm. Artificial intelligence shows promise in the prediagnosis of oral cancer lesions. The success rates will increase in the training models of the data set that will be formed with more images.