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.

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.

Progress of artificial intelligence-driven solutions for automated segmentation of dental pulp cavity on cone-beam computed tomography images. A systematic review.

INTRODUCTION: Automated segmentation of three-dimensional pulp space on cone-beam computed tomography (CBCT) images presents a significant opportunity for enhancing diagnosis, treatment planning, and clinical education in endodontics. The aim of this systematic review was to investigate the performance of AI-driven automated pulp space segmentation on CBCT images. METHODS: A comprehensive electronic search was performed using PubMed, Web of Science, and Cochrane databases, up until February 2024. Two independent reviewers participated in the selection of studies, data extraction, and evaluation of the included studies. Any disagreements were resolved by a third reviewer. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to assess the risk of bias. RESULTS: Thirteen studies that met the eligibility criteria were included. Most studies demonstrated high accuracy in their respective segmentation methods, although there was some variation across different structures (pulp chamber, root canal) and tooth types (single-rooted, multi-rooted). Automated segmentation showed slightly superior performance for segmenting the pulp chamber compared to the root canal and single-rooted teeth compared to multi-rooted ones. Furthermore, second mesiobuccal (MB2) canal segmentation also demonstrated high performance. In terms of time efficiency, the minimum time required for segmentation was 13 seconds. CONCLUSION: AI-driven models demonstrated outstanding performance in pulp space segmentation. Nevertheless, these findings warrant careful interpretation, and their generalizability is limited due to the potential risk and low evidence level arising from inadequately detailed methodologies and inconsistent assessment techniques. In addition, there is room for further improvement, specifically for root canal segmentation and testing of AI performance in artifact-induced images.

Validation of Artificial Intelligence Application for Dental Caries Diagnosis on Intraoral Bitewing and Periapical Radiographs.

OBJECTIVES: This study aimed to assess the reliability of AI-based Diagnocat system that assists the healthcare processes in the diagnosis of caries on intraoral radiographs. METHODS: The proximal surfaces of the 323 selected teeth on the intraoral radiographs were evaluated by two independent observers using the AI-based Diagnocat system. The presence or absence of carious lesions was recorded during Phase 1. After 4 months, the AI-aided human observers evaluated the same radiographs (Phase 2), and the advanced convolutional neural network (CNN) reassessed the radiographic data (Phase 3). Subsequently, data reflecting human disagreements were excluded (Phase 4). For each phase, the Cohen and Fleiss kappa values, as well as the sensitivity, specificity, positive and negative predictive values, and diagnostic accuracy ​​of Diagnocat, were calculated. RESULTS: During the four phases, the range of Cohen kappa values between the human observers and Diagnocat were κ=0.66-1, κ=0.58-0.7, and κ=0.49-0.7. The Fleiss kappa values were κ=0.57-0.8. The sensitivity, specificity and diagnostic accuracy values ranged between 0.51-0.76, 0.88-0.97 and 0.76-0.86, respectively. CONCLUSIONS: The Diagnocat CNN supports the evaluation of intraoral radiographs for caries diagnosis, as determined by consensus between human and AI system observers. CLINICAL SIGNIFICANCE: Our study may aid in the understanding of deep learning-based systems developed for dental imaging modalities for dentists and contribute to expanding the body of results in the field of AI-supported dental radiology, as concluded by other scientific publications.

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.

Tooth numbering and classification on bitewing radiographs: an artificial intelligence pilot study.

OBJECTIVE: The aim of this study is to assess the efficacy of employing a deep learning methodology for the automated identification and enumeration of permanent teeth in bitewing radiographs. The experimental procedures and techniques employed in this study are described in the following section. STUDY DESIGN: A total of 1248 bitewing radiography images were annotated using the CranioCatch labeling program, developed in Eskisehir, Turkey. The dataset has been partitioned into 3 subsets: training (n = 1000, 80% of the total), validation (n = 124, 10% of the total), and test (n = 124, 10% of the total) sets. The images were subjected to a 3 × 3 clash operation in order to enhance the clarity of the labeled regions. RESULTS: The F1, sensitivity and precision results of the artificial intelligence model obtained using the Yolov5 architecture in the test dataset were found to be 0.9913, 0.9954, and 0.9873, respectively. CONCLUSION: The utilization of numerical identification for teeth within deep learning-based artificial intelligence algorithms applied to bitewing radiographs has demonstrated notable efficacy. The utilization of clinical decision support system software, which is augmented by artificial intelligence, has the potential to enhance the efficiency and effectiveness of dental practitioners.

Microcomputed tomography as a diagnostic tool for detection of lymph node metastasis in non-small cell lung cancer: A decision-support approach for pathological examination "A pilot study for method validation".

Background: Non-small cell lung cancer (NSCLC) patients without lymph node (LN) metastases (pN0) may exhibit different survival rates, even when their T stage is similar. This divergence could be attributed to the current pathology practice, wherein LNs are examined solely in two-dimensional (2D). Unfortunately, adhering to the protocols of 2D pathological examination does not ensure the exhaustive sampling of all excised LNs, thereby leaving room for undetected metastatic foci in the unexplored depths of tissues. The employment of micro-computed tomography (micro-CT) facilitates a three-dimensional (3D) evaluation of all LNs without compromising sample integrity. In our study, we utilized quantitative micro-CT parameters to appraise the metastatic status of formalin-fixed paraffin-embedded (FFPE) LNs. Methods: Micro-CT scans were conducted on 12 FFPEs obtained from 8 NSCLC patients with histologically confirmed mediastinal LN metastases. Simultaneously, whole-slide images from these FFPEs underwent scanning, and 47 regions of interest (ROIs) (17 metastatic foci, 11 normal lymphoid tissues, 10 adipose tissues, and 9 anthracofibrosis) were marked on scanned images. Quantitative structural variables obtained via micro-CT analysis from tumoral and non-tumoral ROIs, were analyzed. Result: Significant distinctions were observed in linear density, connectivity, connectivity density, and closed porosity between tumoral and non-tumoral ROIs, as indicated by kappa coefficients of 1, 0.90, 1, and 1, respectively. Receiver operating characteristic analysis substantiated the differentiation between tumoral and non-tumoral ROIs based on thickness, linear density, connectivity, connectivity density, and the percentage of closed porosity. Conclusions: Quantitative micro-CT parameters demonstrate the ability to distinguish between tumoral and non-tumoral regions of LNs in FFPEs. The discriminatory characteristics of these quantitative micro-CT parameters imply their potential usefulness in developing an artificial intelligence algorithm specifically designed for the 3D identification of LN metastases while preserving the FFPE tissue.

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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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.

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.

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 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.

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.

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.

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.

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.

Dental caries and associated factors among Turkish children and adults: Findings from the 3rd National Oral Health Survey.

BACKGROUND: Information on the current status of oral health and related risk factors is lacking in Turkiye. This study aimed to evaluate dental caries and associated factors among the Turkish population using the findings of the third national oral health survey. METHODS: This cross-sectional epidemiological survey was carried out between March and September in 2018 including five index ages and age groups (5-, 12-, 15-year-olds, 35-44 and 65-74 age groups). A multistage cluster sampling design was used to obtain a national representative sample of these age groups. Participants were interviewed face-to-face using a structured questionnaire, and oral examinations were performed based on the methods and criteria suggested by the World Health Organization. The adjusted prevalence ratio for dental caries was estimated considering sociodemographic and behavioural characteristics by using log-binomial and robust Poisson regression analysis in R and SAS software. RESULTS: After data cleaning, a total of 11 091 participants remained. The prevalence of dental caries was 76.5% (95% confidence interval: 75.4%-77.7%) and determined to be associated with age, dental visit, and pain or discomfort history during the previous year among the Turkish population. Prevalence was also associated with the mother's education level among 5-, 12- and 15-year-olds. For the 35-44 age group, individuals living in urban areas had lower dental caries than those living in rural areas. For 65-74 age group, none of the factors were significantly associated with dental caries. CONCLUSION: A high prevalence of dental caries was observed in Turkish children and adults. Therefore, it is necessary to strengthen current preventive oral health programs. Regular disease surveillance should be maintained. National oral health targets should be formulated in accordance with Turkiye's needs, resources and structure.

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.

Effects of N-acetyl-L-cysteine against apical periodontitis in rats with adriamycin-induced cardiomyopathy and nephropathy.

AIM: This study aimed to investigate the potential protective effects of N-acetyl-L-cysteine (NAC) against apical periodontitis (AP) in rats with adriamycin (ADR)-induced kidney and heart diseases. METHODOLOGY: Fourty-eight Wistar albino rats were divided into six groups: (1) Control group, (2) ADR group (1 mg/kg/day ip for 10 days), (3) AP Group (1st mandibular molar tooth), (4) AP + ADR Group, (5) AP + NAC group (150 mg/kg/day ip), and (6) AP + ADR + NAC group. After 3 weeks, the rats were decapitated and blood and tissue samples (heart, kidney, and jaw) were collected. Tissue samples were evaluated by biochemical (inflammatory cytokines and hemodynamic parameters) and radiological analyses. One-way anova with Tukey post hoc tests was used to compare data, considering p < .05 as statistically significant. RESULTS: The serum levels of TNF-α, IL-1ß, BUN, Creatinine, CK, and LDH were elevated in the test groups compared with the control group, and treatment with NAC reduced these levels (p < .05). Heart and kidney tissue analysis showed a higher heart-to-body weight ratio (HW/BW) and kidney-to-body weight ratio (KW/BW) in the test groups compared with the control group (p < .05). No significant differences in HW/BW and KW/BW were found between the control and AP + NAC groups. Volumetric apical bone resorption analysis showed an increase in periapical radiolucencies in AP-induced groups indicating apical periodontitis. NAC treatment reduced the total area and volume of resorption cavities (p < .05). CONCLUSIONS: The results suggest that NAC's antioxidant and anti-inflammatory effects can reduce adriamycin-mediated heart and kidney damage and may have a positive effect on apical periodontitis in individuals with nephropathy and cardiomyopathy.

Variations and thicknesses of anterior belly of the digastric muscle: an ultrasonographic study.

PURPOSE: This study aims to evaluate the anatomy and anatomical variations of the anterior belly of the digastric muscle. METHODS: Hundred and fifty one ultrasonographic images of the digastric muscle pairs were evaluated in Near East University Faculty of Dentistry Department of Dentomaxillofacial Radiology. Morphological variations were recorded using the classification of the digastric muscle into 12 types by Kim et al. For the analysis, the Mann-Whitney U test and Chi-square test were used, and for the correlational analysis, Spearman's rho test was applied. P < 0.05 was considered statistically significant in all tests. RESULTS: Seventy female and 81 male patients aged 19-60 years were evaluated. Type 1 digastric muscle was observed in 145 of 151 patients, Type 2 in 3 patients, and Type 7 in 3 patients. The thicknesses of the right and left digastric muscles were measured, and a statistically significant difference was observed between the groups when the genders were compared. It was observed that both right and left digastric muscle thicknesses were higher in males than females. There was a statistically significant positive high correlation between right and left digastric muscle thicknesses (p = 0.000; r = 0.736). No statistically significant difference was found between genders (p = 0.596) in terms of anatomical variations. CONCLUSION: Considering that the variations of the digastric muscle may have a clinical significance role, the normal anatomy and variations of this muscle should be well known by maxillofacial surgeons and radiologists.