Comparative examination of trabecular bone structure in healthy and diabetic patients with periodontitis using fractal analysis.

OBJECTIVES: Fractal analysis is a numerical method that indicates the structural patterns and complexity of the trabecular bone on radiographs. The aim of this cross-sectional study was to evaluate the trabecular bone structure in systemically healthy patients and diabetes mellitus patients with periodontitis using fractal analysis. METHOD AND MATERIALS: The study included 125 mandibular first molars of nonsmoker patients. The subjects were divided into five subgroups: diabetes mellitus patients with mild-moderate periodontitis, diabetes mellitus patients with advanced periodontitis, systemically healthy individuals with mild-moderate periodontitis, systemically healthy individuals with advanced periodontitis, and systemically healthy individuals with gingivitis (control group). Clinical periodontal parameters (pocket depth, bleeding on probing, clinical attachment loss, and bone loss) were recorded. Two specific sites located in the mesial-distal regions (n = 250) of the mandibular first molars were identified using periapical radiographs captured with a parallel technique. Fractal analysis values were calculated using the box-counting method. One-way analysis of variance (ANOVA), and Pearson correlation analysis were used for statistical evaluation. RESULTS: The highest fractal analysis values were observed in systemically healthy with gingivitis patients (mesial fractal analysis: 1.86 ± 0.01; distal fractal analysis: 1.85 ± 0.01). Patients with periodontitis (mesial fractal analysis: 1.78 ± 0.02; distal fractal analysis: 1.79 ± 0.01) exhibited lower fractal analysis values compared to the control group. There was no significant difference in mesial and distal fractal analysis values between all periodontitis groups. No correlation was found between age, sex, clinical attachment loss, bone loss, and fractal analysis (P > .05). CONCLUSIONS: Although fractal analysis values were lower in the periodontitis groups compared to the control group, fractal analysis did not demonstrate any periodontitis-associated changes of bone trabeculation in diabetes at any stage of periodontitis. Furthermore, there was no significant association between fractal analysis values and age, sex, clinical attachment, and bone loss.

Comparison of gray-level detectability on computer monitors among several dental specialties: A web-based study.

BACKGROUND: Diagnosis in dentistry begins with the correct reading and interpreting of the dental radiograph. OBJECTIVES: The aim of the present study was to examine the effects of the imaging technique used, the dentistry specialty and the years of experience on the gray-level perception among dentists. MATERIAL AND METHODS: A custom web application was developed. Dentomaxillofacial radiologists (DentRads), endodontists (Ends) and general dental practitioners (GDPs) were invited via e-mail to participate in the study. A total of 46 participants met the requirements of the test. The test comprised 2 webpages. On the 1st page, the participants were asked for information such as gender, specialty, the years of experience, and the imaging techniques they used. Then, on the 2nd page, they were welcomed with instructions and directions, and asked to rearrange 85 gray color tones represented by square bars of equal dimensions. These mixed gray bars were placed in 4 rows according to the principles of the FarnsworthMunsell 100-hue test (FM). Each clinician's test results were recorded in a database. The individual's level of recognition of gray tones was evaluated through the total error score (TES), which was calculated using a web-based independent scoring software program. Lower TES values were a desirable result, indicating fewer misplacement, while higher scores indicated more misplacements of gray tones. The testing time (TT) was recorded automatically. RESULTS: The years of the participants' experience as dentists or specialists did not affect TES or TT. The dentists who used the charge-coupled device-complementary metal oxide semiconductor (CCD-CMOS) had lower TES values than those who used analog radiographs (p < 0.05). CONCLUSIONS: While the specialty and the years of experience did not affect the clinicians' ability to recognize gray tones, the digital imaging techniques (photostimulable phosphor (PSP) and CCD/CMOS) could improve the clinicians' gray-level perception.

Detection of the separated root canal instrument on panoramic radiograph: a comparison of LSTM and CNN deep learning methods.

OBJECTIVES: A separated endodontic instrument is one of the challenging complications of root canal treatment. The purpose of this study was to compare two deep learning methods that are convolutional neural network (CNN) and long short-term memory (LSTM) to detect the separated endodontic instruments on dental radiographs. METHODS: Panoramic radiographs from the hospital archive were retrospectively evaluated by two dentists. A total of 915 teeth, of which 417 are labeled as "separated instrument" and 498 are labeled as "healthy root canal treatment" were included. A total of six deep learning models, four of which are some varieties of CNN (Raw-CNN, Augmented-CNN, Gabor filtered-CNN, Gabor-filtered-augmented-CNN) and two of which are some varieties of LSTM model (Raw-LSTM, Augmented-LSTM) were trained based on several feature extraction methods with an applied or not applied an augmentation procedure. The diagnostic performances of the models were compared in terms of accuracy, sensitivity, specificity, positive- and negative-predictive value using 10-fold cross-validation. A McNemar's tests was employed to figure out if there is a statistically significant difference between performances of the models. Receiver operating characteristic (ROC) curves were developed to assess the quality of the performance of the most promising model (Gabor filtered-CNN model) by exploring different cut-off levels in the last decision layer of the model. RESULTS: The Gabor filtered-CNN model showed the highest accuracy (84.37 ± 2.79), sensitivity (81.26 ± 4.79), positive-predictive value (84.16 ± 3.35) and negative-predictive value (84.62 ± 4.56 with a confidence interval of 80.6 ± 0.0076. McNemar's tests yielded that the performance of the Gabor filtered-CNN model significantly different from both LSTM models (p < 0.01). CONCLUSIONS: Both CNN and LSTM models were achieved a high predictive performance on to distinguish separated endodontic instruments in radiographs. The Gabor filtered-CNN model without data augmentation gave the best predictive performance.

A Fused Deep Learning Architecture for the Detection of the Relationship between the Mandibular Third Molar and the Mandibular Canal.

The study aimed to generate a fused deep learning algorithm that detects and classifies the relationship between the mandibular third molar and mandibular canal on orthopantomographs. Radiographs (n = 1880) were randomly selected from the hospital archive. Two dentomaxillofacial radiologists annotated the data via MATLAB and classified them into four groups according to the overlap of the root of the mandibular third molar and mandibular canal. Each radiograph was segmented using a U-Net-like architecture. The segmented images were classified by AlexNet. Accuracy, the weighted intersection over union score, the dice coefficient, specificity, sensitivity, and area under curve metrics were used to quantify the performance of the models. Also, three dental practitioners were asked to classify the same test data, their success rate was assessed using the Intraclass Correlation Coefficient. The segmentation network achieved a global accuracy of 0.99 and a weighted intersection over union score of 0.98, average dice score overall images was 0.91. The classification network achieved an accuracy of 0.80, per class sensitivity of 0.74, 0.83, 0.86, 0.67, per class specificity of 0.92, 0.95, 0.88, 0.96 and AUC score of 0.85. The most successful dental practitioner achieved a success rate of 0.79. The fused segmentation and classification networks produced encouraging results. The final model achieved almost the same classification performance as dental practitioners. Better diagnostic accuracy of the combined artificial intelligence tools may help to improve the prediction of the risk factors, especially for recognizing such anatomical variations.

Should artificial intelligence integrate with dental education? An assessment through the dentomaxillofacial radiology perspective

Introduction: The requirement to adapt dentistry education to the growing knowledge and big data is evident. Future dentists will participate in AI studies as both researchers and users. The main aim was to evaluate the attitudes of undergraduate dental students on artificial intelligence (AI) applications. Secondarily, it was aimed to discuss possible solutions for the integration of AI into education in particular to dentomaxillofacial radiology. Material and Method: A written survey included 16 questions with a 5-point Likert-scale was designed. The content of the survey included basic knowledge about AI terminology, applications on dentomaxillofacial radiology, and future estimations. One hundred seventy-six students attending the 3rd, 4th, and 5th grades were included. The attitudes of the students were assessed with the total score. The responses were scored as: Strongly disagree: 1, Disagree: 2, Neutral: 3, Agree: 4, Strongly agree: 5. The minimum and maximum possible points were 16 and 80, and 48 was the middle score. The scores were classified as 16-31 (group 1), 32-47 (group 2), 48-63 (group 3), and 64-80 (group 4). Cronbach's alpha reliability coefficient was used to test the internal consistency of the questions. One-way analysis of variance and Chi-square test were used to compare normally distributed data. The Mann-Whitney U test was used to compare the that did not show normal distribution. Statistical significance was evaluated with a 5% Type-I error level. Results: The Cronbach's alpha reliability coefficient was 0.849. The response rate of the participants was 83.41% (n=176). The mean total scale score was 57.68 ± 0.651. Group 3 had the largest cluster (67.61%; n=119), whereas the group 1 had the smallest (0.56%; n=1). The total scale score showed no statistically significant difference between the academic years. Conclusion: The attitudes of undergraduate dental students on AI were positive and students are aware of the potential of applications in the field. The conventional dentomaxillofacial radiology curriculum requires an update.

A CBCT based digital analysis of shaping ability of Max Wire® alloy file in the treatment of large and curved root canals

Aim: To evaluate the limits of the shaping ability of MaxWire® alloy file in the treatment of pre-created large and curved root canals with different apical sizes by using cone-beam computed tomography (CBCT). Materials and methods: Forty-five permanent maxillary first molars with moderately curved palatal roots (20°-30°) were divided into three groups, and large root canals were created with apical diameter #35 (Group 1), #40 (Group 2) or #50 (Group 3) by using BioRace NiTi System. Then, they were reshaped with the MaxWire alloy file, XP-endo Shaper®. Canals were scanned before and after instrumentation by using the CBCT scanner. Canal transportation (CT), centering ratio (CR), % increased prepared area (PA), and % increased prepared outline (PO) at 2, 3, and 4 mm from the apex were calculated. Data were statistically analyzed at P < 0.05. Results: There was a significant difference in the mean increase in PO in all apical sizes and all three levels. At both 3 mm and 4 mm levels the maximum PA and PO were achieved in apical size 35, while at 2 mm level the maximum values were obtained in apical size 40. There was no statistically significant difference in CT and CR within groups at 2 mm distance from the apex. Conclusion: Max Wire alloy technology of this novel instrument makes it possible to clean and touch the dentin walls of large and curved root canals. Small FOV and small voxel size of CBCT could also be used in shaping ability studies in endodontics.

Prevalence and characteristics of pneumatizations of the articular eminence and roof of the glenoid fossa on cone-beam computed tomography.

OBJECTIVE: The aim of this study was to determine the prevalence and characteristics of pneumatization of the articular tubercle (PAT) and pneumatization of the roof of the glenoid fossa (PRGF) in a large population using cone-beam computed tomography (CBCT). MATERIALS AND METHODS: This study was designed to evaluate the CBCT images of 1000 patients. The prevalences of the pneumatizations by age, sex, locularity, and laterality were determined. The significance of differences between variables was evaluated by the Chi-square test and analysis of variance. RESULTS: PAT was detected in 28.4% of the zygomatic bone sides and PRGF in 29.6%. Bilateral PAT was detected in 176 (17.6%) patients and bilateral PRGF in 195 (19.5%). The mean age of patients with PAT was 47.33 years and that of patients with PRGF was 45.62 years. Multilocular appearance was observed significantly more often than unilocular type for both pneumatizations (p < 0.01). Unilateral PAT cases were slightly, but significantly, higher than bilateral PAT cases (p = 0.047), while no significant difference was observed between unilateral and bilateral PRGF cases. CONCLUSIONS: In conclusion, PAT and PRGF can be assessed more accurately on CBCT images than on plain radiographs. During routine radiological investigations, maxillofacial radiologists should be aware of zygomatic air cells.

Evaluation of the prevalence of bifid mandibular condyle detected on cone beam computed tomography images in a Turkish population / Evaluación de la prevalencia de cóndilo mandibular bífido detectado en tomografía computadorizada cone-beam en una población turca

The aim of this study was to assess the frequency of the BMC phenomenon in a Turkish patient population. Cone beam computed tomography (CBCT) images of 2634 consecutive patients were retrospectively reviewed. The Chi-squared test was used to determine potential differences in the distribution of BMCs when stratified by sex and side. Among the 2634 patients, 42 (1.7%) patients were found to have BMC. Of these 42 patients, 22 were female (0.8%) and 20 were male (0.7%) with age ranging from 29 to 68 years (mean age 47.47). Among the 42 patients, 39 (92.8%) of the BMCs were unilateral and three (7.1%) were bilateral. Approximately 24 cases (53.3%) were on the right side, and 21 cases (46.6%) were on the left side. All of the BMCs showed a mediolateral orientation. The mean depth of the BMC was 2.55 mm in males and 2.68 mm in females. 2 patients have symptoms whereas the other patients were atraumatic and asymptomatic. BMC is a rare condition that might be more prevalent in the Turkish population. Greater detailed information regarding BMC could be obtained by the widespread use of CBCT in epidemiological studies.

El objetivo de este estudio fue evaluar la frecuencia del fenómeno CMB en una población de pacientes de Turquía. Se revisaron imágenes consecutivas de tomografía computarizada (CBCT) de 2.634 pacientes retrospectivamente. Se utilizó la prueba de Chi-cuadrado para determinar las posibles diferencias en la distribución de CMB estratificado por sexo y lado. Entre los 2.634 pacientes, se encontró que 42 (1,7%) pacientes tenían CMB. De estos 42 pacientes, 22 eran mujeres (0,8%) y 20 eran varones (0,7%), con edades entre 29 a 68 años (promedio edad 47,47). Entre los 42 pacientes, 39 (92,8%) del CMB fueron unilaterales y tres (7,1%) fueron bilaterales. Aproximadamente 24 casos (53,3%) estaban en el lado derecho, y 21 casos (46,6%) estaban en el lado izquierdo. Todas las CMB mostraron una orientación mediolateral. La profundidad media de la CMB era 2,55 mm en los hombres y 2,68 mm en las mujeres. Dos de los pacientes presentaron síntomas, mientras que en el resto de los pacientes no presentó trauma ni síntomas. CMB es una afección poco común que podría ser más frecuente en la población turca. Mayor información y detalle sobre CMB se podría obtener en estudios epidemiológicos con el uso generalizado de CBCT.