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Abstract Background This study aims to evaluate the performance of a deep learning system for the evaluation of tooth development stages on images obtained from panoramic radiographs from child patients. Methods The study collected a total of 1500 images obtained from panoramic radiographs from child patients between the ages of 5 and 14 years. YOLOv5, a convolutional neural network (CNN)-based object detection model, was used to automatically detect the calcification states of teeth. Images obtained from panoramic radiographs from child patients were trained and tested in the YOLOv5 algorithm. True-positive (TP), false-positive (FP), and false-negative (FN) ratios were calculated. A confusion matrix was used to evaluate the performance of the model. Results Among the 146 test group images with 1022 labels, there were 828 TPs, 308 FPs, and 1 FN. The sensitivity, precision, and F1-score values of the detection model of the tooth stage development model were 0.99, 0.72, and 0.84, respectively. Conclusions In conclusion, utilizing a deep learning-based approach for the detection of dental development on pediatric panoramic radiographs may facilitate a precise evaluation of the chronological correlation between tooth development stages and age. This can help clinicians make treatment decisions and aid dentists in finding more accurate treatment options.

Abstract Objectives In the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic detection, segmentation, and numbering of deciduous and permanent teeth in mixed dentition pediatric patients based on PRs. Methods A total of 3854 mixed pediatric patients PRs were labelled for deciduous and permanent teeth using the CranioCatch labeling program. The dataset was divided into three subsets: training (n = 3093, 80% of the total), validation (n = 387, 10% of the total) and test (n = 385, 10% of the total). An artificial intelligence (AI) algorithm using YOLO-v5 models were developed. Results The sensitivity, precision, F-1 score, and mean average precision-0.5 (mAP-0.5) values were 0.99, 0.99, 0.99, and 0.98 respectively, to teeth detection. The sensitivity, precision, F-1 score, and mAP-0.5 values were 0.98, 0.98, 0.98, and 0.98, respectively, to teeth segmentation. Conclusions YOLO-v5 based models can have the potential to detect and enable the accurate segmentation of deciduous and permanent teeth using PRs of pediatric patients with mixed dentition.

Abstract Background The purpose of this study was to investigate the morphology of maxillary first premolar mesial root concavity and to analyse its relation to periodontal bone loss (BL) using cone beam computed tomography (CBCT) and panoramic radiographs. Methods The mesial root concavity of maxillary premolar teeth was analysed via CBCT. The sex and age of the patients, starting position and depth of the root concavity, apicocoronal length of the concavity on the crown or root starting from the cementoenamel junction (CEJ), total apicocoronal length of the concavity, amount of bone loss both in CBCT images and panoramic radiographs, location of the furcation, length of the buccal and palatinal roots, and buccopalatinal cervical root width were measured. Results A total of 610 patients’ CBCT images were examined, and 100 were included in the study. The total number of upper premolar teeth was 200. The patients were aged between 18 and 65 years, with a mean age of 45.21 ± 13.13 years. All the teeth in the study presented mesial root concavity (100%, n = 200). The starting point of concavity was mostly on the cervical third of the root (58.5%). The mean depth and buccolingual length measurements were 0.96 mm and 4.32 mm, respectively. Depth was significantly related to the amount of alveolar bone loss (F = 5.834, p = 0.001). The highest average concavity depth was 1.29 mm in the group with 50% bone loss. The data indicated a significant relationship between the location of the furcation and bone loss (X2 = 25.215, p = 0.003). Bone loss exceeded 50% in 100% of patients in whom the furcation was in the cervical third and in only 9.5% of patients in whom the furcation was in the apical third (p = 0.003). Conclusions According to the results of this study, the depth of the mesial root concavity and the coronal position of the furcation may increase the amount of alveolar bone loss. Clinicians should be aware of these anatomical factors to ensure accurate treatment planning and successful patient management.

Abstract Background Deep learning model trained on a large image dataset, can be used to detect and discriminate targets with similar but not identical appearances. The aim of this study is to evaluate the post-training performance of the CNN-based YOLOv5x algorithm in the detection of white spot lesions in post-orthodontic oral photographs using the limited data available and to make a preliminary study for fully automated models that can be clinically integrated in the future. Methods A total of 435 images in JPG format were uploaded into the CranioCatch labeling software and labeled white spot lesions. The labeled images were resized to 640 × 320 while maintaining their aspect ratio before model training. The labeled images were randomly divided into three groups (Training:349 images (1589 labels), Validation:43 images (181 labels), Test:43 images (215 labels)). YOLOv5x algorithm was used to perform deep learning. The segmentation performance of the tested model was visualized and analyzed using ROC analysis and a confusion matrix. True Positive (TP), False Positive (FP), and False Negative (FN) values were determined. Results Among the test group images, there were 133 TPs, 36 FPs, and 82 FNs. The model’s performance metrics include precision, recall, and F1 score values of detecting white spot lesions were 0.786, 0.618, and 0.692. The AUC value obtained from the ROC analysis was 0.712. The mAP value obtained from the Precision-Recall curve graph was 0.425. Conclusions The model’s accuracy and sensitivity in detecting white spot lesions remained lower than expected for practical application, but is a promising and acceptable detection rate compared to previous study. The current study provides a preliminary insight to further improved by increasing the dataset for training, and applying modifications to the deep learning algorithm. Clinical revelance Deep learning systems can help clinicians to distinguish white spot lesions that may be missed during visual inspection.

Abstract 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.

In this study, it was aimed to determine the 21st century learner skills usage levels (cognitive skills, autonomous skills, collaboration and flexibility skills, and innovation skills) of Turkish language teacher candidates in terms of some variables (gender, grade level, success status). In this study, singular screening, causal comparison and relational screening models were used together within the framework of quantitative research approaches. The respondents were 259 students studying in Turkish Language Teaching Program of Turkish and Social Sciences Department at Balikesir University Necatibey College of Education in the 2018-2019 academic year. The "21st Century Learner Skills Usage Scale" developed by Orhan Göksün (2016) was used as the data collection tool. The scoring of the five-point Likert-type scale consisting of 31 items was made from never (1) to always (5). The 31 items in the scale were gathered under four factors as cognitive skills, autonomous skills, collaboration and flexibility skills, and innovation skills. In the analysis of the data obtained, mean for descriptive analysis, t-test and analysis of variance techniques for comparisons made by the related variables were used. In the study, it was found that the 21st century learner skills usage levels of Turkish language teacher candidates differed in favor of males only in the innovation skills dimension of the scale. It was concluded that as the grade level and grade point average increased, the level of 21st century learner skills use of the participants also increased.

BackgroundTürkiye confirmed its first case of SARS-CoV-2 on March 11, 2020, coinciding with the declaration of the global COVID-19 pandemic. Subsequently, Türkiye swiftly increased testing capacity and implemented genomic sequencing in 2020. This paper describes Türkiye’s journey of establishing genomic surveillance as a middle-income country with limited prior sequencing capacity and analyses sequencing data from the first two years of the pandemic. We highlight the achievements and challenges experienced and distill globally relevant lessons.MethodsWe tracked the evolution of the COVID-19 pandemic in Türkiye from December 2020 to February 2022 through a timeline and analysed epidemiological, vaccination, and testing data. To investigate the phylodynamic and phylogeographic aspects of SARS-CoV-2, we used Nextstrain to analyze 31,629 high-quality genomes sampled from seven regions nationwide.ResultsTürkiye’s epidemiological curve, mirroring global trends, featured four distinct waves, each coinciding with the emergence and spread of variants of concern (VOCs). Utilizing locally manufactured kits to expand testing capacity and introducing variant-specific quantitative reverse transcription polymerase chain reaction (RT-qPCR) tests developed in partnership with a private company was a strategic advantage in Türkiye, given the scarcity and fragmented global supply chain early in the pandemic. Türkiye contributed more than 86,000 genomic sequences to global databases by February 2022, ensuring that Turkish data was reflected globally. The synergy of variant-specific RT-qPCR kits and genomic sequencing enabled cost-effective monitoring of VOCs. However, data analysis was constrained by a weak sequencing sampling strategy and fragmented data management systems, limiting the application of sequencing data to guide the public health response. Phylodynamic analysis indicated that Türkiye’s geographical position as an international travel hub influenced both national and global transmission of each VOC despite travel restrictions.ConclusionThis paper provides valuable insights into the testing and genomic surveillance systems adopted by Türkiye during the COVID-19 pandemic, proposing important lessons for countries developing national systems. The findings underscore the need for robust testing and sampling strategies, streamlined sample referral, and integrated data management with metadata linkage and data quality crucial for impactful epidemiological analysis. We recommend developing national genomic surveillance strategies to guide sustainable and integrated expansion of capacities built for COVID-19 and to optimize the effective utilization of sequencing data for public health action.

Abstract Background Panoramic radiographs, in which anatomic landmarks can be observed, are used to detect cases closely related to pediatric dentistry. The purpose of the study is to investigate the success and reliability of the detection of maxillary and mandibular anatomic structures observed on panoramic radiographs in children using artificial intelligence. Methods A total of 981 mixed images of pediatric patients for 9 different pediatric anatomic landmarks including maxillary sinus, orbita, mandibular canal, mental foramen, foramen mandible, incisura mandible, articular eminence, condylar and coronoid processes were labelled, the training was carried out using 2D convolutional neural networks (CNN) architectures, by giving 500 training epochs and Pytorch-implemented YOLO-v5 models were produced. The success rate of the AI model prediction was tested on a 10% test data set. Results A total of 14,804 labels including maxillary sinus (1922), orbita (1944), mandibular canal (1879), mental foramen (884), foramen mandible (1885), incisura mandible (1922), articular eminence (1645), condylar (1733) and coronoid (990) processes were made. The most successful F1 Scores were obtained from orbita (1), incisura mandible (0.99), maxillary sinus (0.98), and mandibular canal (0.97). The best sensitivity values were obtained from orbita, maxillary sinus, mandibular canal, incisura mandible, and condylar process. The worst sensitivity values were obtained from mental foramen (0.92) and articular eminence (0.92). Conclusions The regular and standardized labelling, the relatively larger areas, and the success of the YOLO-v5 algorithm contributed to obtaining these successful results. Automatic segmentation of these structures will save time for physicians in clinical diagnosis and will increase the visibility of pathologies related to structures and the awareness of physicians.

This research was conducted to determine whether there are differences between the norm values used in the evaluation of children who train in different sports and physical education lessons according to the chronological age and biological age of the children. The research group consists of a total of 239 male students of 13 (n=116) and 14 (n=123) chronological years, who study in grades 7 and 8. In addition, the biological ages of the students who make up the research group are calculated and divided into 13(N=140) and 14 (n=86) biological age groups. Within the scope of the research, heights, body weights, body mass indexes and body fat percentages of students were determined as anthropometric variables. Hand grip, leg strength, flexibility, horizontal and vertical jump, medicine ball throwing, ball throwing and sprint running skills were measured as motoric performance variables. The differences of variables according to chronological age and biological age were calculated by the Independent-Samples t-test. According to age groups, the arithmetic mean (x) is shown as 10%. While establishing the norm, the ratings accepted as Canadian criteria were used. In our study, significant differences were found between body weights in 13 and 14 age groups according to both chronological age and biological age. While there is a similarity in 13 age group in terms of heights, difference in favor of biological age was calculated in 14 age group. Even if there is no difference in the comparisons made for the bio motor abilities that are the subject of our study, percentage differences were found in comparing norm values in 10% slices according to chronological age and biological age. Thanks to norm values generated according to both chronological and biological age, the physical condition of the children, strengths and weaknesses related to the level of students in physical education classes, according to the separation of the application groups of children directed to the appropriate sport or the athletes and the students about training programs that will be applied to the course content it is envisaged that the results of this research will provide guidance. In this way, it is thought that by dividing the chronological age and biological age in children, a possible loss of motivation will be prevented in both physical education and training and competitions.

Repurposing existing drugs and assessing commercially available inhibitors against druggable viral sites might be a potent approach for expediting the drug development in the swiftly evolving COVID-19 pandemic. The toxicity of antiviral drugs can be effectively reduced by using nanostructures in targeted drug delivery. Here, we go over the Hydroxychloroquine (HCQ) expected to interact and adsorb with noble metal-loaded silica nano composites (Ag, Au, and Pt). The spectra of absorption of HCQ and complexes were examined with different solvent solution of protic and aprotic solvents by the Time Dependent Density Functional Theory (TD-DFT) method. Frontier Molecular Orbitals (FMOs) determined the electron transfer inside the molecule. A visual depiction of the molecule's ESP map with colour code representation was used to analyse the distribution of charge inside the molecule. The druglikeness and lipophilicity properties were considered during the drug delivery process to assess whether the molecule was suitable for use as a drug molecule. Finally, the ligand and complexes were carried out for molecular docking investigation with the selected proteins.

The participation of individuals with special needs in performance sports has been increasing from past to present. As in athletes with normal development, stages are planned and implemented in accordance with the goals of high-level performance sports. This study aims to determine the functional threshold power (FTP), VO2max and anaerobic threshold values of a cyclist with special needs to create performance prediction for athletes in similar disability groups. Fırat Uğur, a 26-year-old athlete with special needs who has been participating in cycling competitions for three years and has a body weight of 68 kg and a height of 180.5 cm, participated in the current study. In the findings obtained from the study, the normalized power value of the athlete was found as 204.3 watts, the FTP value as 204.3 watts and the relative power value as 3.00 watts / kg. The athlete's VO2max value was 48.3 ml/kg/min; VO2max load was 298 watts, and VO2max heart rate was 175 beats/min, anaerobic threshold VO2 value was 24.7 ml/kg/min, anaerobic threshold load was 103 watts and anaerobic threshold heart rate was 127 beats/min. When the research findings of the cyclists with special needs were compared with the results obtained from the current study, Fırat Uğur's findings were at lower levels. Considering that the athlete has only participated in competitions as a performance athlete for three years, it can be predicted that he will increase his performance level in the following years. It is recommended that the athlete's training be prepared according to the findings obtained and that the performance development be followed with measurements to be made at regular intervals.

The relationship between the reaction time and the total running time of male (n=407) and female (n=345) athletes competing in the 60-meter wind sprint branch at the world indoor championships between 2006 and 2022 and the competition hour was examined in this study. Statistically significant and negative relationships were found between competition hour and athletes' reaction times, competition hour and athletes' competition result ratings in both genders (rho lt;0.040; p lt;0.05). When the subject is examined in terms of the stages of the competitions, no relationship was found for both genders in the final stage while the same relationships were preserved in the semi-final stage. At the qualification stage, a positive relationship was observed between the competition hour and the reaction time in both men (rho=0.25) and women (rho=0.22). In particular, it will be important that the competition time zones in the qualification stage are in similar circadian zones so as not to create a disadvantage among the athletes, and that the competition programs should be prepared by taking the performance-limiting and performance-supporting time zones into account.

Objectives: The purpose of this study was to evaluate the effectiveness of dental caries segmentation on the panoramic radiographs taken from children in primary dentition, mixed dentition, and permanent dentition with Artificial Intelligence (AI) models developed using the deep learning method. Methods: This study used 6075 panoramic radiographs taken from children aged between 4 and 14 to develop the AI model. The radiographs included in the study were divided into three groups: primary dentition (n: 1857), mixed dentition (n: 1406), and permanent dentition (n: 2812). The U-Net model implemented with PyTorch library was used for the segmentation of caries lesions. A confusion matrix was used to evaluate model performance. Results: In the primary dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found to be 0.8525, 0.9128, and 0.8816, respectively. In the mixed dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found to be 0.7377, 0.9192, and 0.8185, respectively. In the permanent dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found to be 0.8271, 0.9125, and 0.8677, respectively. In the total group including primary, mixed, and permanent dentition, the sensitivity, precision, and F1 scores calculated using the confusion matrix were 0.8269, 0.9123, and 0.8675, respectively. Conclusions: Deep learning-based AI models are promising tools for the detection and diagnosis of caries in panoramic radiographs taken from children with different dentition.

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 Eskişehir, 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.

Purpose: This study aims to examine the diagnostic performance of detecting pulp stones with a deep learning model on bite-wing radiographs. Material and Methods: 2203 radiographs were scanned retrospectively. 1745 pulp stones were marked on 1269 bite-wing radiographs with the CranioCatch labeling program (CranioCatch, Eskişehir, Turkey) in patients over 16 years old after the consensus of two experts of Maxillofacial Radiologists. This dataset was divided into 3 grou as training (n = 1017 (1396 labels), validation (n = 126 (174 labels)) and test (n = 126) (175 labels) sets, respectively. The deep learning model was developed using Mask R-CNN architecture. A confusion matrix was used to evaluate the success of the model. Results: The results of precision, sensitivity, and F1 obtained using the Mask R-CNN architecture in the test dataset were found to be 0.9115, 0.8879, and 0.8995, respectively. Discussion- Conclusion: Deep learning algorithms can detect pulp stones. With this, clinicians can use software systems based on artificial intelligence as a diagnostic support system. Mask R-CNN architecture can be used for pulp stone detection with approximately 90% sensitivity. The larger data sets increase the accuracy of deep learning systems. More studies are needed to increase the success rates of deep learning models.

This research was conducted in order to determine the preferred weight loss methods of wrestlers in the category of juniors, stars and teens who compete in Greco-Roman and freestyle wrestling. 215 male wrestlers between the ages of 14 and 17 participated in the study. In order to determine the weight loss methods of wrestlers, the “athlete weight loss methods and effects scale” developed by Yarar et al. (2016) and the personal information form developed by the researchers were used. In the analysis of the data obtained from the wrestlers SPSS package program was used. Independent t test was used from binary comparisons and ANOVA test was used for multiple comparisons. Significance level was accepted as p

Objectives: This study aimed to develop an artificial intelligence (Al) model that can determine automatic tooth numbering, frenulum attachments, gingival overgrowth areas, and gingival inflammation signs on intraoral photographs and to evaluate the performance of this model. Method and materials: A total of 654 intraoral photographs were used in the study (n = 654). All photographs were reviewed by three periodontists, and all teeth, frenulum attachment, gingival overgrowth areas, and gingival inflammation signs on photographs were labeled using the segmentation method in a web-based labeling software. In addition, tooth numbering was carried out according to the FDI system. An Al model was developed with the help of YOLOv5x architecture with labels of 16,795 teeth, 2,493 frenulum attachments, 1,211 gingival overgrowth areas, and 2,956 gingival inflammation signs. The confusion matrix system and ROC (receiver operator characteristic) analysis were used to statistically evaluate the success of the developed model. Results: The sensitivity, precision, F1 score, and AUC (area under the curve) for tooth numbering were 0.990, 0.784, 0.875, and 0.989; for frenulum attachment these were 0.894, 0.775,0.830, and 0.827; for gingival overgrowth area these were 0.757, 0.675, 0.714, and 0.774; and for gingival inflammation sign 0.737, 0.823, 0.777, and 0.802, respectively. Conclusion: The results of the present study show that Al systems can be successfully used to interpret intraoral photographs. These systems have the potential to accelerate the digital transformation in the clinical and academic functioning of dentistry with the automatic determination of anatomical structures and dental conditions from intraoral photographs. [ABSTRACT FROM AUTHOR]

The aim of this study is to investigate the effect of regular book reading activity on attention level and reaction time in student athletes. The study group consists of 33 student athletes, 17 individuals in control group and 16 individuals in experiment group, doing individual or team sports. Experiment group read books for a period of 40 minutes, three times a week. In addition to personal information form, “Visual Attention Test” is applied to volunteers. The data of the research were evaluated in the SPSS program. Differences were observed in reaction time of students who regularly read books and not (p