Search

Objectives The aim of this artificial intelligence (AI) study was to develop a deep learning algorithm capable of automatically classifying periapical and bitewing radiography images as either periodontally healthy or unhealthy and to assess the algorithm's diagnostic success. Materials and methods The sample of the study consisted of 1120 periapical radiographs (560 periodontally healthy, 560 periodontally unhealthy) and 1498 bitewing radiographs (749 periodontally healthy, 749 periodontally ill). From the main datasets of both radiography types, three sub-datasets were randomly created: a training set (80%), a validation set (10%), and a test set (10%). Using these sub-datasets, a deep learning algorithm was developed with the YOLOv8-cls model (Ultralytics, Los Angeles, California, United States) and trained over 300 epochs. The success of the developed algorithm was evaluated using the confusion matrix method. Results The AI algorithm achieved classification accuracies of 75% or higher for both radiograph types. For bitewing radiographs, the sensitivity, specificity, precision, accuracy, and F1 score values were 0.8243, 0.7162, 0.7439, 0.7703, and 0.7821, respectively. For periapical radiographs, the sensitivity, specificity, precision, accuracy, and F1 score were 0.7500, 0.7500, 0.7500, 0.7500, and 0.7500, respectively. Conclusion The AI models developed in this study demonstrated considerable success in classifying periodontal disease. Future applications may involve employing AI algorithms for assessing periodontal status across various types of radiography images and for automated disease detection., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Yavuz et al.)

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., (© 2024. The Author(s).)

Objectives: This study aimed to develop an artificial intelligence (AI) 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 AI 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 AI 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.

Introduction: Investigations to explore the relationship between smoking and its oral manifestations are important to clinicians. Among these oral manifestations, periodontal diseases and dental caries have still a controversial association. This study aims to analyze the effect of smoking on periodontal disease and caries and their relevance to each other., Methods: Data on demographic and clinical features were retrieved from 7028 patients. Smoking status was categorized as a smoker, non-smoker, former smoker and passive smoker. Each patient received a diagnosis according to the new classification system for periodontal disease, in which periodontal disease is divides into stages (PS). The carries status was diagnosed by evaluating the decayed, missing, and filled teeth (DMFT) index., Results: Of the patients, 66.6% were non-smoker women, whereas 53.7 % of passive smokers were women. Being a worker and having a Bachelor's degree was associated with a higher likelihood of getting diagnosed with periodontal disease and caries in smokers. Smoking significantly influences periodontal disease severity and DMFT values (p<0.001). This becomes more evident in former smokers by showing the highest severe periodontal problems (PS3: 29.7% and PS4: 18.9%), and the highest DMFT mean (16.4 ± 7.4) Accordingly, persons having high DMFT had significantly the most severe periodontal disease, namely PS4 (p<0.05)., Conclusions: Smoking is associated with higher caries prevalence and more severe periodontal disease, and DMFT tend to increase with the severity of periodontitis in the same subjects., Competing Interests: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none was reported., (© 2022 Beklen A. et al.)

Background: Perioperative hypertension is a frequent complication even in preoperatively normotensive or controlled hypertensive patients and there is a lack of data regarding the effect of nondipping pattern on perioperative hypertension., Objective: In this study, we aimed to investigate the possible effect of nondipping blood pressure pattern on the risk of perioperative hypertension., Materials and Methods: 234 hypertensive patients who underwent surgery with general anesthesia were evaluated prospectively. The study enrolled patients with well-controlled preoperative blood pressure. The 24-h ambulatory blood pressure monitoring results were used to classify patients as dippers and nondippers. Perioperative hypertension is defined as a systolic/diastolic arterial blood pressure greater than or equal to 160/90 mmHg or systolic blood pressure elevated by more than 20% from the patient's baseline level for more than 15 min perioperatively., Results: There were 61 (26%) nondipper patients [mean age: 62.1 ± 7.1 years, 25 (40.9%) men] and 173 (74%) dipper patients [mean age: 61.4 ± 8.1 years, 83 (47.9%) men]. In the nondipper group, perioperative hypertension was observed in 31 (51.3%) patients, whereas in the dipper group, 33 (19.3%) patients experienced a hypertensive attack (P < 0.001). The independent predictors of perioperative hypertension were the presence of the nondipping pattern [odds ratio (OR) 3.084; 95% confidence interval (CI) 1.831-5.195; P < 0.001], the presence of diabetes mellitus (OR 2.059; 95% CI 1.215-3.490; P = 0.007), and the number of drugs (OR 2.317; 95% CI 1.102-5.097; P = 0.027)., Conclusion: The frequency of perioperative hypertension was higher in preoperative normotensive and known hypertensive patients who were identified as nondippers. In addition, diabetes mellitus and number of drugs were found to be predictors of perioperative hypertension., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Objective: COVID-19 is a disease with high mortality, and risk factors for worse clinical outcome have not been well-defined yet. The aim of this study is to delineate the prognostic importance of presence of concomitant cardiac injury on admission in patients with COVID-19., Methods: For this multi-center retrospective study, data of consecutive patients who were treated for COVID-19 between 20 March and 20 April 2020 were collected. Clinical characteristics, laboratory findings and outcomes data were obtained from electronic medical records. In-hospital clinical outcome was compared between patients with and without cardiac injury., Results: A total of 607 hospitalized patients with COVID-19 were included in the study; the median age was 62.5 ± 14.3 years, and 334 (55%) were male. Cardiac injury was detected in 150 (24.7%) of patients included in the study. Mortality rate was higher in patients with cardiac injury (42% vs. 8%; P < 0.01). The frequency of patients who required ICU (72% vs. 19%), who developed acute kidney injury (14% vs. 1%) and acute respiratory distress syndrome (71%vs. 18%) were also higher in patients with cardiac injury. In multivariate analysis, age, coronary artery disease (CAD), elevated CRP levels, and presence of cardiac injury [odds ratio (OR) 10.58, 95% confidence interval (CI) 2.42-46.27; P < 0.001) were found to be independent predictors of mortality. In subgroup analysis, including patients free of history of CAD, presence of cardiac injury on admission also predicted mortality (OR 2.52, 95% CI 1.17-5.45; P = 0.018)., Conclusion: Cardiac injury on admission is associated with worse clinical outcome and higher mortality risk in COVID-19 patients including patients free of previous CAD diagnosis., (Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.)

Introduction: Dentists are in a critical position to help patients quit smoking. This study analyses the effectiveness of Turkish dentists in smoking cessation as part of routine patient care., Methods: An in-person cross-sectional survey on previous dental visit experiences was completed by 226 patients recruited from the Department of Periodontology, Eskisehir Osmangazi University, Turkey, from March 2019 to September 2019. The questionnaire included topics on patient's smoking/quit characteristics, experiences on smoking cessation from their dentists, and willingness for the implementation of smoking cessation advice by dentists., Results: In all, 38% of the patients were current smokers, 8% were former smokers, and 68% tried to quit previously. Smokers demonstrated consistently higher scores for plaque index, gingival index, and probing depth, than former/non-smokers (p<0.05). Patients' knowledge of adverse effects was high, and the patients presented a positive attitude toward receiving cessation activities from dentists (86.7%). A total of 89% responded positively to be asked about their smoking behavior. However, the dentists' approach for cessation discussions did not go any further than listing the harmful effects. Only 32% of the patients were informed about side effects of smoking and one-third were encouraged to quit. In general, offering smoking cessation advice was relatively infrequent, and the majority of patients tried to quit smoking by themselves (76%) without using any nicotine replacement product (84%)., Conclusions: Smoking leads to oral health problems. Dentists in Turkey may ask their patients' about their smoking habits but less frequently offer practical help to quit., Competing Interests: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none was reported., (© 2021 Beklen A. et al.)

Objective: Benign paroxysmal positional vertigo (BPPV) is the most common peripheral cause of vertigo. It can be defined as transient vertigo induced by rapid changes in head position associated with a characteristic paroxysmal positional nystagmus. The aim of this study was to search for the possible role of oxidative stress and inflammatory mediators in the pathogenesis of BPPV., Materials and Methods: Total antioxidant status as well as paraoxonase, tumor necrosis factor alpha, interleukin (IL) 6, and IL-1β levels were evaluated in peripheral venous serum samples of 30 BPPV and 30 control patients., Results: Total antioxidant status levels were lower in the BPPV group than in the control group (p=0.008). After Epley's repositioning maneuver in the vertigo group, there was a statistically significant decline in IL-1β levels at the first and third month visits (p=0.014 for first month and p=0.013 for third month)., Conclusion: Our findings suggested that IL-1β and oxidative stress contributed to the pathogenesis of BPPV.