The aim of this article is to shed light on coronavirus disease 2019 (COVID-19) and its oral effects and risk of nosocomial transmission to update the knowledge of dental health care workers. A thorough literature search of the PubMed/Embase/Web of Science/Cochrane central database was conducted to identify the impact of COVID-19 on oral health. We reviewed the recommendations on the recent guidelines set by the Centers for Disease Control and Prevention infection control practices for dentistry, American Dental Association, and the World Health Organization. According to the available evidence, COVID-19 may have a negative impact on the oral health due to the infection itself and due to various other consequences such as therapeutic measures, xerostomia, and other complications of the COVID-19. In light of the above facts, dentists should be wary of the disease, its identification, mode of spread and impacts on the oral health. The dental personnel have been identified as at the highest risk of getting COVID-19 due to cross infection from contact with their patients and aerosols generated in routine dental procedures. As such, they should be aware of the modifications that need to be made to the practice to prevent transmission of the disease. It is evident that COVID-19 has a negative impact on the oral health and at the same time a significant transmission risk to the dental personnel and patients who visit the clinic. If the recommendations issued by the regulatory authorities are meticulously followed, the risk of disease transmission can be lessened.

Background The purpose of this investigation was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the accuracy and usefulness of this system for the detection of alveolar bone loss in periapical radiographs in the anterior region of the dental arches. We also aimed to evaluate the usefulness of the system in categorizing the severity of bone loss due to periodontal disease. Method A data set of 1724 intraoral periapical images of upper and lower anterior teeth in 1610 adult patients were retrieved from the ROMEXIS software management system at King Saud bin Abdulaziz University for Health Sciences. Using a combination of pre-trained deep CNN architecture and a self-trained network, the radiographic images were used to determine the optimal CNN algorithm. The diagnostic and predictive accuracy, precision, confusion matrix, recall, F1-score, Matthews Correlation Coefficient (MCC), Cohen Kappa, were calculated using the deep CNN algorithm in Python. Results The periapical radiograph dataset was divided randomly into 70% training, 20% validation, and 10% testing datasets. With the deep learning algorithm, the diagnostic accuracy for classifying normal versus disease was 73.0%, and 59% for the classification of the levels of severity of the bone loss. The Model showed a significant difference in the confusion matrix, accuracy, precision, recall, f1-score, MCC and Matthews Correlation Coefficient (MCC), Cohen Kappa, and receiver operating characteristic (ROC), between both the binary and multi-classification models. Conclusion This study revealed that the deep CNN algorithm (VGG-16) was useful to detect alveolar bone loss in periapical radiographs, and has a satisfactory ability to detect the severity of bone loss in teeth. The results suggest that machines can perform better based on the level classification and the captured characteristics of the image diagnosis. With additional optimization of the periodontal dataset, it is expected that a computer-aided detection system can become an effective and efficient procedure for aiding in the detection and staging of periodontal disease.

Background: The clinical attachment level (CAL) and radiographically assessed bone levels are used to assess the loss of periodontal tissue support in periodontitis, a chronic, multifactorial inflammatory disease of the periodontium. However, few studies have been done to study the relationship between these two parameters. According to our knowledge, this is the first study investigating the relationship between the two measurements using intraclass correlation analysis. Aim: The aim of the study is to investigate the relationship between CAL and radiographically assessed bone level in teeth affected with periodontitis. Methods: A retrospective cross-sectional study was conducted by selecting a sample of 880 periodontal sites in 104 periodontitis patients, aged 25-60 years. CAL and peri-apical radiographs of the selected sites were obtained from the computerized patient records. The distance from the cemento-enamel junction (CEJ) to the base of the alveolar bone level (ABL) was measured. The data was analyzed using SPSS. Results: Intraclass correlation analysis (ICC) revealed a moderate degree of reliability between CAL and CEJ to ABL measurements. The average ICC was 0.68 with a 95% confidence interval of 0.53-0.77 (p < .001) indicating moderate to good reliability. Comparing the types of teeth, the central incisors, particularly the lower central incisors showed the highest ICC values (ICC: 0.822, CI: 0.77-0.86) indicating good reliability while the premolar and molars showed poor to moderate agreement (Maxillary