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: Hepatitis B is a blood-borne infectious liver disease caused by the Hepatitis B Virus (HBV) and it is best prevented by immunization. Due to occupational exposure, medical students have an increased risk of contracting HBV. Therefore, it is essential for all medical students to have good knowledge about HBV and to complete their HBV vaccinations. Aims: The aim of this study was to assess and compare HBV knowledge, awareness, and vaccination compliance among pre-clinical medical students in four universities. Settings and Design: A cross-sectional study was conducted in September 2018 at the College of Medicine of four governmental universities: King Saud Bin Abdulaziz University for Health Sciences, King Saud University, Princess Noura university, and Imam Mohammed bin Saud Islamic University, in Riyadh, Saudi Arabia. Methods and Materials: Two-hundred-sixty-three pre-clinical medical students completed a questionnaire with sections about demographics, HBV awareness, knowledge, and vaccination compliance. Statistical analysis used: The data was transferred to Excel and SPSS version 22 was used for statistical analysis. A significance level of P < 0.05 was considered statistically significant. Results: The overall knowledge about HBV and vaccination compliance were poor. KSU students had the highest vaccination compliance ( n = 52, 54.2%) and KSAU-HS the lowest ( n = 19, 23,8%). The most-cited reasons for noncompliance were “forgetting about the vaccine” and “busy schedule“. Conclusion: Overall, most of the participants had poor HBV knowledge and vaccine compliance. Therefore, we recommend the implementation of pre-clinical vaccine checking and the addition of an infectious disease awareness and prevention program.
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