The Coronavirus disease 2019 (COVID-19) is an infectious disease spreading rapidly and uncontrollably throughout the world. The critical challenge is the rapid detection of Coronavirus infected people. The available techniques being utilized are body-temperature measurement, along with anterior nasal swab analysis. However, taking nasal swabs and lab testing are complex, intrusive, and require many resources. Furthermore, the lack of test kits to meet the exceeding cases is also a major limitation. The current challenge is to develop some technology to non-intrusively detect the suspected Coronavirus patients through Artificial Intelligence (AI) techniques such as deep learning (DL). Another challenge to conduct the research on this area is the difficulty of obtaining the dataset due to a limited number of patients giving their consent to participate in the research study. Looking at the efficacy of AI in healthcare systems, it is a great challenge for the researchers to develop an AI algorithm that can help health professionals and government officials automatically identify and isolate people with Coronavirus symptoms. Hence, this paper proposes a novel method CoVIRNet (COVID Inception-ResNet model), which utilizes the chest X-rays to diagnose the COVID-19 patients automatically. The proposed algorithm has different inception residual blocks that cater to information by using different depths feature maps at different scales, with the various layers. The features are concatenated at each proposed classification block, using the average-pooling layer, and concatenated features are passed to the fully connected layer. The efficient proposed deep-learning blocks used different regularization techniques to minimize the overfitting due to the small COVID-19 dataset. The multiscale features are extracted at different levels of the proposed deep-learning model and then embedded into various machine-learning models to validate the combination of deep-learning and machine-learning models. The proposed CoVIR-Net model achieved 95.7% accuracy, and the CoVIR-Net feature extractor with random-forest classifier produced 97.29% accuracy, which is the highest, as compared to existing state-of-the-art deep-learning methods. The proposed model would be an automatic solution for the assessment and classification of COVID-19. We predict that the proposed method will demonstrate an outstanding performance as compared to the state-of-the-art techniques being used currently.

This study aims to estimate the knowledge and awareness of physicians about the hazards of radiological examinations on their health and on their patients. 466 questionnaires administered through a Google spreadsheet were answered by physicians from the 20 cities of Saudi Arabia. The sample included 167 radiologists, 106 neonatologists, 19 oncologists, 45 surgeons and 18 orthopaedists, 11 paediatricians and 100 physicians on different specialities. Only 133 of the physicians had received a radiation protection course in the workplace. A total of 73% from participants revealed many gaps in knowledge. For example, 51% of the respondents were unable to classify mammography as ionizing radiation and 69.3% did not know the recommended annual dose limit to the whole body of a radiation worker. The overall knowledge score ranged from 0% to 16.5% (mean 5.3%), with a low score among surgeons and orthopaedists. These results clearly indicate the heterogeneous knowledge for the physicians' and needs to be improved by implementation of pre-employment orientation courses or adding a subject in the under or postgraduate curricula.

This study of doses to patients from emitted photoneutrons in a medical linear accelerator (Varian 2100C) was carried out. Dose calculation was performed using Monte Carlo Geant4 code. The model was used to calculate the neutron fluence, as a function of the neutron energy inside the treatment room to estimate the effective dose to patients. The ambient dose equivalent versus field size for patients is reported in this study. The ambient dose equivalent using 1 x 1 cm(2) field size, at isocentre and X-ray modes of 20, 18, 15 and 10 MV, was found to be 1.85, 1.79, 0.61 and 0.06 mSv Gy(-1), respectively. The mean energies of emitted photoneutrons were 0.48, 0.44, 0.40 and 0.16 MeV at X-ray modes of 20, 18, 15 and 10 MV, respectively. The results of ambient dose equivalent from emitted photoneutrons cannot be ignored and can represent a risk for healthy tissues. This study emphasised that Geant4 Monte Carlo code is an appropriate choice for studying photoneutron production and transport.

Aim: To assess the root canal filling quality performed by general dental practitioners in Yemen through radiographic evaluation. Materials and Methods: Four hundred fifty-five digital panoramic radiographs were selected from the archive of the Dental Health Center in Sana'a, Yemen. The final sample consisted of 221 patients, 685 teeth, and 977 root canals. The criteria for overall radiographic adequacy of root canal fillings were defined as the presence of adequate length, density and taper, and absence of iatrogenic errors (ledges, transportations and perforations). Chi-square test was used to determine statistical significance between different parameters. Results: This study considered only radiographic criteria for evaluation of the root canal fillings. The percentage of root canal fillings with adequate length, density and taper was 30.8%, 29.6% and 20.7%, respectively. Considering the incidence of iatrogenic errors, perforations were present in 12 root canals (1.2%), while the presence of transportations was observed in 20 root canals (2.0%). However, ledges were no detected in any root canals. Conclusion: The root canal filling quality performed by general dental practitioners in Yemen is poor.

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