Home mobile radiography service in the COVID-19 era

Infection control and radiation safety practices in the radiology department during the COVID-19 outbreak

RATIONALE AND OBJECTIVES

Radiology personnel must have good knowledge, experience and adherence to radiation protection and infection control practices to ensure patient safety and prevent the further spread of the COVID-19 virus. This study analysed compliance and adherence to radiation protection and infection control during COVID-19 mobile radiography.

METHODS

A cross-sectional using online survey was conducted from September to December 2021. Data on demographic characteristics, adherence to radiation protection and infection control practice were collected during mobile radiography for COVID-19 patients in the study. A random sample of the radiographers working in COVID-19 centres in the United Arab Emirates.

RESULTS

Responses were received from 140 participants, with a response rate of 87.5%. Females were the predominant participants (n = 81; 58%). Participants aged ages between 18-25 years (n = 46; 33%) and 26-35 years (n = 42; 30%), (n = 57; 41%) had less than five years of experience, followed by participants who had more than 15 years (n = 38; 27%). Most participants (n = 81; 57.9%) stated that they performed approximately 1-5 suspected or confirmed COVID-19 cases daily. The participants had moderate to high adherence to radiation protection, with a mean and standard deviation of 42.3 ± 6.28. Additionally, infection control adherence was high, with 82% of the participants showing high adherence.

CONCLUSION

Continuous guidance, training and follow-up are recommended to increase adherence and compliance to radiation protection and infection control compliance. Educational institutions and professional organisations must collaborate to provide structured training programmes for radiology practitioners to overcome the practice and knowledge gap.

Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment

OBJECTIVE

To report an overview and update on Artificial Intelligence (AI) and COVID-19 using chest Computed Tomography (CT) scan and chest X-ray images (CXR). Machine Learning and Deep Learning Approaches for Diagnosis and Treatment were identified.

METHODS

Several electronic datasets were analyzed. The search covered the years from January 2019 to June 2021. The inclusion criteria were studied evaluating the use of AI methods in COVID-19 disease reporting performance results in terms of accuracy or precision or area under Receiver Operating Characteristic (ROC) curve (AUC).

RESULTS

Twenty-two studies met the inclusion criteria: 13 papers were based on AI in CXR and 10 based on AI in CT. The summarized mean value of the accuracy and precision of CXR in COVID-19 disease were 93.7% ± 10.0% of standard deviation (range 68.4-99.9%) and 95.7% ± 7.1% of standard deviation (range 83.0-100.0%), respectively. The summarized mean value of the accuracy and specificity of CT in COVID-19 disease were 89.1% ± 7.3% of standard deviation (range 78.0-99.9%) and 94.5 ± 6.4% of standard deviation (range 86.0-100.0%), respectively. No statistically significant difference in summarized accuracy mean value between CXR and CT was observed using the Chi square test (p value > 0.05).

CONCLUSIONS

Summarized accuracy of the selected papers is high but there was an important variability; however, less in CT studies compared to CXR studies. Nonetheless, AI approaches could be used in the identification of disease clusters, monitoring of cases, prediction of the future outbreaks, mortality risk, COVID-19 diagnosis, and disease management.