Background Advanced developments in diagnostic radiology have provided a rapid increase in the number of radiological investigations worldwide. Recently, Artificial Intelligence (AI) has been applied in diagnostic radiology. The purpose of developing such applications is to clinically validate and make them feasible for the current practice of diagnostic radiology, in which there is less time for diagnosis. Purpose To assess radiologists’ knowledge about AI’s role and establish a baseline to help in providing educational activities on AI in diagnostic radiology in Saudi Arabia. Material and Methods An online questionnaire was designed using QuestionPro software. The study was conducted in large hospitals located in different regions in Saudi Arabia. A total of 93 participants completed the questionnaire, of which 32 (34%) were trainee radiologists from year 1 to year 4 (R1–R4) of the residency programme, 33 (36%) were radiologists and fellows, and 28 (30%) were consultants. Results The responses to the question related to the use of AI on a daily basis illustrated that 76 (82%) of the participants were not using any AI software at all during daily interpretation of diagnostic images. Only 17 (18%) reported that they used AI software for diagnostic radiology. Conclusion There is a significant lack of knowledge about AI in our residency programme and radiology departments at hospitals. Due to the rapid development of AI and its application in diagnostic radiology, there is an urgent need to enhance awareness about its role in different diagnostic fields.

Radiological examinations have played a crucial role in the identification and management of COVID-19 patients. Therefore, knowledge and awareness of infection control among healthcare workers in radiology departments are important to prevent disease transmission. This study aimed to assess the knowledge and practice of infection control for COVID-19 among healthcare workers in radiology departments in Saudi Arabia. A cross-sectional, online questionnaire was administered among healthcare workers in radiology departments in Saudi Arabia in May 2020. The questionnaire consisted of demographic characteristics, profession, knowledge of infection control for COVID-19, and good practice of infection control measures for COVID-19 in radiology departments. A descriptive statistical analysis and chi-square test were performed using SPSS software. A total of 234 (91%) of healthcare workers replied that they have good knowledge about the precautions needed during the examination of positive COVID-19 cases in radiology departments, and 216 (84%) replied that they knew the necessary precautions when using portable X-ray machine. Moreover, 191 (>74%) of those surveyed agreed that wearing personal protective equipment and following the CDC sequence. There was significant association between profession and good clinical practices in radiology departments regarding COVID-19. Such knowledge could limit the spread of COVID-19 among the healthcare workers in radiology departments.

Aim:The aim of this study is to assess the use of ArcCHECK (AC) as an alternative method to replace film dosimetry for pre-treatment quality assurance (QA) of three-dimensional conformal radiation therapy, intensity-modulated radiation therapy (IMRT), and volumetric-modulated arc therapy (VMAT) stereotactic ablative radiotherapy (SABR) treatment plans.Materials and Methods:Twenty-five patients with a varied diagnosis of lung, spine, sacrum, sternum, ribs, scapula, and femur undergoing SABR were selected for this study. Pre-treatment QA was performed for all the patients using ionization chamber and film dosimetry. Measurements were also carried out on an AC phantom. The planned and measured doses from the AC device and EBT3 films were compared using four different gamma criteria: 2%/2 mm, 3%/2 mm, 3%/1 mm, and 3%/3 mm.Results:The mean gamma passing rates at 3%/3 mm for all non-spine SABR cases were 98.79 ± 0.96 and 99.27 ± 1.03 with AC and films, respectively. The mean passing rates at 3%/2 mm for AC and films were 98.76 ± 0.42 and 99.43 ± 0.27 respectively for spine VMAT SABR, and 87.15 ± 2.45 and 99.79 ± 0.14 respectively for spine IMRT SABR. In the case of spine tumors, the gamma criterion was tightened due to the proximity of spinal cord to the planning target volume. Our results show that AC provides good results for all VMAT SABR plans.Conclusion:The AC results at 3%/3 mm were in good agreement with film dosimetry for most cases. We observed a significant reduction in QA time on using AC for SABR QA. This study showed that AC results are comparable to film dosimetry for all studied sites except for spine IMRT SABR.

Summary. Aim: The accuracy of the dose calculation is vital in the stereotactic ablative body radiotherapy (SABR) technique to achieve clinically effective dose distribution for better tumor control. Multiple commercial radiotherapy treatment planning systems (TPS) were implemented with different algorithms, such as Acuros XB in Eclipse and Superposition in XiO. The aim of this study is to investigate five different dose calculation algorithms, namely, pencil beam convolution (PBC), Acuros XB, AAA implemented in an Eclipse system, collapsed cone convolution (CCC) algorithm implemented in Mobius3D and superposition algorithms implemented in the XiO system, and then validate the results against measurements using an Institute of Physical Sciences in Medicine (IPSM) phantom with different density materials for in-field and out-of- field conditions. Material and Methods: The IPSM phantom was used to investigate the dose calculation algorithm performances in four different densities (water, lung, ribs, and dense bone) using different beam configurations, including small beam fields utilised in lung SABR. Five commercial algorithms implemented in two TPS (Eclipse and XiO) and one plan check (M3D) system were used for in-field and out-of-field measurement. Results: In the in-field condition, the Acuros XB algorithm had lower mean differences than the measured dose by the IC ranging from –0.46 to 0.24 for all the densities. In the out-of-field condition, the results of eclipse system: AAA, PBC and Acuros XB algorithms demonstrated underdose point’s measurements by –40% for all densities except for AAA calculations in lung density (overdosed by 40%). The measured points of the superposition algorithms were overestimated to the actual dose less than 30% in water, lung and dense bone. At the same densities, the CCC algorithms showed relatively the lowest differences in percentage compared to the superposition algorithms. Conclusion: Our results showed that the Acuros XB and superposition algorithms are closer to the actual measured dose than AAA, PBC and CCC for majority of the field conditions for water-equivalent, lung, rib and dense bone densities. The CCC algorithm resulted in a better agreement with the measurement of the out-of-field points compared with the other algorithms.