https://www.selleckchem.com/products/AZD7762.html 001) and GPPCS (p < 0.0001) and lower ESS (p = 0.004) scores. Those who had knowledge on the facility safety (56.6 %) and risk management (59.2 %) plans had higher scores on KCS, PSCS, GPPCS and FCS, while had lower scores on ESS (p < 0.05 for each). Our findings revealed association of female gender, co-morbid psychiatric disease, lack of training unawareness of safety and risk management plans, lack of experience in COVID-19 imaging and high workload with higher risk of poor emotional state and/or intense fear of the disease among radiology technicians during pandemic. Our findings revealed association of female gender, co-morbid psychiatric disease, lack of training unawareness of safety and risk management plans, lack of experience in COVID-19 imaging and high workload with higher risk of poor emotional state and/or intense fear of the disease among radiology technicians during pandemic. Follow-up of aortic aneurysms by computed tomography (CT) is crucial to balance the risks of treatment and rupture. Artificial intelligence (AI)-assisted radiology reporting promises time savings and reduced inter-reader variabilities. The influence of AI assistance on the efficiency and accuracy of aortic aneurysm reporting according to the AHA / ESC guidelines was quantified based on 324 AI measurements and 1944 radiological measurements 18 aortic aneurysm patients, each with two CT scans (arterial contrast phase, electrocardiogram-gated) with an interval of at least six months have been included. One board-certified radiologist and two residents (8/4/2 years of experience in vascular imaging) independently assessed aortic diameters at nine landmark positions. Aneurysm extensions were compared with original CT reports. After three weeks washout period, CTs were re-assessed, based on graphically illustrated AI measurements. Time-consuming guideline-compliant aortic measurements revealed additional affeiabilities that can hamper