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Introduction: This study aimed to construct an artificial intelligence system to detect Coronavirus disease-2019 (COVID-19) pneumonia on computed tomography (CT) images and to test its diagnostic performance. Methods: Data were acquired between March 18-April 17, 2020. CT data of 269 reverse tran-scriptase-polymerase chain reaction proven patients were extracted, and 173 studies (122 for training, 51 testing) were finally used. Most typical lesions of COVID-19 pneumonia were la-beled by two radiologists using a custom tool to generate multiplanar ground-truth masks. Us-ing a patch size of 128x128 pixels, 18,255 axial, 71,458 coronal, and 72,721 sagittal patches were generated to train the datasets with the U-Net network. Lesions were extracted in the or-thogonal planes and filtered by lung segmentation. Sagittal and coronal predicted masks were reconverted to the axial plane and were merged into the intersect-ed axial mask using a voting scheme. Results: Based on the axial predicted masks, the sensitivity and specificity of the model were found as 91.4% and 99.9%, respectively. The total number of positive predictions has increased by 3.9% by the use of intersected predicted masks, whereas the total number of negative predic-tions has only slightly decreased by 0.01%. These changes have resulted in 91.5% sensitivity, 99.9% specificity, and 99.9% accuracy. Conclusion: This study has shown the reliability of the U-Net architecture in diagnosing typical pulmonary lesions of COVID-19 in CT images. It also showed a slightly favorable effect of the intersection method to increase the model's performance. Based on the performance level pre-sented, the model may be used in the rapid and accurate detection and characterization of the typical COVID-19 pneumonia to assist radiologists.
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Objective: The aim of this study was to investigate anxiety and post-traumatic stress symptoms (PTSS) and their possible associated factors among youths, comparing to their older adult counterparts. Patients and Methods: This cross-sectional online study assessed 1493 participants in Turkey. Beck Anxiety Inventory (BAI) and the Post-traumatic Stress Disorder (PTSD) Checklist for DSM-5 (PCL-5) were used. Results: The Youths (15-24 years) reported to experience more anxiety and PTSS than Adults (25-59 years). There were weak correlations between BAI, PCL-5 scores and duration of recovery and isolation in Adults infected with corona virus disease 19 (COVID-19), but not in Youths. Younger age and lower economic status were common factors for severe anxiety and PTSS, additionally history of mental health needs and loss of an acquaintance due to COVID-19 were specific predictors for anxiety, and having a medical condition was predictor for PTSS in Youths. In Adults, female, lower economic status, having a medical condition, history of mental health needs, and loss of an acquaintance due to COVID-19 were common factors for worsening both PTSS and anxiety, additionally younger age for PISS and being infected by COVID-19 for anxiety were specific predictors. Conclusion: Pandemic might have a greater impact on mental well-being of youths than adults. Identification of risk factors can shed light on planning, prevention and intervention strategies.