BackgroundA considerable group of internet users consists of university users; however, despite internet benefits and capabilities, internet overuse is a threat to societies especially to young people and students.ObjectiveThe objective of this study was to determine the predictive role of information literacy in internet addiction among students of Iran University of Medical Sciences during 2016.MethodsThis analytical cross-sectional study was conducted in Iran University of Medical Sciences in 2016. Using stratified random sampling method, 365 students from different disciplines were selected. Measuring tools included the Information Literacy Questionnaire, the Yang Online Drug Addiction Scale and the General Health Questionnaire. The collected data were analyzed by Pearson product-moment correlation, independent samples t-test and multiple linear regression using SPSS version 22.ResultsAccording to this study, 31.2% of students had internet addiction (29.9% were mildly addicted and 1.3% had severe addiction). There was a significant and inverse relationship between higher information literacy and internet addiction (R= −0.45) and (p<0.001). The predictor variable “Information literacy” explained 20% of the variation in the outcome variable “Internet addiction”.ConclusionStudents play a substantial role in promoting the cultural and scientific level of knowledge in society; the higher their information literacy, the lower the level of Internet addiction, and consequently the general health of society will improve. It seems that wise planning by authorities of Iran’s universities to prevent internet addiction and to increase information literacy among students is needed.

Objectives: It is unlikely that by fall and winter of 2020, standard vaccine or treatment is available for COVID-19 infection. In this period, differentiation between COVID-19 and Influenza induced pneumonia will be critical for patient management. To develop an automated platform to perform this task, artificial intelligence models were developed by using the transfer learning techniques on chest CT.Methods: Chest CT images from known cases of COVID-19, H1N1 Influenza induced pneumonia (before December 2019), and normal chest CTs were collected. Different pre-trained Convolutional Neural Networks (CNN) models, including VGG 16, VGG 19, ResNet-50, Wide ResNet, InceptionV3, and SqueezNet were fine-tuned on this data set. 60% of the dataset was used for training, 20% for validation, and 20% for test the final models. Accuracy, Precision, Recall and F1 score of each model were calculated.Results: For differentiation of COVID-19 pneumonia versus H1N1 Influenza pneumonia versus normal CTs, the ResNet-50 (accuracy above 92%) outperformed other models followed by InceptionV3 and wide ResNet.Conclusions: The pre-trained image classification AI models are feasible to be fine-tuned and used for differentiation COVID-19 versus H1N1 Influenza pneumonia. In this context, ResNet-50 and then InceptionV3 architectures appear more promising and are suitable start points for further development. We share the source code and trained models in the supplement of this manuscript to be used by other researchers for further development.