en Medical Physics Medical Physics تحقيق بديع Original Research <div style="text-align: justify;">Background: A new coronavirus appeared in late December 2019 in Wuhan, China. He was named Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This virus is responsible for Covid-19, the name given to the disease associated with it. It spreads worldwide, infecting more than a million people and killing more than 70 miles. The rapid and accurate diagnosis of suspected Covid-19 cases plays a crucial role in medical treatment and timely quarantine. Materials and Methods: In order to counter the Covid-19 pandemic, we have developed a method for the automatic detection of Covid-19, from 2D computed tomography (CT) chest images. It is a supervised software system based on the ANN (Artificial Neural Network) algorithm. Pulmonary CT images were collected from multiple international datasets, with a total of 395 images: 70% were used for training and 30% were used for testing. For each patient, the lungs were segmented using simple thresholding. Then, the segmented lungs were fed into a neural network to predict the probability of SARS-CoV-2 infectious. Results: The internal validation achieved a total accuracy of 97.5% with a specificity of 96.6 % and a 100 % sensitivity. Conclusion: These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate Covid-19 diagnosis.</div> Covid-19, chest ct images, SARS-CoV-2, neural network algorithm. 349 356 http://ijrr.com/browse.php?a_code=A-10-2188-4&slc_lang=en&sid=1 H. Romdhane romdhane.hamida@gmail.com 7900319475328460020103 7900319475328460020103 Yes Université de Tunis El Manar, Laboratoire de recherche en Biophysique et Technologies Médicales (LRBTM), ISTMT H. Dziri 7900319475328460020104 7900319475328460020104 No Université de Tunis El Manar, Laboratoire de recherche en Biophysique et Technologies Médicales (LRBTM), ISTMT M. Ali Cherni 7900319475328460020105 7900319475328460020105 No Université de Tunis, LR13 ES03 SIME, ENSIT, Montfleury 1008 Tunisia D. Ben-Sellem 7900319475328460020106 7900319475328460020106 No Université de Tunis El Manar, Laboratoire de recherche en Biophysique et Technologies Médicales (LRBTM), ISTMT