An accurate neural network algorithm to diagnose Covid-19 from CT images

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-ofprinciple for using artificial intelligence to extract radiological features for timely and accurate covid-19 diagnosis.