Artificial Intelligence Approaches on X‑ray‑oriented Images Process for Early Detection of COVID‑19

Background: covid-19 is a global public health problem that is crucially important to be diagnosed in the early stages. this study aimed to investigate the use of artificial intelligence (ai) to process x-ray-oriented images to diagnose covid-19 disease. methods: a systematic search was conducted in medline (through pubmed), scopus, isi web of science, cochrane library, and ieee xplore digital library to identify relevant studies published until 21 september 2020. results: we identified 208 papers after duplicate removal and filtered them into 60 citations based on inclusion and exclusion criteria. direct results sufficiently indicated a noticeable increase in the number of published papers in july-2020. the most widely used datasets were, respectively, github repository, hospital-oriented datasets, and kaggle repository. the keras library, tensorflow, and python had been also widely employed in articles. x-ray images were applied more in the selected articles. the most considerable value of accuracy, sensitivity, specificity, and area under the roc curve was reported for resnet18 in reviewed techniques; all the mentioned indicators for this mentioned network were equal to one (100%). conclusion: this review revealed that the application of ai can accelerate the process of diagnosing covid-19, and these methods are effective for the identification of covid-19 cases exploiting chest x-ray images.