COVID-19 is a pandemic having devastating implications on healthcare systems globally. Evidence shows that COVID-19 infected patients with pneumonia may present on chest x-rays with a pattern that is difficult to characterise using only the human eye. Therefore, artificial intelligence (AI) techniques using deep learning, which can consistently identify infected patients from non-infected ones given a radiographic examination of the patient, can be used as a reliable diagnostic tool. Considering chest x-rays are one of the most commonly performed radiological studies (coupled with the near universal availability of testing machines), applying AI techniques on them could prove to be valuable for COVID-19 diagnosis during clinical management. We therefore aim to establish a reliable diagnostic tool based on a deep-learning framework for the screening of patients who present with COVID-19 related abnormalities on chest x-rays. Over the course of 7 months we will build a dataset using open source data which are freely available, as well as with de-identified patient data collected from health institutions in Pakistan. Using this dataset, a deep learning model will be trained, which would be able to accurately screen patients who present with abnormalities relevant to COVID-19 in their radiographic examination. This tool will ultimately aid in expediting the diagnosis and referral of COVID-19 patients, resulting in improved clinical outcomes.

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