Detection of COVID‑19 using AI – Deep learning and multiple medical imaging modalities

Akhloufi, Moulay | $50,000

New Brunswick Université de Moncton 2020 NSERC Alliance COVID-19 Grant

SARS-CoV2 is a new coronavirus identified as the cause of the 2019 coronavirus disease (COVID-19) which started in Wuhan, China in late 2019 and has spread around the world. This pandemic is impacting the daily lives of everyone around the world. Currently, the most common screening and diagnostic test for COVID‑19 is a laboratory test called reverse transcription polymerase chain reaction (RT-PCR). This test uses samples collected from the patient (with a nasopharyngeal and or throat swab). The specificity of this test is considered high. However, its sensitivity can be as low as 60-70%, leading to a significant number of false negatives and increasing the risk of community transmission. Other concerns are the test result long turnaround time and the worldwide shortage of reagents and swabs, which limits the number of tests that can be conducted. Experts say that for social distancing measures to be safely lifted, we will need to run a large number of tests.

Medical imaging modalities have also been used to detect signs of COVID-19. More recently, an international panel of experts evaluated the utility of imaging technologies in the management of COVID-19, especially chest radiography (CXR) and computed tomography (CT). Another interesting modality that shows a lot of promise for fast triage of new infections is chest ultrasound (CUS). Recent research shows that all these imaging modalities can be valuable in the fight against this pandemic and can be used along with RT-PCR laboratory tests. The main objective of this project is to develop an AI-based tool capable of detecting COVID‑19 using medical imaging modalities of the chest (radiography (CXR), ultrasonography (CUS) and computerized tomography (ChCT)). In addition, we will develop an explainability algorithm to give a visual feedback showing the location of the signs of the disease. This visual feedback will be very useful in helping the confirmation and the stage of the disease by a health practitioner.

With funding from the Government of Canada

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