Automated Frame-by-Frame Assessment of Lung Ultrasound Imaging in Severe COVID-19 Patients Using Machine Learning

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The Coronavirus disease (COVID-19) has become the most pressing concern for the Canadian healthcare system due to the alarmingly increasing number of new cases and deaths every day. Reports by healthcare agencies indicate that around 7-14% of all confirmed COVID-19 patients have been hospitalized in Canada. COVID-19 lung involvement is an ominous sign as patients can decompensate rapidly and within a matter of hours need urgent admission to an intensive care unit (ICU) for mechanical ventilation to maintain lung function until they recover. Therefore, it is critical to accurately diagnose and monitor the disease in patients for better management of hospitals and ICUs, given the limited available resources such as beds and mechanical ventilators. Lung conditions could be assessed using chest radiographs or computed tomography (CT). Repeated radiographs or CT scans of younger patients also represent a high radiation dose known to lead to cancer. About 12% of all hospital admissions for COVID-19 patients are younger than forty years old. In addition, radiation-free non-invasive scanning is essential for COVID-19 patients who are pregnant. In this project, we proposed to use ultrasound imaging with machine learning algorithm development to detect and monitor pneumonia in severe COVID-19 patients. The proposed project will be undertaken by the partnership between the University of Alberta and MEDO.ai in Edmonton, Canada. Ultrasound imaging is inexpensive, non-invasive, free of ionization radiation and portable. Due to their small size, ultrasound scanners are relatively easy to disinfect, which is critical in the case of the highly infectious COVID-19 virus. The initial machine learning algorithm development will rely on ultrasound scans obtained from 296 patients. We will integrate the learned machine models into a web-based diagnostic system to produce a tool that can be used effectively by a healthcare worker with limited training. The system could also be used in rural and remote areas with limited hospital facilities.