Grant: Alliance Grants

The sustained increase in new cases of covid-19 across the country and potential for subsequent outbreaks call for new tools to assist health professionals with early diagnosis and patient monitoring. Growing evidence around the world is showing that lung ultrasound examination can detect manifestations of covid-19 infection. Ultrasound imaging has several characteristics that make it ideally suited for routine use: Small hand-held systems can be contained inside a protective sheath, making it easier to disinfect than X-ray or computed tomography; lung ultrasound allows triage of patients in long term care homes, tents or other areas outside of the hospital where other imaging modalities are not available; and it can determine lung involvement during the early phases of the disease and monitor affected patients at bedside on a daily basis. However, some challenges still remain with routine use of lung ultrasound. Namely, current examination practices and image interpretation are quite challenging, especially for unspecialised personnel. The objective of this partnership between Marion Surgical and Ontario Tech researchers is to create a user friendly computer-aided method that can assist with lung ultrasound diagnosis and monitoring of covid-19. Marion Surgical has extensive expertise with medical simulators and image processing. They have developed a simulator for pulmonary endarterectomy that uses machine learning to identify the presence of blood clots in the lung autonomously in CT scans. The company seeks to apply this approach to help medical professionals assess the risks associated with covid-19 infections, and better understand how it manifests in lung ultrasound images. This collaboration will leverage Marion Surgical's software development expertise with Ontario Tech's resources and lab expertise to create a first-of-its-kind platform for covid-19 diagnosis that is compatible with a pocket-size ultrasound transducer. This will greatly assist medical professionals fighting covid-19 on the front lines where resources are limited, such as in long-term care homes.