In a world of pandemics such as SARS and COVID-19, our health care environments have shown their vulnerability. Unfortunately, we have seen outbreaks in long term care facilities in Quebec, Alberta, BC and Ontario, with dozens of seniors at each site contracting and passing from COVID. In addition to life lost, health care workers themselves are put at risk which leads to a shortage of workers during urgent times. Additional costs are realized as patients require more investigations, longer stays in hospitals and additional treatments. Utilizing a MaskRCNN model to separate objects in video, we propose utilizing real-time image detection of humans (visitors and health care workers) using protective wear (I.e. masks, gloves, and gowns) in health care settings to safeguard workers and patients. Our model is also being trained to determine if PPE is donned and doffed accurately, as literature has shown that roughly 40% of Canadian healthcare workers do this incorrectly, further increasing transmission. The results of this project will provide MedDuck with a novel form of image detectionability that will have immediate real-world application.

Industry Partner(s):MedDuck

Academic Institution:Queen's University

Academic Researcher: Dacin, Tina

Focus Areas: COVID-19

Platforms: Cloud, GPU