Next generation machine learning technologies for democratizing healthcare through point of care ultrasound

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Point-of-care ultrasound (POCUS) describes the process whereby any healthcare practitioner uses a portable ultrasound device to diagnose conditions at the bedside, whether in an urban hospital, rural clinic, or a remote location. When used appropriately, POCUS can reduce wait times for diagnostic tests, make patient treatment pathways more efficient, streamline transfers to save healthcare costs, and save lives. Although this technology is becoming cheaper and more portable, its use still remains significantly constrained by the high levels of training required by its operators to become proficient in interpretation and analysis. Through $2.5M investment from Canada's Digital Technology Supercluster, we have developed IN-POCUS, Intelligent Network for POCUS, in BC. By deploying 80+ POCUS devices across the province, we are now able to provide ultrasound technology to frontline physicians at these critical pandemic times.This NSERC Alliance project is to tackle two of the key challenges we face today in integrating machine learning technologies within IN-POCUS for routine clinical decision making: 1) We require solutions where we can continuously learn from data; and 2) We need to mitigate risk to patients by alleviating silent failure common to machine learning technologies today. Through partnership with Canada's leading POCUS manufacturer, Clarius Mobile Health, world leading medical imaging solutions provider, Change Healthcare Canada Company, and in coordination with IN-POCUS through Providence Healthcare, Vancouver General Hospital, and Rural Coordination Centre BC, we propose to tackle these challenges. We anticipate that machine learning technologies developed will not only have direct impact on IN-POCUS, but they will have broad impact on the entire discipline of machine learning in healthcare, where speed to deployment while mitigating risk is critical. The Alliance project will train 2 PhD, 2 MSc, 5 undergraduate students, and a Research Associate. These HQP will work directly with project partners to establish requirements, develop prototypes and validate solutions in a rapid cycle with end-users to lead the global impact on patient care.