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

医疗点超声（POCUS）描述了任何医疗从业者使用便携式超声设备在床边诊断病情的过程，无论是在城市医院，农村诊所还是偏远地区。如果使用得当，POCUS可以减少诊断测试的等待时间，使患者的治疗途径更有效，简化转移以节省医疗成本，并挽救生命。尽管这项技术变得越来越便宜和便携，但它的使用仍然受到操作员需要进行高水平培训才能熟练掌握解释和分析的限制。通过加拿大数字技术超级集群的250万美元投资，我们在BC省开发了in -POCUS，即POCUS智能网络。通过在全省部署80多台POCUS设备，我们现在能够在这些关键的大流行时期为一线医生提供超声技术。这个NSERC联盟项目是为了解决我们今天在将机器学习技术整合到in - pocus中进行常规临床决策时面临的两个关键挑战：1)我们需要能够持续从数据中学习的解决方案；2)我们需要通过减轻当今机器学习技术常见的无声故障来降低患者的风险。通过与加拿大领先的POCUS制造商Clarius Mobile Health、世界领先的医疗成像解决方案提供商Change Healthcare Canada Company的合作，以及通过Providence Healthcare、温哥华总医院和BC省农村协调中心与in -POCUS的合作，我们建议应对这些挑战。我们预计，机器学习技术的发展不仅会对in - pocus产生直接影响，还会对医疗保健领域的整个机器学习学科产生广泛影响，在医疗保健领域，部署速度和降低风险至关重要。该联盟项目将培养2名博士、2名硕士、5名本科生和1名研究助理。这些HQP将直接与项目合作伙伴合作，在与最终用户的快速循环中确定需求、开发原型并验证解决方案，从而对患者护理产生全球影响。

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.