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

下一代机器学习技术通过护理点超声实现医疗保健民主化

• 批准号: 556820-2020
• 负责人: Abolmaesumi, Purang
• 金额: $ 7.21万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=721633
• 关键词: Next; generation; machine; learning; technologies

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.

临床点超声(PPoUS)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、农村诊所还是在偏远地区。如果使用得当，Pocus可以减少诊断测试的等待时间，使患者的治疗路径更加高效，简化传输以节省医疗成本，并拯救生命。尽管这项技术正在变得更便宜和更便携，但它的使用仍然受到操作员熟练掌握解释和分析所需的高水平培训的严重限制。通过加拿大数字技术超级集群的250万美元投资，我们在公元前开发了IN-Pocus，即Pocus的智能网络。通过在全省部署80多台Pocus设备，我们现在能够在大流行的关键时刻为一线医生提供超声技术。NSERC联盟项目旨在解决我们今天在IN-Pocus中集成机器学习技术以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)我们需要通过减少当今机器学习技术常见的静默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合作，并通过普罗维登斯医疗保健、温哥华综合医院和BC农村协调中心与IN-Pocus合作，我们建议应对这些挑战。我们预计，开发的机器学习技术不仅将对IN-Pocus产生直接影响，还将对医疗保健领域的整个机器学习学科产生广泛影响，在这一领域，部署速度和降低风险至关重要。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，与最终用户一起快速周期地建立需求、开发原型和验证解决方案，以引领对患者护理的全球影响。