SBIR Phase I: A noninvasive, low-cost, point-of-care wearable electronic patch for continuous pregnancy monitoring
SBIR Phase I: A noninvasive, low-cost, point-of-care wearable electronic patch for continuous pregnancy monitoring
批准号:
1843361
负责人:
Eric Dy
金额:
$22.49万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-02-01 至 2021-04-30
中文摘要
这个小企业创新研究(SBIR)第一阶段项目的更广泛的影响/商业潜力是通过开发一种无创、低成本、即时护理的可穿戴电子贴片,用于临床和家庭环境中的妊娠监测,改善高危妊娠护理和早产检测。在美国,每年有400万妇女分娩。十分之一以上的妊娠被认为是高危妊娠,即母亲、胎儿或新生儿出现不良健康状况的风险较高。该平台将允许医生远程监控和管理高危妊娠,并在发现分娩或其他潜在并发症时进行必要的干预。通过加强胎儿健康的早期检测和增加获得护理的机会,该设备将允许改善医疗保健服务,从而提高母婴安全,预防孕产妇和新生儿发病率,并降低医疗保健成本。通过将机器学习应用于可能成为最大和最全面的孕产妇和胎儿健康数据集,该提议的平台可以成为研究人员确定早产的潜在原因和生物标志物的宝贵资源。主要最终用户是高危妊娠和早产风险较高的妇女。目标客户是医院、医疗保健提供者和保险公司。这项小企业创新研究(SBIR)第一阶段项目旨在开发一种前所未有的手段来支持孕产妇和胎儿健康的进步:一种最先进的小型化移动监测仪,它可以分散地贴在母亲的腹部,并使用传感器在家庭和临床环境中无创地监测胎儿心率(FHR)和其他生理参数。该设备与智能手机通信,智能手机作为网关将数据发送到基于云的平台,在那里数据被收集、存储和分析,医生可以设置通知阈值。在这个项目中,已被证明可以有效测量子宫活动和胎儿运动的贴片技术将被用于开发一种能够在妊娠25周检测FHR的颠覆性产品。将实现远程部署所需的小型化、功耗和成本水平。然后将在医院的孕妇中进行原型监视器的可用性研究,然后进行等效性研究,以验证原型与当前临床金标准心电图的准确性。该解决方案将提高FHR检测的特异性,改进对监测数据的解释,并汇总数据以训练人工智能模型来预测早产等不良事件。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to improve high-risk pregnancy care and preterm labor detection by developing a noninvasive, low-cost, point-of-care wearable electronic patch for pregnancy monitoring in both clinical and home environments. Each year, 4 million women give birth in the US. More than one in ten of pregnancies is considered high-risk, where the mother, fetus, or newborn has elevated risk of experiencing an adverse health condition. The proposed platform will allow doctors to remotely monitor and manage high-risk pregnancies and intervene, as needed, upon detection of labor or other potential complications. By enhancing early detection of fetal well-being and increasing access to care, the device will allow for improved healthcare delivery, thus increasing mother and infant safety, preventing maternal and neonatal morbidities, and lowering healthcare costs. By applying machine learning to what could become the largest and most comprehensive dataset on maternal and fetal health, the proposed platform could become a valuable resource to researchers to identify underlying causes and biomarkers of preterm birth. Primary end users are women with high-risk pregnancies, and elevated risk of preterm birth. Target customers are hospitals, health care providers and insurance companies. This Small Business Innovation Research (SBIR) Phase I project seeks to develop an unprecedented means to support advances in maternal and fetal health: a state-of-the-art miniaturized mobile monitor that discretely sticks onto the mother's abdomen and uses sensors to noninvasively monitor fetal heart rate (FHR) and other physiological parameters in home and clinical environments. The device communicates to a smartphone, which acts as gateway to send data to a cloud-based platform, where the data is collected, stored and analyzed, with doctors able to set notification thresholds. For this project, patch technology, proven to effectively measure uterine activity and fetal movement will be leveraged to develop a disruptive product capable of detecting FHR as early as 25 weeks gestation. Miniaturization, power consumption and cost levels necessary for deployment in remote settings will be achieved. A usability study of the prototype monitor will then be conducted in expectant women in hospital settings, followed by an equivalency study to validate accuracy of the prototype compared to cardiotocogram, the current clinical gold standard. This solution will increase specificity of FHR testing, and improve interpretation of monitoring data, as well as aggregate data to train AI models to predict adverse events such as preterm birth.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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