STTR Phase I: User-Friendly Spirometer and Mobile App for Self-Management and Home Monitoring of Asthma Patients

STTR 第一阶段：用户友好的肺活量计和移动应用程序，用于哮喘患者的自我管理和家庭监测

• 批准号: 1622950
• 负责人: Charvi Shetty
• 金额: $ 22.5万
• 依托单位: KNOX Medical Diagnostics Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1622950&HistoricalAwards=false
• 关键词: STTR; Phase; User; Friendly; Spirometer

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project will close the gap between the hospital and home for proper asthma care. The greatest hurdles for proper lung assessment are costly devices ranging from $1000 to $30,000, and mandatory oversight from a skilled lab technician to ensure proper use. These hospital lung function tests allow for early detection of declining lung performance up to several days in advance of visible symptoms like coughing or wheezing, but no option for reliable assessment in the home currently exists. The proposed technology incorporates machine learning into an easy to use mobile-app that replicates a trained lab technician's coaching. In combination with an affordable consumer device that measures lung function, the technology makes proper lung assessment accessible outside the hospital, for regular tracking between office visits supported with physician-guided suggestions for reducing unnecessary and costly emergency visits and hospitalizations. The proposed project offers a preventative care solution for 10 million children with asthma in the US alone. This solution entails an engaging mobile-app game controlled by a handheld device that measures lung capacity, which together provide parents with an action plan to prevent their child?s asthma symptoms before they occur. Guidelines will be developed in consultation with pediatric pulmonologists to gain results as reliable as tests conducted under the guidance of a respiratory therapist in the hospital. A large database of expert labeled lung measurements will be used to train and test the neural network model to detect and decipher feature patterns and correlations. If imprecise information is collected, the app's machine-learned algorithm will determine the cause of failure and suggest a corrective action for the next attempt. This ensures that only properly collected lung measurements trigger recommendations for action. By the end of this proposal period, the applicant will have a machine learning algorithm on a mobile-app that matches the efficacy of in-person coaching by a trained lab technician, as well as evaluate the feasibility of the proposed technology for Phase II considerations.

这个小企业技术转让（STTR）第一阶段项目的更广泛的影响/商业潜力将缩小医院和家庭之间的差距，以获得适当的哮喘护理。正确的肺部评估的最大障碍是昂贵的设备，从1000美元到30，000美元不等，以及熟练的实验室技术人员的强制性监督，以确保正确使用。这些医院肺功能测试允许在出现咳嗽或喘息等可见症状前几天早期检测到肺功能下降，但目前还没有在家中进行可靠评估的选择。这项技术将机器学习整合到一个易于使用的移动应用程序中，该应用程序复制了训练有素的实验室技术人员的指导。结合可测量肺功能的经济实惠的消费者设备，该技术可在医院外进行适当的肺部评估，以便在门诊之间进行定期跟踪，并提供医生指导的建议，以减少不必要和昂贵的急诊和住院治疗。该项目仅在美国就为1000万哮喘儿童提供了预防性护理解决方案。这个解决方案需要一个由手持设备控制的吸引人的移动应用程序游戏，该游戏可以测量肺活量，共同为父母提供一个行动计划，以防止他们的孩子？哮喘的症状在他们出现之前。将与儿科肺科医生协商制定指南，以获得与在医院呼吸治疗师指导下进行的测试一样可靠的结果。专家标记的肺测量的大型数据库将用于训练和测试神经网络模型，以检测和破译特征模式和相关性。如果收集到不精确的信息，应用程序的机器学习算法将确定失败的原因，并为下一次尝试提出纠正措施。这确保了只有正确收集的肺部测量值才能触发行动建议。到本提案期结束时，申请人将在移动应用程序上拥有一个机器学习算法，该算法与训练有素的实验室技术人员亲自指导的效果相匹配，并评估拟议技术在第二阶段的可行性。