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

这一小型企业技术转让(STTR)第一阶段项目的更广泛影响/商业潜力将缩小医院和家庭之间的差距，以提供适当的哮喘护理。进行正确的肺部评估的最大障碍是昂贵的设备，从1000美元到3万美元不等，以及由熟练的实验室技术人员强制监督以确保正确使用。这些医院的肺功能测试可以在咳嗽或喘息等明显症状出现之前几天就及早发现肺功能下降，但目前还不存在在家中进行可靠评估的选择。这项拟议的技术将机器学习融入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。这项技术与一种价格合理的测量肺功能的消费设备相结合，使人们可以在医院外进行适当的肺部评估，以便在医生指导的建议下定期跟踪办公室就诊之间的情况，以减少不必要和昂贵的紧急就诊和住院治疗。拟议的项目仅在美国就为1000万患有哮喘的儿童提供了预防性护理解决方案。这一解决方案需要一个由手持设备控制的引人入胜的手机应用程序游戏，该游戏可以测量肺活量，共同为父母提供一个行动计划，在孩子出现哮喘症状之前预防它们-S。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。将使用专家标记的肺部测量的大型数据库来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。如果收集到不准确的信息，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。这确保只有正确收集的肺部测量才会触发行动建议。到本建议书期末，申请者将在移动应用程序上拥有与训练有素的实验室技术人员进行面对面指导的效果相匹配的机器学习算法，并评估拟议技术的可行性，以满足第二阶段的考虑。

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.