SBIR Phase I: Voice-based telehealth interface for symptom monitoring and screening for chronic and acute respiratory diseases, including COVID-19

小企业创新研究第一阶段项目的更广泛影响/商业潜力是一种基于智能手机的新方法，用于慢性和急性呼吸道疾病(包括新冠肺炎)的症状监测和筛查。目前评估呼吸系统疾病的方法不容易获得，或者不能规模化地筛查大量人口。这项拟议的技术将使任何能够连接到互联网的麦克风(例如智能手机)的人都能够使用语音作为指示器来检测和监测呼吸道疾病。这项技术将在几分钟内管理简单的测试，并在几秒钟内提供结果，而不需要专门的用户培训。预期成果是一个广泛、实时的筛查、监测和恶化预警系统，该系统可以远程分析包括新冠病毒-19在内的慢性和急性呼吸道疾病患者的语音信号。这个小企业创新研究第一阶段项目旨在开发基于语音的分类器，用于诊断新冠肺炎并监测疾病的严重程度。现有的在呼吸和语音中检测指示肺功能和呼吸系统疾病的声音生物标志物的算法将被扩展，以纳入新冠肺炎签名。将收集新冠肺炎检测呈阳性的患者的音频记录，以提取微签名，并开发算法来自动识别模式，并将其与临床结果和报告的症状映射。研究目标包括开发：(1)区分有症状和无症状患者的二进制分类器；(2)当提供一系列语音样本时，关联(在未来预测)患者症状严重程度变化的多类别分类器；(3)为医生开发仪表板，提供横断面和纵向分析的最新报告和可视化；(4)提供可集成到第三方IT基础设施中的肺功能指标和分类器的API。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is a novel smartphone-based method for symptom monitoring and screening for chronic and acute respiratory diseases, including COVID-19. Current methods of evaluating respiratory diseases are not easily accessible or do not scale to screen large populations. The proposed technology will enable detection and monitoring of respiratory diseases to anyone with access to an internet-connected microphone (e.g., smartphone), using voice as an indicator. The technology will administer simple tests in minutes and deliver results in seconds, without requiring specialized user training. The anticipated outcome is a widespread, real-time screening, monitoring and exacerbation warning system that remotely analyzes voice signals for patients with chronic and acute respiratory diseases, including COVID-19.This Small Business Innovation Research (SBIR) Phase I project seeks to develop voice-based classifiers that diagnose COVID-19 and monitor the severity of the disease. Existing algorithms that detect vocal biomarkers in breath and speech indicative of lung function and respiratory disease will be extended to incorporate COVID-19 signatures. Audio recordings from patients receiving a positive COVID-19 test will be collected to extract micro -signatures and develop algorithms to automatically recognize and map patterns to clinical findings and reported symptoms. The research objectives include developing: (1) A binary classifier that differentiates symptomatic and asymptomatic patients; (2) A multi-class classifier that correlates (in future predicts) changes in the severity of a patient’s symptoms when provided a series of voice samples (3) Developing a dashboard for physicians that provides up to date reports and visualizations of the cross sectional and longitudinal analytics (4) An API giving lung function metrics and classifiers available for integration into 3rd party IT infrastructure.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.