RAPID: Screening and Prognosis of COVID-19 by a Novel RF Stethoscope

新冠肺炎疫情给我们的社会带来了灾难，但作为我们第一道防线的公共筛查技术仍然薄弱。仅仅发烧是不够的；呼吸困难、胸痛和嗅觉障碍依赖于自我报告，因此不可靠或不准确。然而，如果不对呼吸道传染病进行有效和快速的筛查，在不严重担心新一波新冠肺炎感染或一种新的冠状病毒造成同等或更大损害的情况下，很难恢复正常的社交活动。新冠肺炎症状性观察的源头是肺炎。组织炎症导致发烧和嗅觉障碍；肺组织中的水分充血导致呼吸困难和胸痛。像许多其他身体功能一样，当组织受损时，人类的呼吸会自我补偿，这使得肺炎的早期阶段在目前的筛查标准下是“无症状的”。本项目旨在开发和测试一种使用射频信号检测肺组织损伤的有效传感器技术。这项技术借鉴了百年听诊器听呼吸声的想法。肺部和呼吸道的受损组织会导致异常声音或不同的组织振动特征，这可以迅速被识别为新冠肺炎感染的另一个迹象。然而，传统的听诊器需要裸露的皮肤接触才能获得高质量的记录，需要安静的环境才能将干扰降至最低。该项目将开发一种新的射频听诊器，它可以测量衣服上的组织振动特征，并可以在非常嘈杂的环境中可靠地工作，如公共交通。这项研究将建立一个健康和确诊的新冠肺炎患者的呼吸特征数据库。基于成熟的无线技术，无线电听诊器可以廉价和广泛地部署。用户可以前往检查站，在几秒内完成筛查，即可获得新冠肺炎的早检。随后可以进行进一步的病理测试和社会隔离，对这一大流行进行必要的管理。这项新的射频听诊器技术提供了一种快速方便的方法来测量肺组织的振动特征，并可应用于其他与心肺功能相关的医疗应用。本项目旨在通过一种新型的射频听诊器为新冠肺炎建立一个症状筛查和持续预测平台，该平台可以作为公共交通工具中快速且经济有效的筛查，也可以作为护理点设施或家中的经常性预测工具。该方法的传感模式是肺组织振动特征，它在肺炎的各个阶段都会发生变化。该方法类似于传统的声学听诊器，但读出的是射频信号而不是声音信号。通过衣服上方的射频传感探头，将少量电磁能量耦合到气管、支气管和肺实质深处，并通过组织粘弹性和含水率测量组织振动特征，区分健康人和潜在的新冠肺炎患者。主要研究任务包括射频传感器的开发，在健康成年人身上进行测试，以及在纽约市威尔康奈尔医学中心对潜在的新冠肺炎患者进行长期持续监测。新冠肺炎感染的特征将通过听诊、生理学和机器学习方法与正常呼吸数据库区分开来。该奖项反映了美国国家科学基金会的法定使命，并已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，被认为值得支持。

The COVID-19 pandemic has brought disasters to our society, but the public screening technology as our first line of defense remains weak. Fever alone is not sufficient; difficulty in breathing, chest pain and anosmia rely on self report, and hence not reliable or accurate. However, without effective and fast screening against respiratory contagious diseases, it is difficult to resume normal social contacts without serious concerns of a new wave of COVID-19 infection or a new coronavirus that causes equal or more damages. The source of symptomatic observation of COVID-19 is pneumonia. The tissue inflammation causes fever and anosmia; the congestion from water in lung tissues causes difficulty in breathing and chest pain. Human respiration, like many other body functions, will self compensate in the presence of damaged tissues, which makes the early stage of pneumonia “asymptomatic” under the present screening criteria. This project seeks to develop and test an effective sensor technology to detect the lung tissue damage using radio-frequency (RF) signals. The technology borrows the ideas of listening to breathing sounds by century-old stethoscopes. Damaged tissues in the lung and airway will cause abnormal sounds, or different tissue vibration characteristics, that can be identified quickly as another indication of COVID-19 infection. However, conventional stethoscopes require bare skin contact for quality recording and quiet ambience to minimize interference. The project will develop a new RF stethoscope, which can measure tissue vibration characteristics over clothing and can operate reliably in very noisy environment such as public transits. The research will build a database of breathing features from healthy and confirmed COVID-19 patients. Based on the mature wireless technologies, the radio stethoscope can be inexpensively and broadly deployed. The user can go to a checkpoint and finish the screening within a few seconds for early screening of COVID-19. Further pathological tests and social isolation can then follow through the necessary management of this pandemic. This new RF stethoscope technology provides a fast and convenient way to measure lung tissue vibration characteristics and can be applied to other healthcare applications related to cardiopulmonary functions.This project aims to establish a symptomatic screening and continuous prognosis platform for COVID-19 by a novel RF stethoscope, which can be deployed as a fast and cost-effective screening in public transit and as a recurrent prognosis tool in point-of-care facilities or at home. The sensing modality in the proposed method is the lung tissue vibration characteristics which will change in all stages of a pneumonia. The method is similar to that of the conventional acoustic stethoscope, but the readout is by the RF signals instead of the sound signals. A small amount of the electromagnetic energy will be coupled deeply into the trachea, bronchus and lung parenchyma by the RF sensing probes over clothing, and the tissue vibration features will be measured to distinguish healthy persons from potential COVID-19 patients by the tissue viscoelasticity and water content. The major research tasks include the RF sensor development, testing on healthy adults, and long-term continuous monitoring of potential COVID-19 patients at the Weill Cornell Medical Center in New York City. Features for COVID-19 infection will be distinguished against the normal breathing database by auscultation physiology and machine learning methods.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.