ASCENT: Multimodal chest e-tattoo with customized IC and deep learning algorithm for tracking and predicting progressive pneumonia

ASCENT：多模式胸部电子纹身，具有定制 IC 和深度学习算法，用于跟踪和预测进行性肺炎

• 批准号: 2133106
• 负责人: Nanshu Lu
• 金额: $ 150万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2133106&HistoricalAwards=false
• 关键词: ASCENT; Multimodal; chest; tattoo; customized

Coronavirus infections may cause life-threatening pneumonia with a mortality rate more than 10% in certain populations, which could quickly overwhelm any medical care system. Continuous monitoring of the infected and suspected at the hospital or under self-quarantine can help optimize triage and treatment. However, so far there is no available mobile device and algorithm platform that can perform reliable, comprehensive, continuous and long-term monitoring and assessment for pneumonia patients in either clinical or free-living environments. The goal of this ASCENT research is to develop, integrate, and test foundational technologies required for a scalable monitoring and triage system for patients who have contracted pneumonia. The objective is to integrate a wireless, noninvasive, week-long wearable, and multimodal physiological sensor platform (e-tattoos) with a dedicated integrated circuit (IC), connect it to an FDA (U.S. Food and Drug Administration) cleared virtual patient monitoring platform (Sickbay) which also hosts a customized deep learning algorithm, for the continuous monitoring and assessment of the severity of progressive pneumonia. The result will be a gamechanging hardware and software system that provides continuous monitoring and intelligent assessment for highly-infectious and critically-ill patients but also protects healthcare providers from infection and contamination.There is a longstanding systems challenge that the world lacks long-term, high-fidelity, continuous and scalable clinical surveillance platforms for infectious disease patients to battle with global pandemic like COVID-19. The progression of pneumonia is associated with the changes in vital signs such as core body temperature, respiratory rates, heart rates, blood oxygen saturation and so on. Since clinical deterioration of patients at risk of developing pneumonia can be short and unpredictable, continuous multimodal monitoring and accurate assessment is necessary for this population, whether in the hospitals or at home. The five investigators bring together well-established expertise in multimodal wearable sensors (Lu), mixed signal IC design (Li), time-series data analytics (Miao), clinical systems integration and scalable patient monitoring (Rusin), as well as critical care medicine (Jain). This multidisciplinary engineering and clinical team attempt to address this system-level challenge through: 1) development of wireless wearable sensors called e-tattoo with dedicated IC capable of noninvasive and week-long multimodal patient monitoring; 2) data analysis and deep learning algorithm development and integration with e-tattoo through an FDA (U.S. Food and Drug Administration) cleared virtual patient monitoring platform, Sickbay; 3) e-tattoo and algorithm validation on 20 patients with progressive pneumonia at Texas Children’s Hospital. The broader impacts for the society are dramatically improving how critically ill patients are monitored as well as training next generation engineers to carry out convergent research. The ultimate vision is to establish a scalable means of safely surveilling patients and orchestrating high-quality care across the country.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.

冠状病毒感染可能导致某些人群的死亡率超过10％，导致肺炎威胁生命，这可能会迅速淹没任何医疗系统。连续监测在医院或自我固定下的感染和怀疑可以帮助优化分类和治疗。但是，到目前为止，还没有可用的移动设备和算法平台可以对临床或自由生活环境中的肺炎患者进行可靠，全面，连续和长期的监测和评估。这项上升研究的目的是为患有肺炎的患者开发，整合和测试可扩展监测和分类系统所需的基础技术。 The objective is to integrate a wireless, noninvasive, week-long wearable, and multimodal physiological sensor platform (e-tattoos) with a dedicated integrated circuit (IC), connect it to an FDA (U.S. Food and Drug Administration) cleared virtual patient monitoring platform (Sickbay) which also hosts a customized deep learning algorithm, for the continuous monitoring and assessment of the severity of progressive pneumonia.结果将是一个游戏的硬件和软件系统，可为高度感染和严重的患者提供持续的监控和智能评估，同时还可以保护医疗保健提供者免受感染和污染的影响。世界上长期缺乏长期，持续和可伸缩的临床临床表现平台的长期挑战，无法与全球性疾病进行战斗。肺炎的进展与生命体征的变化有关，例如核心体温，呼吸率，心率，血氧安全性等。由于对患有肺炎风险的患者的临床定义可能是短暂且不可预测的，因此无论是在医院还是在家中，都必须进行连续的多模式监测和准确的评估。五名研究人员将多式联运可穿戴传感器（LU），混合信号IC设计（L​​I），时间序列数据分析（MIAO），临床系统集成和可扩展患者监测（RUSIN）以及重症监护医学（Jain）汇集了公认的专业知识。这个多学科的工程和临床团队试图通过以下方式解决这一系统级别的挑战：1）开发具有ETATTOO的无线可穿戴传感器，具有专用IC，具有无创和为期一周的多模式患者监测的专用IC； 2）通过FDA（美国食品和药物管理局）清除了虚拟的患者监测平台，Sickbay通过FDA（美国食品和药物管理局）与电子纹身的开发和集成与E纹身的开发和集成； 3）对20名在德克萨斯儿童医院进行性肺炎的患者进行电子纹身和算法验证。对社会的更广泛影响正在极大地改善如何监测重症患者，并培训下一代工程师进行融合研究。最终的愿景是建立一种可扩展的方法，以安全监视患者并在全国范围内策划高质量的护理。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响来评估，被认为是珍贵的支持。

项目成果

An 84-dB-SNDR Low-OSR Fourth-Order Noise-Shaping SAR With an FIA-Assisted EF-CRFF Structure and Noise-Mitigated Push-Pull Buffer-in-Loop Technique

• DOI: 10.1109/jssc.2022.3199241
• 发表时间: 2022-12
• 期刊: IEEE Journal of Solid-State Circuits
• 影响因子: 5.4
• 作者: Tian Xie;Tzu-Han Wang;Zhe Liu;Shaolan Li

Effects of AC frequency on the capacitance measurement of hybrid response pressure sensors

交流频率对混合响应压力传感器电容测量的影响

• DOI: 10.1039/d2sm01250b
• 发表时间: 2022
• 期刊: Soft Matter
• 影响因子: 3.4
• 作者: Li, Zhengjie;Ha, Kyoung-Ho;Wang, Zheliang;Kim, Sangjun;Davis, Ben;Lu, Ruojun;Sirohi, Jayant;Lu, Nanshu
• 通讯作者: Lu, Nanshu

Seeing inside a body in motion

观察身体内部的运动

• DOI: 10.1126/science.adc8732
• 发表时间: 2022
• 期刊: Science
• 影响因子: 56.9
• 作者: Tan, Philip;Lu, Nanshu
• 通讯作者: Lu, Nanshu

Strategies for body-conformable electronics

• DOI: 10.1016/j.matt.2022.02.006
• 发表时间: 2022-04-06
• 期刊: MATTER
• 影响因子: 18.9
• 作者: Liu, Siyi;Rao, Yifan;Lu, Nanshu
• 通讯作者: Lu, Nanshu