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.

冠状病毒感染可能会导致危及生命的肺炎，在某些人群中死亡率超过10%，这可能会迅速压倒任何医疗保健系统。在医院或自我隔离状态下对感染者和疑似感染者进行持续监测，有助于优化分诊和治疗。然而，无论是在临床还是在自由生活的环境中，目前还没有一个可用的移动终端和算法平台能够对肺炎患者进行可靠、全面、连续和长期的监测和评估。这项ASCENT研究的目标是开发，集成和测试为感染肺炎的患者提供可扩展的监测和分诊系统所需的基础技术。其目标是将无线、无创、可穿戴、多模式生理传感器平台（e-tattoos）与专用集成电路（IC）集成，将其连接到FDA（美国食品和药物管理局）批准的虚拟患者监测平台（Sickbay），该平台还托管定制的深度学习算法，用于持续监测和评估进行性肺炎的严重程度。这将是一个改变游戏规则的硬件和软件系统，为高传染性和危重患者提供持续监测和智能评估，同时保护医疗服务提供者免受感染和污染。长期以来，世界缺乏长期，高保真，连续和可扩展的传染病患者临床监测平台，以对抗COVID-19等全球大流行病。肺炎的进展与核心体温、呼吸频率、心率、血氧饱和度等生命体征的变化相关，由于有发生肺炎风险的患者的临床恶化可能是短暂且不可预测的，因此无论是在医院还是在家中，对这一人群进行持续的多模式监测和准确评估都是必要的。这五位研究人员汇集了多模式可穿戴传感器（Lu），混合信号IC设计（Li），时间序列数据分析（Miao），临床系统集成和可扩展患者监护（Rusin）以及重症监护医学（Jain）的成熟专业知识。这个多学科工程和临床团队试图通过以下方式解决这一系统级挑战：1）开发称为e-tattoo的无线可穿戴传感器，其专用IC能够进行无创和为期一周的多模式患者监测; 2）通过FDA开发数据分析和深度学习算法，并与e-tattoo集成。（美国食品和药物管理局）批准的虚拟患者监测平台Sickbay; 3）在德克萨斯儿童医院的20名进行性肺炎患者身上进行电子纹身和算法验证。对社会的更广泛影响是显着改善重症患者的监测方式，以及培训下一代工程师进行融合研究。最终愿景是建立一种可扩展的方法，安全地监控患者并在全国范围内协调高质量的护理。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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