EAGER: COVID-19 Real-time Detection via Hyperspectral Analysis of Sweat Metabolite Biometrics

EAGER：通过汗液代谢物生物识别的高光谱分析进行 COVID-19 实时检测

• 批准号: 2036151
• 负责人: Emanuela Marasco
• 金额: $ 10万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2036151&HistoricalAwards=false
• 关键词: EAGER; COVID; 19; Real; time

The project seeks to address the ongoing challenge pertaining to the Covid-19 outbreak and the need for prompt diagnosis in a reality where the Test Kits are scarcely available, expensive, labs-based and slow. The analysis of biometric metabolites has the potential to be clinically applicable in monitoring the health of individuals based on particular biomarker combinations. This exploratory study will evaluate the sensitivity and specificity of sweat metabolite biometrics for detecting COVID19 infection in human subjects with and without symptoms. The proposed methodology involves a radically different testing approach while engaging novel interdisciplinary perspectives: medical technology, artificial intelligence and machine learning are combined to prevent and diagnose an infectious serious disease. This will reduce the cognitive burden on humans by promoting human-machine teaming for improved overall detection performance. The project seeks to develop tools that will enable real-time and accurate screening of COVID-19 applicable on a large-scale population, aiding the community to face further spread of it. Through monitoring of the disease biomarkers in sweat, the proposed method has also the advantage of being non-invasive. The methods, theory, and data resulting from this proposal will impact the scientific community in several positive ways and will be made publicly available through an appropriate website. Findings and results achieved through this project will enhance the engineering curricula, including image processing, biometrics and machine learning. Advances produced with this project will be disseminated by the investigators through publications and web seminars. The proposed research will create a new hyperspectral imaging methodology to acquire sweat metabolites specifically impacted by COVID-19 to be processed through pattern recognition strategies. Hyperspectral imaging is a powerful tool for non-destructive analysis, enabling real-time monitoring of spatially resolved spectral information of materials. This proposal seeks to design, extract and evaluate features from hyperspectral data cubes, stacked images across pre-defined wavelengths, using a compact hyperspectral imager without involving chemical methods. A novel dataset of hyperspectral fingerprint images will be developed during this research study. Advanced image processing techniques will be used to extract rich signals and machine learning for training pattern classifiers to distinguish between diseased and non-diseased subjects. In addition, this project seeks to build a bridge between recent advances in chemistry and image processing, creating a novel effective representation of COVID-19 profiles based on discriminative biomarkers quantified in terms of concentrations in the hyperspectral domain. The concentrations of the biochemical content in human sweat have been measured using reagent kits and instruments such as spectrophotometers. With this research, relevant spatial information will be integrated with corresponding spectral signature to enable the diagnosis through artificial intelligence. The proposed COVID-19 detector will be assessed using standard performance metrics of machine learning algorithms and compared to tampon-based testing 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.

该项目旨在解决与Covid-19疫情有关的持续挑战，以及在检测试剂盒几乎不可用、昂贵、基于实验室且缓慢的现实中迅速诊断的需求。生物代谢物的分析具有临床应用于基于特定生物标志物组合监测个体健康的潜力。这项探索性研究将评估汗液代谢物生物特征检测在有症状和无症状的人类受试者中检测COVID 19感染的灵敏度和特异性。所提出的方法涉及一种完全不同的测试方法，同时采用新颖的跨学科视角：将医疗技术、人工智能和机器学习相结合，以预防和诊断传染性严重疾病。这将通过促进人机合作来改善整体检测性能，从而减轻人类的认知负担。该项目旨在开发适用于大规模人群的COVID-19实时准确筛查工具，帮助社区应对其进一步传播。通过监测汗液中的疾病生物标志物，该方法还具有非侵入性的优势。 该提案产生的方法、理论和数据将以几种积极的方式影响科学界，并将通过适当的网站公开提供。通过该项目取得的发现和成果将加强工程课程，包括图像处理，生物识别和机器学习。调查人员将通过出版物和网络研讨会传播该项目取得的进展。拟议的研究将创建一种新的高光谱成像方法，以获取特别受COVID-19影响的汗液代谢物，并通过模式识别策略进行处理。高光谱成像是一种强有力的无损分析工具，能够实时监测材料的空间分辨光谱信息。该提案旨在设计、提取和评估高光谱数据立方体的特征，使用紧凑的高光谱成像仪，而不涉及化学方法，在预定义的波长上堆叠图像。在这项研究中，将开发一个新的高光谱指纹图像数据集。先进的图像处理技术将用于提取丰富的信号和机器学习，用于训练模式分类器，以区分患病和非患病受试者。此外，该项目旨在在化学和图像处理的最新进展之间建立一座桥梁，基于高光谱域中浓度量化的区分性生物标志物，创建COVID-19谱的新型有效表示。人类汗液中的生化成分的浓度已经使用试剂盒和仪器如粘度计测量。通过这项研究，相关的空间信息将与相应的光谱特征相结合，从而通过人工智能进行诊断。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

A Novel Time-Series Database of Finger Hypercubes Before and After Hand Sanitization with Demographics

手部消毒前后手指超立方体的新颖时间序列数据库与人口统计

• 发表时间: 2022
• 期刊: and Image Processing. ICPR 2022 International Workshops and Challenges
• 作者: S. Sriram;E. Marasco
• 通讯作者: E. Marasco

Mitigating the Impact of Hand Sanitizer on the Spectral Signature of Finger Hypercubes

减轻洗手液对手指超立方体光谱特征的影响

• DOI: 10.1109/ijcb54206.2022.10008002
• 发表时间: 2022
• 期刊: IEEE
• 作者: Marasco, Emanuela;Tao, Yuanting
• 通讯作者: Tao, Yuanting