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.

该项目旨在应对与Covid-19爆发有关的持续挑战，以及在测试套件几乎无法获得，昂贵，基于实验室且缓慢的现实中进行及时诊断的需求。生物特征代谢物的分析有可能在临床上根据特定的生物标志物组合来监测个体的健康。这项探索性研究将评估汗水代谢物生物识别技术在患有和没有症状的人类受试者中检测COVID19感染的敏感性和特异性。所提出的方法涉及一种根本不同的测试方法，同时吸引了新颖的跨学科观点：将医疗技术，人工智能和机器学习结合在一起，以预防和诊断感染性严重疾病。这将通过促进人机组合以提高整体检测性能来减轻人类对人类的认知负担。该项目旨在开发工具，以实现适用于大规模人口的Covid-19的实时和准确筛查，以帮助社区面临进一步的传播。通过监测汗水中的疾病生物标志物，提出的方法也具有无创的优势。 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，并通过适当的网站公开提供。通过该项目实现的结果和结果将增强工程课程，包括图像处理，生物识别技术和机器学习。该项目所产生的进步将由调查人员通过出版物和网络研讨会传播。拟议的研究将创建一种新的高光谱成像方法，以获取受COVID-19的特异性影响的汗液代谢产物，以通过模式识别策略来处理。高光谱成像是用于非破坏性分析的强大工具，可以实时监测材料的空间频谱信息。该提案旨在使用紧凑的高光谱成像仪在高光谱数据立方体中设计，提取和评估跨预定波长的图像，而不涉及化学方法的特征。在这项研究期间，将开发出一个高光谱指纹图像的新型数据集。先进的图像处理技术将用于提取丰富的信号和机器学习，以供训练模式分类器，以区分患病和未切除的受试者。此外，该项目旨在在化学和图像处理的最新进展之间建立桥梁，从而基于根据高光谱域中量化的判别生物标志物来创建一个新颖的COVID-19概况的有效表示。使用试剂盒和仪器（例如分光光度计计）测量了人汗中生化含量的浓度。通过这项研究，相关的空间信息将与相应的光谱签名集成在一起，以通过人工智能诊断。拟议的COVID-19探测器将使用机器学习算法的标准性能指标进行评估，并将其与基于卫生棉条的测试方法进行比较。该奖项反映了NSF的法定任务，并且认为值得通过基金会的知识分子优点和更广泛影响的评估标准通过评估来获得支持。

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