I-Corps: Nutritional and health risk assessment pre-screening tool based on 3D optical scanning
I-Corps: Nutritional and health risk assessment pre-screening tool based on 3D optical scanning
批准号:
1947394
负责人:
Sima Sobhiyeh
金额:
$5.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-09-15 至 2021-07-31
中文摘要
这个I-Corps项目的更广泛的影响/商业潜力将是,它可以通过减少与生物识别筛查所需的个性化护理的体力劳动相关的成本以及医学专家花费的时间来节省数十万美元的健康评估成本。它将允许从业者通过识别患有代谢和心血管疾病的高风险人群来“分类”他们的筛查,以进行进一步的深入评估。此外,该I-Corps项目可以减少与生物特征筛查相关的法律的责任和风险,因为该技术依赖于非侵入性和非侵入性的3D光学扫描技术。该I-Corps项目将是首批将基于3D光学扫描的非侵入性、非侵入性和全自动健康预筛查工具商业化的项目之一。它是一个Web应用程序,可以分析人体受试者的3D光学扫描,并提供有关受试者健康状况的详细报告。Web应用程序的一个独特功能是,它可以容纳来自大多数当前可用的3D光学扫描仪的数据,这些扫描仪的当前系统输出是机器特定的,并且其输出内容现在非常有限。更具体地说,该技术通过在受试者的3D光学扫描上应用几何数学来量化受试者的身体大小和形状,然后使用机器学习方法来估计受试者的健身和健康状况,包括受试者的身体成分和慢性疾病(如糖尿病和心脏病)的风险。该奖项反映了NSF的法定使命,并通过使用基金会的学术价值和更广泛的影响审查标准。
英文摘要
The broader impact/commercial potential of this I-Corps project will be that it can save hundreds of thousands of dollars in health assessment costs by reducing costs associated with manual labor for the individualized care required at biometric screenings and the amount of time spent by medical experts. It will allow practitioners to "triage" their screenings by identifying people who are at high risk of developing metabolic and cardiovascular diseases for further in-depth evaluations. Moreover, this I-Corps project can reduce legal liability and risk associated with biometric screenings as the technology relies on 3D optical scanning technology, which is non-invasive and non-intrusive.This I-Corps project will be one of the first to commercialize a non-invasive, non-intrusive, and fully automated health pre-screening tool based on 3D optical scanning. It is a web application that analyzes a 3D optical scan of a human subject and provides a detailed report on the subject's fitness and wellness status. A unique feature of the web application is that it can accommodate data from most currently available 3D optical scanners whose current system outputs are machine-specific and whose outputs are now very limited in their contents. More specifically, the technology quantifies a subject's body size and shape by applying geometrical mathematics on the subject's 3D optical scan and then uses machine learning approaches to estimate the subject's fitness and wellness status including the subject's body composition and risk of chronic diseases such as diabetes and heart disease.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.
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