STTR Phase I: An Artificial Intelligence (AI)-based algorithm using nanosensor-based salivary analytics to predict clinical outcomes in symptomatic COVID-19 patients

STTR 第一阶段：基于人工智能 (AI) 的算法，使用基于纳米传感器的唾液分析来预测有症状的 COVID-19 患者的临床结果

• 批准号: 2032579
• 负责人: Huma Jafry
• 金额: $ 24.72万
• 依托单位: NANOINNOVATIONS, LLC
• 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: 2020-12-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2032579&HistoricalAwards=false
• 关键词: STTR; Phase; Artificial; Intelligence; AI

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to provide U.S. hospitals with a tool to help accurately predict the expected severity of illness for COVID-19 infected patients at the time of initial diagnosis. A simple interface uses Artificial Intelligence-based predictive algorithms to help hospitals make informed and accurate decisions about which patients require specific care and treatment interventions. This enhanced process allows hospitals better and faster decision-making on patient care and treatments, redirecting hospital resources (including staff, hospital beds, ICU) for maximum effectiveness. In the longer term, the platform can be adapted to predict illnesses related to other infectious diseases, and also scaled for countries where availability of hospital infrastructure is limited.This Small Business Technology (STTR) Phase I project will develop a completely new category of medical diagnostic and prognostic tools via a novel approach that relies on analysis of a complex multi-variate signal, reflective of the patient’s entire salivary metabolome and proteome. Artificial intelligence tools will be used to see if signal clusters correlating with patient outcomes can be identified. This is a radical departure from traditional medical diagnostics which evaluate individual biomarkers for a clinical diagnosis. Such approaches are ill suited to the task of predicting future patient outcomes. The scope of this pilot phase work is the development of an effective algorithm and understanding algorithm efficacy and reliability in prediction and classification of outcomes for COVID-19 patients. The goals of the pilot project are to: (i) obtain COVID-19 patient bio-fluid samples, and (ii) develop machine learning techniques for an effective predictive algorithm. Multiple machine learning techniques and comparison strategies will be used for algorithm development and efficacy testing.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.

这个小企业技术转让（STTR）第一阶段项目的更广泛影响/商业潜力是为美国医院提供一种工具，帮助准确预测COVID-19感染患者在初步诊断时的预期疾病严重程度。一个简单的界面使用基于人工智能的预测算法，帮助医院做出明智和准确的决定，哪些患者需要特定的护理和治疗干预。这一增强的流程使医院能够更好、更快地对患者护理和治疗做出决策，重新分配医院资源（包括员工、病床、ICU），以实现最大效益。从长远来看，该平台可以用于预测与其他传染病相关的疾病，也可以扩展到医院基础设施有限的国家。这个小企业技术（STTR）第一阶段项目将通过一种新的方法开发一种全新的医疗诊断和预后工具，该方法依赖于对复杂的多变量信号的分析，反映了患者的整个唾液代谢组和蛋白质组。人工智能工具将被用来查看是否可以识别与患者结果相关的信号簇。这是对传统医学诊断的根本背离，传统医学诊断评估用于临床诊断的个体生物标志物。这种方法不适合预测未来患者结果的任务。该试点阶段工作的范围是开发有效的算法，并了解算法在COVID-19患者结局预测和分类方面的有效性和可靠性。该试点项目的目标是：（i）获得COVID-19患者的生物流体样本，以及（ii）开发机器学习技术以实现有效的预测算法。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。