SBIR Phase II: Machine Learning for Rapid Automated Viral Infectivity Assays (COVID-19)

SBIR 第二阶段：用于快速自动化病毒感染性检测的机器学习 (COVID-19)

• 批准号: 2136850
• 负责人: Ilya Goldberg
• 金额: $ 99.99万
• 依托单位: ViQi LLC
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2136850&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Machine; Learning

The broader impact of this Small Business Innovation Research (SBIR) Phase II project is to accelerate the development of antiviral drugs and vaccines for conditions such as COVID-19. The ability to accurately determine if cells are infected with virus is crucial for evaluating antiviral drugs and vaccine candidates. Currently, determining if a virus is infectious is done by infecting cells and waiting for them to die, which can take many days. The proposed technology uses artificial intelligence (AI) to analyze images of cells for signs of virus. This can be done within hours of infection instead of days, which can greatly accelerate the development of vaccines and antiviral drugs. In addition, it is simpler because the AI analysis is automated and does not need special probes or dyes to detect viruses.The proposed project will collect images of cells infected with various viruses imaged with automated microscopes. AIs will be trained to distinguish healthy and sick cells at various times after infection. The project will study many different viruses and the changes they induce in cells. The trained AIs will be used to process infectivity assays. The software will run on remote data centers and thus images will be uploaded for analysis and reporting. An AI can be trained using images from the common 96-well plate.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.

这项小型企业创新研究（SBIR）第二阶段项目的更广泛影响是加速抗病毒药和疫苗在Covid-19等条件下的开发。准确确定细胞是否感染病毒的能力对于评估抗病毒药物和候选疫苗的能力至关重要。当前，确定病毒是否具有感染性是通过感染细胞并等待死亡而完成的，这可能需要很多天。拟议的技术使用人工智能（AI）来分析细胞图像中的病毒迹象。这可以在感染的几个小时内而不是几天内完成，这可以大大加速疫苗和抗病毒药的发展。另外，这更简单，因为AI分析是自动化的，并且不需要特殊的探针或染料来检测病毒。该项目将收集用自动显微镜成像的各种病毒感染的细胞图像。 AIS将在感染后的不同时间训练，以区分健康和病态的细胞。该项目将研究许多不同的病毒及其在细胞中诱导的变化。训练有素的AI将用于处理感染性测定。该软件将在远程数据中心上运行，因此将上传图像以进行分析和报告。可以使用Common 96孔板的图像对AI进行培训。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评论标准来评估值得支持的。