SBIR Phase I: Machine Learning for Early Detection of COVID-19 Plaques in Cells

SBIR 第一阶段：机器学习用于早期检测细胞中的 COVID-19 斑块

• 批准号: 2029707
• 负责人: Ilya Goldberg
• 金额: $ 25.56万
• 依托单位: ViQi 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: 2020-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2029707&HistoricalAwards=false
• 关键词: SBIR; Phase; Machine; Learning; Early

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to accelerate the development of vaccines and anti-virals with artificial intelligence (AI) techniques. This project will develop technology to detect changes in virus-infected cells days or weeks before they can be detected manually. This will accelerate studies for novel anti-viral compounds characterizing their effectiveness on rapidly mutating viral strains, such as influenza and SARS-CoV-2. This will impact COVID-19 research and general virology.This SBIR Phase I project will investigate AI techniques to accelerate testing of anti-viral agents in plaque assays for the development of vaccines and anti-virals. These assays measure the number of infectious viral particles in a sample by observing the effects of infection on a culture of susceptible cells. Currently, the assay takes 2-14 days because several rounds of infection are necessary to ensure an accurate reading. This project will advance AI techniques to automatically detect infected cells in microscopy images without human intervention or time-consuming preparations, thereby increasing the throughput for these assays. To achieve this goal, this project will: 1) Collect a time-course of microscopy images of infected cell cultures for training an AI model to measure virus infections automatically on large cell culture plates; 2) Investigate microscopy image acquisition approaches with respect to ease of integration in existing workflows and image quality; 3) Evaluate the suitability of various AI techniques; 4) Determine the detection accuracy and compare it with traditional assays.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）I期项目的更广泛的影响/商业潜力是通过人工智能（AI）技术加速疫苗和反病毒的发展。该项目将开发技术，以检测几天或几周才能手动检测到病毒感染细胞的变化。这将加速对新型抗病毒化合物的研究，这些研究表征了它们对快速突变病毒菌株的有效性，例如流感和SARS-COV-2。这将影响COVID-19的研究和通用病毒学。该SBIR I期项目将研究AI技术，以加速菌斑测定中的抗病毒药物的测试，以开发疫苗和抗病毒。这些测定方法通过观察感染对易感细胞培养的影响，测量样品中传染性病毒颗粒的数量。 目前，该测定需要2-14天，因为需要进行几轮感染以确保准确阅读。该项目将推进AI技术，以在无需人工干预或耗时准备的情况下在显微镜图像中自动检测受感染的细胞，从而增加这些测定的吞吐量。为了实现这一目标，该项目将：1）收集感染细胞培养物的显微镜图像，以训练AI模型，以在大型细胞培养板上自动测量病毒感染； 2）研究了显微镜图像采集方法，以简化现有工作流程和图像质量的整合； 3）评估各种AI技术的适用性； 4）确定检测准确性并将其与传统测定法相比。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的。