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.

小企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力是利用人工智能（AI）技术加速疫苗和抗病毒药物的开发。该项目将开发技术，在人工检测之前几天或几周检测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，这些化合物的特点是对快速突变的病毒株（如流感和SARS-CoV-2）有效。这将影响COVID-19研究和一般病毒学。SBIR第一阶段项目将研究人工智能技术，以加速在空斑试验中测试抗病毒药物，用于开发疫苗和抗病毒药物。这些测定通过观察感染对易感细胞培养物的影响来测量样品中感染性病毒颗粒的数量。 目前，该测定需要2-14天，因为需要几轮感染以确保准确的阅读。该项目将推进人工智能技术，自动检测显微镜图像中的感染细胞，而无需人为干预或耗时的准备，从而提高这些检测的吞吐量。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的时间过程，用于训练人工智能模型，以在大型细胞培养板上自动测量病毒感染; 2）研究显微镜图像采集方法，以便于整合现有工作流程和图像质量; 3）评估各种人工智能技术的适用性; 4）确定检测准确性并与传统检测方法进行比较。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而被认为值得支持。