Quantitative histopathology for cancer prognosis using quantitative phase imaging on stained tissues

使用染色组织的定量相位成像进行癌症预后的定量组织病理学

• 批准号: 10249738
• 负责人: Kevin William Eliceiri
• 金额: $ 57.63万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-16 至 2024-03-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10249738
• 关键词: 2019-nCoV; Academia; Air; Artificial Intelligence; Back; Biology; Breath Tests; COVID-19; COVID-19 testing; Cancer Prognosis; Chemicals; Classification; Clinical; Clinical Microbiology; Computer software; Computers; Data; Devices; Exhalation; Fluorescence; Fluorescence Microscopy; Future; Glass; Goals; Histopathology; Home; Image; Imaging Device; Imaging Techniques; Individual; Industry; Influenza; Infrastructure; Interference Microscopy; Label; Light; Maps; Measures; Microscope; Modification; Morphologic artifacts; Nature; Optics; Patients; Performance; Phase; Photobleaching; Phototoxicity; Population; Preparation; Procedures; Publications; Research; Running; Sales; Slide; Specificity; Structure; System; Technology; Testing; Time; Tissue Stains; Training; Viral; Virus; Virus Diseases; algorithm training; base; clinical infrastructure; coronavirus disease; cost; deep learning; deep learning algorithm; design; imaging system; instrument; monitoring device; nanoscale; new technology; novel; operation; pandemic disease; particle; point of care; point of care testing; prototype; screening; tool

Summary Fast, accurate, and scalable testing has been recognized unanimously as crucial for mitigating the impact of COVID-19 and future pandemics. We propose a technology that allows rapid (~2 minutes) testing for SARS CoV-2. Our technology combines novel label-free imaging and dedicated deep-learning algorithms to detect and classify viral populations in exhaled air. If successful, this project will result in a device based on quantitative phase imaging and integrated AI tools, which will detect the unlabeled virus acquired by the patient’s breath condensed on a microscope slide. Toward this goal, we will advance Spatial Light Interference Microscopy (SLIM), an ultrasensitive label-free imaging technique, proven to measure structures down to the sub-nanometer scale. SLIM was developed in the PI’s Lab at UIUC, its original publication received 490 citations to date, and has been commercialized by Phi Optics (Research Park, UIUC), with sales across the world in both academia and industry. Applying the computed fluorescence maps back to the QPI data, we propose to measure nanoscale features of viral particles, with high specificity, minimal preparation time, and independent of clinical infrastructure. As a result, the new technology will eventually be ideal for point-of-care settings, surveillance screening and as a home monitoring device. We anticipate that our approach will be scalable to other viruses, with new imaging and training data.

概括 快速，准确且可扩展的测试已被一致认为对于缓解措施至关重要 COVID-19和未来大流行的影响。我们提出了一种允许快速的技术（〜2 分钟）测试SARS COV-2。我们的技术结合了新颖的无标签成像和 专门的深度学习算法，以检测和分类呼出空气中的病毒种群。如果 成功，该项目将导致基于定量阶段成像并集成的设备 AI工具，该工具将检测患者在患者的呼吸上获得的未标记的病毒 显微镜载玻片。朝向这个目标，我们将提高空间光干扰显微镜 （Slim）是一种超敏感的无标签成像技术，证明可以测量结构向下 子纳米尺度。 Slim是在UIUC的Pi的实验室中开发的，其原始出版物 迄今 UIUC），全球销售在学术界和行业中。 将计算的荧光图应用回QPI数据，我们建议测量 病毒颗粒的纳米级特征，具有高特异性，最小的准备时间和 独立于临床基础设施。结果，新技术最终将是理想的选择 护理点设置，监视筛查和作为家庭监控设备。我们期待 通过新的成像和培训数据，我们的方法将可扩展到其他病毒。