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

摘要 快速、准确和可扩展的测试已被一致认为是缓解 新冠肺炎的影响和未来的大流行。我们提出了一种技术，允许快速(~2 分钟)检测SARS CoV-2。我们的技术结合了新的无标签成像和 专门的深度学习算法，用于检测和分类呼出空气中的病毒种群。如果 该项目的成功将导致一种基于定量相位成像和集成的设备 人工智能工具，它将检测患者的呼吸浓缩在 显微镜载玻片。为了实现这一目标，我们将推进空间光干涉显微镜 (SLIM)是一种超灵敏的无标记成像技术，已被证明可以测量精确到 亚纳米尺度。SILM是在UIUC的PI实验室开发的，它的原始出版物 到目前为止，已收到490篇引文，并已由Phi Optics(Research Park， UIUC)，在学术界和产业界都有销售。 将计算的荧光地图应用于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，最短的制备时间，以及 独立于临床基础设施。因此，这项新技术最终将成为 医疗点设置、监视筛查和作为家庭监控设备。我们期待着 我们的方法将可扩展到其他病毒，并提供新的成像和训练数据。

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.