Harnessing Coded Ptychography to Deliver AI-powered Evaluation of Unstained Lung Biopsies at the Point-Of-Care

母公司第一阶段SBIR赠款旨在分析肺FNAs的定量时相成像 使用在不同波长的光捕获的编码光层析显微镜 (特定目标#1)，并证明了Al可以正确地检测出未染色的细胞 图像(具体目标2)。邀请补充对象Arianna Pryor直接支持 具体目标#2预计将使我们能够为Al寻求更多样化的解决方案空间 开发模型来检测细胞上未染色的QPI成像，产生更多 稳健的解决方案，并扩大母公司赠款的结果和影响。普莱尔女士的 结合她的教育，开发肺细胞学Al模型的经验 生物医学工程方面的研究使她成为该项目的理想人选。此外， 技术经验和指导、临床医生直接互动和创业 在一家初创公司工作获得的经验直接支持她的成长和未来 职业目标。

The parent Phase I SBIR grant aims to analyze quantitative phase imaging of lung FNAs using coded ptychography microscopy captured at different wavelengths of light (Specific Aim #1), and demonstrate that Al can correctly detect cells on unstained phase images (Specific Aim #2). Engaging the supplementee, Arianna Pryor, to directly support specific aim #2 is expected to allow us to pursue a more diverse solution space for Al model development to detect cells on the unstained QPI imaging, resulting in a more robust solution and amplifying the results and impact of the parent grant. Ms. Pryer's experience in Al model development for lung cytology, in conjunction with her education in biomedical engineering makes her an ideal candidate for the project. Additionally, the technical experience and mentoring, direct clinician interactions and entrepreneurial experience gained through working in a startup directly supports her growth and future career goals.