Project Summary Sickle cell disease (SCD) is severe hematological disease seen in Sub-Saharan Africa and the U.S., affecting up to 3% of the newborn population Africa. In the US, the disease affects 100,000 Americans, but is disproportionately borne by African Americans with one out of every 365 African-Americans affected. SCD patients experience vaso-occlusive crises (VOCs) as acute bouts of pain. Currently, the only cure for SCD is a bone marrow transplant but the cost and difficulty of this procedure results in SCD patients often choosing to instead manage their disease with treatments and routine monitoring. However, there is currently no objective measure or lab test to determine VOCs and associated pain severity. Routine monitoring of red blood cell (RBC) health, however, could provide a window for avoiding VOCs and enable more thorough, quantitative analysis of therapeutic interventions. The goal of this project is to create a prototype device suitable for use at the point of care for monitoring SCD patients by advancing a novel technology for rapid, automated, high throughput red blood cell (RBC) imaging. The approach is based on quantitative phase imaging (QPI) to create a hologram of every individual RBC in a sample. These imaging data can then be evaluated for the proportion of sickled cells using machine learning. Further, since up to 10^6 RBCs are imaged in each sample, a large volume of data will be obtained, suitable for developing AI algorithms that can reveal additional information. Significantly, the device is compact and low cost, indicating that there is significant potential for translation to a point of care device. In this project, we will seek to translate our technology, with demonstrated high throughput imaging capabilities, to realize a prototype device suitable for use in clinical trials. They key development steps include development of the imaging device to enable a robust, compact form factor, advancing the design of the microfluidic device that carries the RBC's and creating new and improved algorithms to enable high throughput segmentation and analysis of the obtained imaging data. The output will be a prototype device for assessing the RBC health of individuals based using a small blood draw which can be analyzed locally to enable treatment decisions more quickly.