Simplified White Blood Cell Differential: An Inexpensive, Smartphone- and Paper-Based Blood Cell Count
AffiliationUniv Arizona, Dept Biomed Engn
MetadataShow full item record
CitationM. V. Bills, B. T. Nguyen and J. Yoon, "Simplified White Blood Cell Differential: An Inexpensive, Smartphone- and Paper-Based Blood Cell Count," in IEEE Sensors Journal, vol. 19, no. 18, pp. 7822-7828, 15 Sept.15, 2019. doi: 10.1109/JSEN.2019.2920235
JournalIEEE SENSORS JOURNAL
Rights© 2019 IEEE.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractSorting and measuring blood by cell type is extremely valuable clinically and provides physicians with key information for diagnosing many different disease states including: leukemia, autoimmune disorders, and bacterial infections. Despite the value, the present methods are unnecessarily costly and inhibitive particularly in resource poor settings, as they require multiple steps of reagent and/or dye additions and subsequent rinsing followed by manual counting using a hemocytometer, or they require a bulky, expensive equipment such as a flow cytometer. While direct on-paper imaging has been considered challenging, paper substrate offers a strong potential to simplify such reagent/dye addition and rinsing. In this paper, three-layer paper-based device is developed to automate such reagent/dye addition and rinsing via capillary action, and separating white blood cells (WBCs) from whole blood samples. Direct on-paper imaging is demonstrated using a commercial microscope attachment to a smartphone coupled with a blue LED and 500 nm long pass optical filter. Image analysis is accomplished using an original MATLAB code, to evaluate the total WBC count, and differential WBC count, i.e., granulocytes (primarily neutrophils) versus agranulocytes (primarily lymphocytes). Only a finger-prick of whole blood is required for this assay. The total assay time from finger-prick to data collection is under five minutes. Comparison with a hemocytometry-based manual counting corroborates the accuracy and effectiveness of the proposed method. This approach could he potentially used to help make blood cell counting technologies more readily available, especially in resource poor and point-of-care settings.
VersionFinal accepted manuscript
SponsorsBiomedical Imaging and Spectroscopy Training Grant from the U.S. National Institutes of Health [T32-EB000809]