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Proceedings Paper

Low-cost computing and network communication for a point-of-care device to perform a 3-part leukocyte differential
Author(s): Amy J. Powless; Lauren E. Feekin; Joshua A. Hutcheson; Daisy V. Alapat; Timothy J. Muldoon
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Paper Abstract

Point-of-care approaches for 3-part leukocyte differentials (granulocyte, monocyte, and lymphocyte), traditionally performed using a hematology analyzer within a panel of tests called a complete blood count (CBC), are essential not only to reduce cost but to provide faster results in low resource areas. Recent developments in lab-on-a-chip devices have shown promise in reducing the size and reagents used, relating to a decrease in overall cost. Furthermore, smartphone diagnostic approaches have shown much promise in the area of point-of-care diagnostics, but the relatively high per-unit cost may limit their utility in some settings. We present here a method to reduce computing cost of a simple epi-fluorescence imaging system using a Raspberry Pi (single-board computer, <$40) to perform a 3-part leukocyte differential comparable to results from a hematology analyzer. This system uses a USB color camera in conjunction with a leukocyte-selective vital dye (acridine orange) in order to determine a leukocyte count and differential from a low volume (<20 microliters) of whole blood obtained via fingerstick. Additionally, the system utilizes a "cloud-based" approach to send image data from the Raspberry Pi to a main server and return results back to the user, exporting the bulk of the computational requirements. Six images were acquired per minute with up to 200 cells per field of view. Preliminary results showed that the differential count varied significantly in monocytes with a 1 minute time difference indicating the importance of time-gating to produce an accurate/consist differential.

Paper Details

Date Published: 4 March 2016
PDF: 6 pages
Proc. SPIE 9715, Optical Diagnostics and Sensing XVI: Toward Point-of-Care Diagnostics, 97150A (4 March 2016); doi: 10.1117/12.2213267
Show Author Affiliations
Amy J. Powless, Univ. of Arkansas (United States)
Lauren E. Feekin, Univ. of Arkansas (United States)
Joshua A. Hutcheson, Univ. of Arkansas (United States)
Daisy V. Alapat, Univ. of Arkansas for Medical Sciences (United States)
Timothy J. Muldoon, Univ. of Arkansas (United States)


Published in SPIE Proceedings Vol. 9715:
Optical Diagnostics and Sensing XVI: Toward Point-of-Care Diagnostics
Gerard L. Coté, Editor(s)

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