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Proceedings Paper

Reducing user error in dipstick urinalysis with a low-cost slipping manifold and mobile phone platform (Conference Presentation)
Author(s): Gennifer T. Smith; Nicholas Dwork; Saara A. Khan; Matthew Millet; Kiran Magar; Mehdi Javanmard; Audrey K. Bowden
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Paper Abstract

Urinalysis dipsticks were designed to revolutionize urine-based medical diagnosis. They are cheap, extremely portable, and have multiple assays patterned on a single platform. They were also meant to be incredibly easy to use. Unfortunately, there are many aspects in both the preparation and the analysis of the dipsticks that are plagued by user error. This high error is one reason that dipsticks have failed to flourish in both the at-home market and in low-resource settings. Sources of error include: inaccurate volume deposition, varying lighting conditions, inconsistent timing measurements, and misinterpreted color comparisons. We introduce a novel manifold and companion software for dipstick urinalysis that eliminates the aforementioned error sources. A micro-volume slipping manifold ensures precise sample delivery, an opaque acrylic box guarantees consistent lighting conditions, a simple sticker-based timing mechanism maintains accurate timing, and custom software that processes video data captured by a mobile phone ensures proper color comparisons. We show that the results obtained with the proposed device are as accurate and consistent as a properly executed dip-and-wipe method, the industry gold-standard, suggesting the potential for this strategy to enable confident urinalysis testing. Furthermore, the proposed all-acrylic slipping manifold is reusable and low in cost, making it a potential solution for at-home users and low-resource settings.

Paper Details

Date Published: 2 May 2017
PDF: 1 pages
Proc. SPIE 10081, Frontiers in Biological Detection: From Nanosensors to Systems IX, 100810J (2 May 2017); doi: 10.1117/12.2256593
Show Author Affiliations
Gennifer T. Smith, Stanford Univ. (United States)
Nicholas Dwork, Stanford Univ. (United States)
Saara A. Khan, Stanford Univ. (United States)
Matthew Millet, Stanford Univ. (United States)
Kiran Magar, Stanford Univ. (United States)
Mehdi Javanmard, Rutgers, The State Univ. of New Jersey (United States)
Audrey K. Bowden, Stanford Univ. (United States)


Published in SPIE Proceedings Vol. 10081:
Frontiers in Biological Detection: From Nanosensors to Systems IX
Amos Danielli; Benjamin L. Miller; Sharon M. Weiss, Editor(s)

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