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Smartphone-based Colorimetric Analysis of Urine Dipsticks for At-Home Prenatal Care

Master's Thesis

Smartphone-based Colorimetric Analysis of Urine Dipsticks for At-Home Prenatal Care

Advisors

Michael Nisse (M.Sc.), Dr. Hanna Hübner (UK Erlangen), Prof. Dr. B. Eskofier

Duration

07/2020-12/2020

Abstract:

The early detection of maternal and fetal diseases and ensuring maternal and fetal health are the
main objectives of prenatal care. An essential part of prenatal care are laboratory diagnostics,
including the examination of urine samples, which are conducted at regular intervals as part of
the general examination repeatedly scheduled in prenatal care [1]. Urine tests based on test strips
belong to the so-called Point of Care Testing or patient-oriented immediate diagnostics, which are
playing an increasing role in the healthcare system and are also benetting from the progressive
integration of smartphones [2].
In this research work, a method is to be developed that automates the evaluation of urine test
strips with the aid of a smartphone, allowing the patient to perform the examination complety
from home. Previous studies showed, that the potential of smartphones for urinalysis is high and
an evaluation without further equipment is feasible [3]. Some studies already investigated the
inuence of varying illumination and dierent smartphones [4, 5]. However, little work regarding
pregnant women, obstetrical care, a large study population as well as the respective feasibility
and usability questions exists in literature.
Therefore the aim of this research work is to evaluate the feasibilty of an automated colorimetric
analysis of urine dipsticks using image processing and/or machine learning and a respective
smartphone application for use in a home environment within prenatal care.

 

References:

[1] Gemeinsamer Bundesausschuss. Richtlinien des Gemeinsamen Bundesausschusses über die ärztliche Betreuung während der Schwangerschaft
und nach der Entbindung (Mutterschafts-Richtlinien). (1985, geändert 2013).
[2] Sandeep Vashist. Point-of-Care Diagnostics: Recent Advances and Trends. Biosensors, 7(4):62, December 2017.
[3] Junjie Liu, Zhaoxin Geng, Zhiyuan Fan, Jian Liu, and Hongda Chen. Point-of-care testing based on smartphone: The current state-of-the-art
(20172018). Biosensors and Bioelectronics, 132:1737, May 2019.
[4] Haakon Karisen and Tao Dong. Illumination and device independence for colorimetric detection of urinary biomarkers with smartphone. In
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 51845187,
Orlando, FL, USA, August 2016. IEEE.
[5] Yong He, Kai Dong, Yongheng Hu, and Tao Dong. Colorimetric recognition for urinalysis dipsticks based on quadratic discriminant analysis.
In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 39023905,
Seogwipo, July 2017. IEEE.