Smartphone-Based Colorimetric Analysis of Urine Test Strips for At-Home Prenatal Care

Flaucher M, Nissen M, Jäger K, Titzmann A, Pontones C, Hübner H, Fasching P, Beckmann M, Gradl S, Eskofier B (2022)

Publication Language: English

Publication Type: Journal article

Publication year: 2022

Journal

IEEE Journal of Translational Engineering in Health and Medicine Institute of Electrical and Electronics Engineers (IEEE)

Pages Range: 1-9

Article Number: 2800109

Journal Issue: 10

DOI: 10.1109/JTEHM.2022.3179147

Open Access Link: https://ieeexplore.ieee.org/document/9785816

Abstract

Objective: Clinical urine tests are a key component of prenatal care. As of now, urine test strips are evaluated through a time consuming, often error-prone and operator-dependent visual color comparison of test strips and reference cards by medical staff. Methods and procedures: This work presents an automated pipeline for urinalysis with urine test strips using smartphone camera images in home environments, combining several image processing and color combination techniques. Our approach is applicable to off-the-shelf test strips in home conditions with no additional hardware required. For development and evaluation of our pipeline we collected image data from two sources: i) A user study (26 participants, 150 images) and ii) a lab study (135 images). Results: We trained a region-based convolutional neural network that is able to detect the urine test strip location and orientation in images with a wide variety of light conditions, backgrounds and perspectives with an accuracy of 85.5%. The reference card can be robustly detected through a feature matching approach in 98.6% of the images. Color comparison by Hue channel (0.81 F1-Score), Matching factor (0.80 F1-Score) and Euclidean distance (0.70 F1-Score) were evaluated to determine the urinalysis results. Conclusion: We show that an automated smartphone-based colorimetric analysis of urine test strips in a home environment is feasible. It facilitates examinations and provides the possibility to shift care into an at-home environment. Clinical impact: The findings demonstrate that routine urine examinations can be transferred into the home environment using a smartphone. Simultaneously, human error is avoided, accuracy is increased and medical staff is relieved.

Authors with CRIS profile

Madeleine Flaucher Machine Learning and Data Analytics Lab Michael Nissen Machine Learning and Data Analytics Lab Katharina Jäger Machine Learning and Data Analytics Lab Adriana Titzmann Department of Obstetrics and Gynaecology Constanza Pontones Department of Obstetrics and Gynaecology Hanna Hübner Department of Obstetrics and Gynaecology Peter Fasching Professur für Translationale Frauenheilkunde und Geburtshilfe Matthias Beckmann Lehrstuhl für Geburtshilfe und Frauenheilkunde Stefan Gradl-Trautvetter Machine Learning and Data Analytics Lab Björn Eskofier Machine Learning and Data Analytics Lab

Related research project(s)

SMART Start: Smarte Sensorik in der Schwangerschaft - Ein integratives Konzept zur digitalen, präventiven Versorgung schwangerer Frauen (SMART Start) March 1, 2020 - Jan. 31, 2024

How to cite

APA:

Flaucher, M., Nissen, M., Jäger, K., Titzmann, A., Pontones, C., Hübner, H.,... Eskofier, B. (2022). Smartphone-Based Colorimetric Analysis of Urine Test Strips for At-Home Prenatal Care. IEEE Journal of Translational Engineering in Health and Medicine, 10, 1-9. https://doi.org/10.1109/JTEHM.2022.3179147

MLA:

Flaucher, Madeleine, et al. "Smartphone-Based Colorimetric Analysis of Urine Test Strips for At-Home Prenatal Care." IEEE Journal of Translational Engineering in Health and Medicine 10 (2022): 1-9.

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