» Articles » PMID: 36798885

Improving the Reliability of Smartphone-based Urine Colorimetry Using a Colour Card Calibration Method

Overview
Journal Digit Health
Date 2023 Feb 17
PMID 36798885
Authors
Affiliations
Soon will be listed here.
Abstract

Objective: Urine colorimetry using a digital image-based colorimetry is potentially an accessible hydration assessment method. This study evaluated the agreement between urine colorimetry values measured with different smartphone brands under various lighting conditions in patients with dengue fever.

Methods: The urine samples were photographed in a customized photo box, under five simulated lighting conditions, using five smartphones. These images were analyzed using Adobe Photoshop to obtain urine Red, Green and Blue (RGB) values with and without colour correction. A commercially available colour calibration card was used for colour correction. Using intraclass correlation coefficient (ICC), inter-phone and intra-phone agreements of urine RGB values were analyzed.

Results: Without colour correction, the various smartphones produced the highest agreement for Blue and Green values under the 'daylight' lighting condition. With colour correction, ICC values showed 'exceptional' inter-phone and intra-phone agreement for the Blue and Green values (ICC > 0.9). Red values showed 'poor' (ICC < 0.5) agreement with and without colour correction in all lighting conditions. Out of the five phones compared in this study, Phone 4 produced the lowest intra-phone agreement.

Conclusions: Colour calibration using photo colour cards improved the reliability of smartphone-based urine colorimetry, making this a promising point-of-care hydration assessment tool using the ubiquitous smartphone.

Citing Articles

Machine Learning-Based Quantification of Lateral Flow Assay Using Smartphone-Captured Images.

Davis A, Tomitaka A Biosensors (Basel). 2025; 15(1.

PMID: 39852070 PMC: 11763061. DOI: 10.3390/bios15010019.


Artificial Intelligence in Diagnostics: Enhancing Urine Test Accuracy Using a Mobile Phone-Based Reading System.

Kim H, Kim M, Zhang H, Kim H, Jeon J, Seo Y Ann Lab Med. 2024; 45(2):178-184.

PMID: 39676422 PMC: 11788702. DOI: 10.3343/alm.2024.0304.


Non-invasive approaches to hydration assessment: a literature review.

Tahar A, Zrour H, Dupont S, Pozdzik A Urolithiasis. 2024; 52(1):132.

PMID: 39325254 DOI: 10.1007/s00240-024-01630-y.


RGB Color Model: Effect of Color Change on a User in a VR Art Gallery Using Polygraph.

Drofova I, Richard P, Fajkus M, Valasek P, Sehnalek S, Adamek M Sensors (Basel). 2024; 24(15).

PMID: 39123975 PMC: 11314843. DOI: 10.3390/s24154926.

References
1.
Kalayanarooj S, Vaughn D, Nimmannitya S, Green S, Suntayakorn S, Kunentrasai N . Early clinical and laboratory indicators of acute dengue illness. J Infect Dis. 1997; 176(2):313-21. DOI: 10.1086/514047. View

2.
Koo T, Li M . A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J Chiropr Med. 2016; 15(2):155-63. PMC: 4913118. DOI: 10.1016/j.jcm.2016.02.012. View

3.
Armstrong L, Maresh C, Castellani J, Bergeron M, Kenefick R, LaGasse K . Urinary indices of hydration status. Int J Sport Nutr. 1994; 4(3):265-79. DOI: 10.1123/ijsn.4.3.265. View

4.
Vienot F . Relations between inter- and intra-individual variability of color-matching functions. Experimental results. J Opt Soc Am. 1980; 70(12):1476-83. DOI: 10.1364/josa.70.001476. View

5.
Barley O, Chapman D, Abbiss C . Reviewing the current methods of assessing hydration in athletes. J Int Soc Sports Nutr. 2020; 17(1):52. PMC: 7602338. DOI: 10.1186/s12970-020-00381-6. View