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Effect of Illuminance on Color-based Analysis of Diabetes-Related Urine Fusion Analytes on Dipstick Using a Smartphone Camera

스마트폰 카메라를 활용한 뇨시험지 당뇨병관련 융합 분석인자의 색기반 분석에 미치는 외부 조도 영향

  • Received : 2021.03.16
  • Accepted : 2021.05.20
  • Published : 2021.05.28

Abstract

Recently, the miniaturization and digitalization for the inspection devices of point-of-care testing (POCT) are rapidly evolving. In the urine test, a lot of researches on index paper technology are being conducted because people can be self-diagnosed through visual color comparison using a urine test paper, Dipsick. The purpose of this study is to analyze the RGB values from the color changes on Dipstick Pad, which isused for urine test, using a smartphone camera. To this end, the primary, analytes in urine wasdiabetes-related parameters such as glucose, ketone body and pH, which is the most frequently tested elements, and we pursuited to quantify the changes in dipstick color caused from artificial urine containing different ranges of sugar, ketone body, and pH. In this experiment, changes in RGB values under bright and dark illuminances were compared, and changes in RGB value were monitored as a function of concentration of analytes under the ambient illumination of laboratory. As a result, color separation at the bright luminance region was good, but it did not appearat the low luminance region, and the changed profiles in RGB value under different illuminances was suggested to correct the problem of the color separation algorithm.

최근 현장검사장치의 소형화 및 디지털화가 급격히 빠르게 진화하고 있다. 뇨검사는 일반인이 요시험지인 딥스틱을 이용하여 시각적인 색비교를 통해 자가진단이 가능하기에 색인지 기술에 대한 연구가 많이 이루어지고 있다. 본 연구는 요시험지검사에 사용되는 딥스틱(Dipstick Pad)의 색 변화를 스마트폰 카메라를 이용하여 얻어진 이미지로부터 RGB 값을 분석하였다. 비교 대상은 가장 많이 검사하는 질환으로 당뇨 증상과 관련 있는 요소인 당, 케톤체, pH의 양적변화에 대한 뇨 색 변화를 관찰하였다. 본 실험에서 일반적인 조도의 영향을 기준으로 밝은 조도와 어두운 조도 조건에서 뇨시험지상의 색변화에서 추출된 RGB값의 변화를 관찰하였다. 결과적으로 밝은 조도 조건에서의 색상 추출값이 높고 농도에 따른 반영이 잘 이루어지는 반면 낮은 조도 영역에는 색상 추출값이 낮게 나타났다. 따라서, 색 분리 알고리즘의 문제점을 개선하고자 RGB 수치의 변화값을 제시하였다.

Keywords

Acknowledgement

The research was supported by the Research Institute R&DB Program through the Ministry of Science and ICT. (2020-DG-RD-0006)

References

  1. H. Jeong, B. Tombor, R. Albert, Z. N. Oltvai & A.-L. Barabasi. (2000). The large-scale organization of metabolic networks. Nature, 407(6804), 651-654. DOI : 10.1038/35036627
  2. M. Bhatia, S. Kaur & Sandeep K. Sood. (2020). IoT-inspired smart home based urine infection prediction. Journal of Ambient Intelligence and Humanized Computing. DOI : 10.1007/s12652-020-01952-w
  3. S. Decramer et al. (2008). Urine in Clinical Proteomics. Molecular & Cellular Proteomics, 7(10), 1850-1862. DOI : 10.1074/mcp.R800001-MCP200
  4. L. Mackay, M. J. Lyall, S. Delaney, J. A. McKnight, & M. W. J. Strachan. (2010). Are blood ketones a better predictor than urine ketones of acid base balance in diabetic ketoacidosis?. Practical Diabetes International, 27(9), 396-399. DOI : 10.1002/pdi.1529
  5. S. Decramer et al. (2008). Urine in Clinical Proteomics. Molecular & Cellular Proteomics, 7(10), 1850-1862. DOI : 10.1074/mcp.R800001-MCP200
  6. S. Booth, C. Baleriola & William D. Rawlinson. (2006). Comparison of two rapid influenza A/B test kits with reference methods showing high specificity and sensitivity for influenza A infection. J Med Virol, 78(5), 619-622. DOI : 10.1002/jmv.20584
  7. D. Ryan, K. Robards, P.D. Prenzler & M. Kendall. (2011). Recent and potential developments in the analysis of urine: A review. Analytica Chimica Acta, 684(1-2), 17-29. DOI : 10.1016/j.aca.2010.10.035
  8. R. Khasriya et al. (2010). The Inadequacy of Urinary Dipstick and Microscopy as Surrogate Markers of Urinary Tract Infection in Urological Outpatients With Lower Urinary Tract Symptoms Without Acute Frequency and Dysuria. Journal of Urology, 183(5), 1843-1847. DOI : 10.1016/j.juro.2010.01.008
  9. H. G. Pohl et al. (2020). The Urine Microbiome of Healthy Men and Women Differs by Urine Collection Method. Int Neurourol J, 24(1), 41-51. DOI : 10.5213/inj.1938244.122
  10. V. Kavuru, T. Vu, L. Karageorge, D. Choudhury, R. Senger & J. Robertson. (2019). Dipstick analysis of urine chemistry: benefits and limitations of dry chemistry-based assays. Postgraduate Medicine, 225-233. DOI : 10.1080/00325481.2019.1679540
  11. K. E. Maduemem, Y. D. Rodriguez & B. Fraser. (2019). How Sensitive are Dipstick Urinalysis and Microscopy in Making Diagnosis of Urinary Tract Infection in Children?. Int J Prev Med, 10(1), 62. DOI : 10.4103/ijpvm.IJPVM_353_17
  12. C. D. Chin, V. Linder & S. K. Sia. (2006). Lab-on-a-chip devices for global health: Past studies and future opportunities. The Royal Society of Chemistry, 7(1), 41-57. DOI : 10.1039/b611455e
  13. C. C. Blyth, J. R. Iredell & D. E. Dwyer. (2009). Rapid-Test Sensitivity for Novel Swine-Origin Influenza A (H1N1) Virus in Humans. New England Journal of Medicine, 361(25), 2493-2493. DOI : 10.1056/NEJMc0909049
  14. J. Wang et al. (2017). Dipstick proteinuria and risk of myocardial infarction and all-cause mortality in diabetes or pre-diabetes: a population-based cohort study. Scientific Reports, 7(1), 11986. DOI : 10.1038/s41598-017-12057-4
  15. S. Misra & N. S. Oliver. (2014). Utility of ketone measurement in the prevention, diagnosis and management of diabetic ketoacidosis. Diabetic Medicine, 32(1), 14-23. DOI : 10.1111/dme.12604
  16. D. Ketan. (2016). Blood Ketones: Measurement, Interpretation, Limitations, and Utility in the Management of Diabetic Ketoacidosis. The Review of Diabetic Studies, 13(4), 217-225. DOI : 10.1900/rds.2016.13.217
  17. L. Laffel. (1999). Ketone bodies: a review of physiology, pathophysiology and application of monitoring to diabetes. diabetes metabolism research and reviews, 15(6), 412-426. DOI : 10.1002/(sici)1520-7560(199911/12)15:6<412::aid-dmr r72>3.0.co;2-8
  18. L. Mackay, MJ. Lyall, S. Delaney, JA. McKnight & MWJ. Strachan. (2010). Are blood ketones a better predictor than urine ketones of acid base balance in diabetic ketoacidosis?. Practical Diabetes International, 27(9), 396-399. DOI : 10.1002/pdi.1529
  19. S. Ahmed A & M. Ashraf A. (2020). Determination of acid dissociation constants of Alizarin Red S, Methyl Orange, Bromothymol Blue and Bromophenol Blue using a digital camera. RSC Advances, 10(19), 11311-11316. DOI : 10.1039/C9RA10568A
  20. J. S. Kim, C. G. Jin, S. K. Lee, S. G. Lee & C. U. Choi. (2011). Geometric Calibration and Accuracy Evaluation of Smartphone Camera. Journal of Korean Society for Geospatial Information Science, 19(3), 115-125.
  21. S. D. Kim, Y. M. Koo & Y. H. Yun. (2017). A Smartphone-Based Automatic Measurement Method for Colorimetric pH Detection Using a Color Adaptation Algorithm. Sensors, 17(7), 1604. DOI : 10.3390/s17071604
  22. S. Li, H. Joshua A & P. Ian. (2012). Point-of-care colorimetric detection with a smartphone. Lab on a Chip, 12(21), 4240-4243. DOI : 10.1039/c2lc40741h
  23. P.L. Reu, W. Sweatt, T. Miller & D. Fleming. (2015). Camera System Resolution and its Influence on DigitalImage Correlation. Experimental Mechanics 55(1), 9-25. DOI : 10.1007/s11340-014-9886-y
  24. J. I Park, H. W. Lee & Y. S. Kim. (2015). A Study on the Proper Position of Illumination Sensor for Dimming Lighting Control Based on Practical Life: Focusing on Summer Solstice and Winter Solstice. International Journal of Smart Home 9(9), 185-194. DOI : 10.14257/ijsh.2015.9.9.20
  25. K. Haakon & D. Tao. (2017). Smartphone-Based Rapid Screening of Urinary Biomarkers. IEEE Transactions on Biomedical Circuits and Systems, 11(2), 455-463. DOI : 10.1109/TBCAS.2016.2633508
  26. R. V. H. Ginardi, A. Saikhu, R. Sarno, D. Sunaryono, A. S. Kholimi & R. N. T. Shanty. (2014). Intelligent Method for Dipstick Urinalysis Using Smartphone Camera. Lecture Notes in Computer Science, 66-77. DOI : 10.1007/978-3-642-55032-4_7
  27. M. Ra, M. S. Muhammad, C. Lim, S. Han, C. Jung & W. Y. Kim. (2018). Smartphone-Based Point-of-Care Urinalysis Under Variable Illumination. IEEE Journal of Translational Engineering in Health and Medicine, 6, 1-11. DOI : 10.1109/JTEHM.2017.2765631