Ann Lab Med.  2025 Mar;45(2):178-184. 10.3343/alm.2024.0304.

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

• Kim HJ
• Kim M
• Zhang H
• Kim HR
• Jeon JW
• Seo Y
• Choi Q

• KMID: 2565649
• DOI: http://doi.org/10.3343/alm.2024.0304

Abstract

Background
Urinalysis, an essential diagnostic tool, faces challenges in terms of standardization and accuracy. The use of artificial intelligence (AI) with mobile technology can potentially solve these challenges. Therefore, we investigated the effectiveness and accuracy of an AI-based program in automatically interpreting urine test strips using mobile phone cameras, an approach that may revolutionize point-of-care testing.
Methods
We developed novel urine test strips and an AI algorithm for image capture. Sample images from the Chungnam National University Sejong Hospital were collected to train a k-nearest neighbor classification algorithm to read the strips. A mobile application was developed for image capturing and processing. We assessed the accuracy, sensitivity, specificity, and ROC area under the curve for 10 parameters.
Results
In total, 2,612 urine test strip images were collected. The AI algorithm demonstrated 98.7% accuracy in detecting urinary nitrite and 97.3% accuracy in detecting urinary glucose. The sensitivity and specificity were high for most parameters. However, this system could not reliably determine the specific gravity. The optimal time for capturing the test strip results was 75 secs after dipping.
Conclusions
The AI-based program accurately interpreted urine test strips using smartphone cameras, offering an accessible and efficient method for urinalysis. This system can be used for immediate analysis and remote testing. Further research is warranted to refine test parameters such as specific gravity to enhance accuracy and reliability.

Keyword

Artificial intelligence; Mobile application; Smartphone; Urinalysis

Figure

• 

  Fig. 1 Urine test strip and reference chart

• 

  Fig. 2 Schematic representation of the experimental workflow used for AI-based urine test strip analysis. Abbreviations: RGB, red, green, blue; AI, artificial intelligence; ROC AUC, area under the ROC curve.

• 

  Fig. 3 Comparative analysis of the diagnostic test performance of AI-based urine test strip analysis with multiple datasets in terms of accuracy (A), sensitivity (B), specificity (C), and ROC AUC (D). Abbreviations: AI, artificial intelligence; blo, blood; leu, leukocytes; pro, protein; glu, glucose; ket, ketones; nit, nitrite; bil, bilirubin; uro, urobilinogen; sg, specific gravity; ROC AUC, area under the ROC curve.

• 

  Fig. 4 Time-based performance metrics of the artificial intelligence-based urine test strip analysis in terms of accuracy (A), sensitivity (B), specificity (C), and ROC AUC (D). Abbreviations: blo, blood; leu, leukocytes; pro, protein; glu, glucose; ket, ketones; nit, nitrite; bil, bilirubin; uro, urobilinogen; sg, specific gravity; mean_4, mean of the blo, glu, pro, and nit values; ROC AUC, area under the ROC curve.

Reference

References

1. Lei R, Huo R, Mohan C, Mohan C. 2020; Current and emerging trends in point-of-care urinalysis tests. Expert Rev Mol Diagn. 20:69–84. DOI: 10.1080/14737159.2020.1699063. PMID: 31795785. PMCID: PMC7365142.

2. Kavuru V, Vu T, Karageorge L, Choudhury D, Senger R, Robertson J. 2020; Dipstick analysis of urine chemistry: benefits and limitations of dry chemistry-based assays. Postgrad Med. 132:225–33. DOI: 10.1080/00325481.2019.1679540. PMID: 31609156.

3. Khan AI, Khan M, Khan R. 2023; Artificial intelligence in point-of-care testing. Ann Lab Med. 43:401–407. DOI: 10.3343/alm.2023.43.5.401. PMID: 37080740. PMCID: PMC10151281.

4. Coppens A, Speeckaert M, Delanghe J. 2010; The pre-analytical challenges of routine urinalysis. Acta Clin Belg. 65:182–9. DOI: 10.1179/acb.2010.038. PMID: 20669786.

5. De Bruyne S, De Kesel P, Oyaert M. 2023; Applications of artificial intelligence in urinalysis: is the future already here? Clin Chem. 69:1348–60. DOI: 10.1093/clinchem/hvad136. PMID: 37708293.

6. Oyaert M, Delanghe J. 2019; Progress in automated urinalysis. Ann Lab Med. 39:15–22. DOI: 10.3343/alm.2019.39.1.15. PMID: 30215225. PMCID: PMC6143458.

7. Stauss M, Dhaygude A, Ponnusamy A, Myers M, Woywodt A. 2021; Remote digital urinalysis with smartphone technology as part of remote management of glomerular disease during the SARS-CoV-2 virus pandemic: single-centre experience in 25 patients. Clin Kidney J. 15:903–11. DOI: 10.1093/ckj/sfab286. PMID: 35498887. PMCID: PMC9050594.

8. Nixon M, Outlaw F, Leung TS. 2020; Accurate device-independent colorimetric measurements using smartphones. PLoS One. 15:e0230561. DOI: 10.1371/journal.pone.0230561. PMID: 32214340. PMCID: PMC7098568. PMID: 56be6714eb144529a8b1bdad205ba1fe.

9. Hong JI, Chang BY. 2014; Development of the smartphone-based colorimetry for multi-analyte sensing arrays. Lab Chip. 14:1725–32. DOI: 10.1039/C3LC51451J. PMID: 24671456.

10. Noor Azhar M, Bustam A, Naseem FS, Shuin SS, Md Yusuf MH, Hishamudin NU, et al. 2023; Improving the reliability of smartphone-based urine colorimetry using a colour card calibration method. Digit Health. 9:20552076231154684. DOI: 10.1177/20552076231154684. PMID: 36798885. PMCID: PMC9926368. PMID: cab3dc4b0f7541f8aa7e66d4c26b2d97.

11. Flaucher M, Nissen M, Jaeger KM, Titzmann A, Pontones C, Huebner H, et al. 2022; Smartphone-based colorimetric analysis of urine test strips for at-home prenatal care. IEEE J Transl Eng Health Med. 10:2800109. DOI: 10.1109/JTEHM.2022.3179147. PMID: 35865751. PMCID: PMC9292338. PMID: c30016d7a84c4352a5f24cc17deac695.

12. Nixon M, Outlaw F, MacDonald LW, Leung TS. 2019; The importance of a device specific calibration for smartphone colorimetry. Proc IS&T 27th Color and Imaging Conf. 27:49–54. https://doi.org/10.2352/issn.2169-2629.2019.27.10. DOI: 10.2352/issn.2169-2629.2019.27.10.

13. Shao CH, Chen CL, Lin JY, Chen CJ, Fu SH, Chen YT, et al. 2017; Metabolite marker discovery for the detection of bladder cancer by comparative metabolomics. Oncotarget. 8:38802–10. DOI: 10.18632/oncotarget.16393. PMID: 28415579. PMCID: PMC5503573.

14. Sanghvi AB, Allen EZ, Callenberg KM, Pantanowitz L. 2019; Performance of an artificial intelligence algorithm for reporting urine cytopathology. Cancer Cytopathol. 127:658–66. DOI: 10.1002/cncy.22176. PMID: 31412169.

15. Eisner R, Greiner R, Tso V, Wang H, Fedorak RN. 2013; A machine-learned predictor of colonic polyps based on urinary metabolomics. Biomed Res Int. 2013:303982. DOI: 10.1155/2013/303982. PMID: 24307992. PMCID: PMC3838851.

16. Sarang P. 2023. K-nearest neighbors. Thinking data science. The Springer series in applied machine learning. Springer International Publishing;Cham: p. 131–41. DOI: 10.1007/978-3-031-02363-7_6.