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Enhancing urinalysis with smartphone and AI: a comprehensive review of point-of-care urinalysis and nutritional advice

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Abstract

Point-of-care diagnostics (POC), including urine test strips, offer several advantages over traditional laboratory-based urine analysis. POC allows us to regulate our nutritional needs. Urine analysis is a common diagnostic and well-being monitoring tool used to evaluate the overall health of an individual. It involves the examination of a sample of urine to detect and measure various substances and markers found in urine, such as proteins, glucose, leukocytes, ketones, and bilirubin, among others. Urine test strips, also known as dipstick tests, are a quick and convenient method of urine analysis that can be performed in conjunction with smartphone and AI-based analyses. These tests use a small strip of paper with chemically-treated reagents that change color when they react with specific substances found in urine. Dietary intake can have a significant impact on the composition of urine, as certain nutrients and compounds are metabolized and excreted through the kidneys. Understanding the effects of dietary intake on urine biomarkers can provide valuable insight into the overall health and nutritional status of individuals. This review explores existing literature to highlight the intersection between strip-based urine analysis, smartphone-based analysis, gold standards, and recent developments in urine analysis.

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Authors and Affiliations

  1. Vivosens, Inc., 44 Tehama Street, Suite 409, San Francisco, CA, 94105, United States

    Haluk Çelik, Balım Bengisu Caf, Gizem Çebi & Miray Tayfun

  2. Program of Stem Cell and Tissue Engineering, Institute of Graduate Education, Istinye University, 34010, Zeytinburnu, Istanbul, Turkey

    Haluk Çelik

  3. Program of Bioengineering, Graduate School of Science and Engineering, Yıldız Technical University, 34220, Esenler, Istanbul, Turkey

    Balım Bengisu Caf

  4. Faculty of Medicine, Medical Biochemistry, Istinye University, 34010, Zeytinburnu, Istanbul, Turkey

    Caner Geyik

  5. Program of Chemical Engineering, Institute of Graduate School, Istanbul Technical University, ITU Ayazaga Kampusu, 34469, Maslak, Istanbul, Turkey

    Gizem Çebi

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HC contributed to Conceptualization, writing—original draft preparation; BBC, CG, GÇ, MT contributed to review and editing. All authors contributed to the final version of the manuscript.

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Çelik, H., Caf, B.B., Geyik, C. et al. Enhancing urinalysis with smartphone and AI: a comprehensive review of point-of-care urinalysis and nutritional advice. Chem. Pap. 78, 651–664 (2024). https://doi.org/10.1007/s11696-023-03137-z

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