Akıllı Telefon Tabanlı Evde İdrar Analizi Vivoo Uygulaması: Validasyon Çalışması

Year 2024, Volume: 7 Issue: 1, 60 - 73, 15.04.2024

Balım Bengisu Caf Gizem Çebi Haluk Çelik Aliasghar Noroozi Ali Atasever Miray Tayfun

https://doi.org/10.38001/ijlsb.1371191

Abstract

Hasta başı tanı (POC) analizi, hızlı ve uygun tıbbi teşhis sağlamada önemli bir yaklaşım olarak ortaya çıkmıştır. Akıllı telefon tabanlı çözümler, POC'nin erişilebilirliğini ve kolaylığını daha da artırarak laboratuvardan bağımsız olarak verimli analiz yapılmasını sağlar. Akıllı telefon teknolojisinin POC ile entegrasyonu, kullanıcılara çeşitli sağlık parametrelerini rahatlıkla izleme olanağı sağlayan Vivoo mobil uygulaması gibi yenilikçi uygulamaların önünü açmaktadır. Çalışmamız akıllı telefon tabanlı POC idrar tahlili Vivoo mobil uygulamasının doğruluğunu ve güvenilirliğini doğrulamaktadır. Yapay idrar örneklerinin hem Vivoo hem de geleneksel laboratuvar yöntemleri kullanılarak analiz edildiği karşılaştırmalı bir yaklaşım takip edildi. Çok çeşitli sağlık parametreleri değerlendirildi. Bu çalışma boyunca toplam 2618 test şeriti kullanıarak Vivoo'nun doğruluğu değerlendirildi. Test şeritleri sonuçlarının beklenen ölçüm sonuçlarıyla tam olarak eşleştiği görüldü. Ayrıca ±1 renk bloğu kabul kriteri uygulandığında test edilen şeritlere ait 2618 ölçümün 2608'inin beklenen ölçüm sonuçlarını tam olarak karşıladığı görüldü. Sonuçlara göre Vivoo'nun tam eşleşme anlaşma oranı için %95 güven aralığı %87,55 ± %1.27 ve %99.62 ± %0.24 olarak belirlendi. Bir sağlıklı yaşam ürünü olarak, bu çalışma Vivoo'nun hem cihaz güvenilirliği hem de performans açısından uygun olduğu sonucuna varılmaktadır.

Keywords

Hasta başı tanı, İdrar analizi, Yapay zeka, Sağlık, Sağlık Hizmeti

Smartphone-Based Point-of-Care Urinalysis Vivoo App: A Validation Study

Abstract

Point-of-care (POC) analysis has emerged as a pivotal approach in providing rapid and convenient medical diagnostics. Smartphone-based solutions further augment the accessibility and ease of POC, enabling efficient on-the-go analysis. The integration of smartphone technology with POC has paved the way for innovative applications such as the Vivoo App, which empowers users to monitor various health parameters conveniently. Our study validated the accuracy and reliability of the smartphone-based POC urinalysis Vivoo mobile application. A comparative approach was followed wherein artificial urine samples were analyzed using both the Vivoo and traditional laboratory methods. A diverse range of health parameters were assessed. A total of 2618 strips were used over the course of this study to evalate the accuracy of Vivoo. The test strips results appeared to match exactly the expected measurement results. In addition, when the ±1 color block acceptance criterion was applied, 2608 of 2618 measurements of the tested strips were found to have met the expected measurement results completely. Based on the results, the 95% confidence interval for the exact match agreement proportion of Vivoo is 87.55% ± 1.27% and 99.62% ± 0.24%. As a wellness product, this study thus concludes that the Vivoo is appropriate in terms of both device reliability and performance. The app's ability to provide accurate and timely health results offers promising opportunities to improve individual health management.

Keywords

Point of care, Urinalysis, Articicial intelligence, Wellness, Health

References

• Liu, D., et al., Trends in miniaturized biosensors for point-of-care testing. TrAC Trends in Analytical Chemistry, 2020. 122, 115701. https://doi.org/10.1016/j.trac.2019.115701.
• Hou, Y., et al., Recent advances and applications in paper-based devices for point-of-care testing. Journal of Analysis and Testing, 2022. 6(3), 247-273. https://doi.org/10.1007/s41664-021-00204-w.
• Trenti, T., Synergy between point-of-care testing and laboratory consolidations. The electronic Journal of the International Federation of Clinical Chemistry and Laboratory Medicine, 2021. 32(3):328.
• Lei, R., Huo, R., Mohan, C., Current and emerging trends in point-of-care urinalysis tests. Expert review of molecular diagnostics, 2020. 20(1):69-84. https://doi.org/10.1080/14737159.2020.1699063.
• Mahoney, E., et al., Point-of-care urinalysis with emerging sensing and imaging technologies. Journal of The Electrochemical Society, 2019. 167(3), 037518. https://doi.org/10.1149/2.0182003JES.
• Echeverry, G., Hortin, G.L., Rai, A.J., Introduction to urinalysis: historical perspectives and clinical application. Methods Molecular Biology, 2010. 641:1-12. https://doi.org/10.1007/978-1-60761-711-2_1.
• Boon, H. A., et al., Point-of-care tests for pediatric urinary tract infections in general practice: a diagnostic accuracy study. Family Practice, 2022. 39(4), 616-622. https://doi.org/10.1093/fampra/cmab118.
• Zhang, Z., et al., Urine analysis has a very broad prospect in the future. Frontiers in Analytical Science, 2022. 1, 812301. https://doi.org/10.3389/frans.2021.812301.
• Kavuru, V., et al., Dipstick analysis of urine chemistry: benefits and limitations of dry chemistry-based assays. Postgraduate Medicine, 2020. 132(3), 225-233. https://doi.org/10.1080/00325481.2019.1679540.
• Rysz, J., et al., Novel biomarkers in the diagnosis of chronic kidney disease and the prediction of its outcome. International journal of molecular sciences, 2017. 18(8), 1702. https://doi.org/10.3390/ijms18081702.
• Kutter, D., The urine test strip of the future. Clinica chimica acta, 2000. 297(1-2):297-304. https://doi.org/10.1016/s0009-8981(00)00255-2.
• Lepowsky, E., et al., Paper-based assays for urine analysis. Biomicrofluidics, 2017. 11(5):051501. https://doi.org/10.1063%2F1.4996768.
• Rink, S., & Baeumner, A.J., Progression of paper-based point-of-care testing toward being an indispensable diagnostic tool in future healthcare. Analytical Chemistry, 2023, 95(3), 1785-1793. https://doi.org/10.1021/acs.analchem.2c04442.
• Ventola, C.L., Mobile devices and apps for health care professionals: uses and benefits. Pharmacy and Therapeutics, 2014. 39(5):356.
• Baxter, C., et al., Assessment of mobile health apps using built-in smartphone sensors for diagnosis and treatment: systematic survey of apps listed in international curated health app libraries. JMIR mHealth and uHealth. 2020;8(2):e16741. https://doi.org/10.2196/16741.