Analytical and diagnostic evaluation of the Anvajo fluidlab 2 analyzer: a novel urine particle analyzer for clinical application using digital holographic microscopy?
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Sigrid Deprez
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Matthijs Oyaert
Abstract
Objectives
The performance of a novel urine particle analyzer, fluidlab 2 (Anvajo GmbH, Dresden, Germany), was evaluated against phase-contrast visual microscopy according to the most recent EFLM European Urinalysis Guideline.
Methods
The fluidlab 2 device combines digital holographic microscopy with neural network-based object detection for particle classification. Its compact benchtop design is suitable for bedside use, reducing turnaround times. The analytical performance (imprecision, linearity, LoQ) was evaluated according to the 2023 EFLM Urinalysis Guideline. Method comparison involved the analysis of 450 urine samples, assessing RBC, WBC, and SEC counts against visual microscopy using Passing-Bablok regression and Spearman’s correlation. Bland-Altman plots were used to evaluate the agreement with clinical performance standards, while weighted Cohen’s kappa was used to measure diagnostic agreement on an ordinal scale.
Results
By applying Dahlberg’s procedure, a desirable relative coefficient of variation R(CV) ≤2.0 was obtained for RBC and WBC. Linearity of up to 7 × 106/L and 6 × 106/L was achieved. The estimated LoQ at CV=30 % reached 20 × 106/L for RBC and 5 × 106/L for WBC. Spearman’s correlation coefficient against visual microscopy was 0.86, 0.92 and 0.94 for RBC, WBC and SEC, respectively. Agreement with visual microscopy (Cohen’s weighted kappa) was 0.92 for RBC, 0.93 for WBC, 0.96 for SEC, 0.86 for casts, 0.82 for non-SEC, 0.33 for crystals and 0.51 for bacterial counts.
Conclusions
Fluidlab 2 provides desirable imprecision for RBC and WBC, and meets the criteria for linearity and LoQ. Cohen’s weighted kappa coefficients show an optimal comparison to visual microscopy for RBC, WBC and SEC and a minimum comparison for casts and non-SEC. This evaluation demonstrated promising results for the use of the fluidlab 2 analyzer in a clinical setting to detect kidney-related diseases based on urine particle analysis.
Acknowledgment
The authors wish to thank the lab technicians of the Ghent University Hospital for their assistance in the study. We thank ANVAJO for providing the reagents for this evaluation.
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Research ethics: The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013) and approved by the Ethical Committee of the Ghent University Hospital (ONZ-2024-0086).
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: The data that support the findings of this study are available from the corresponding author, MO, upon reasonable request.
References
1. Kouri, TT, Hofmann, W, Falbo, R, Oyaert, M, Schubert, S, Gertsen, JB, et al.. The EFLM european urinalysis guideline 2023. Clin Chem Lab Med 2024;62:1653–786. https://doi.org/10.1515/cclm-2024-0070.Suche in Google Scholar PubMed
2. Oyaert, M, Delanghe, J. Progress in automated urinalysis. Ann Lab Med 2018;39:15–22. https://doi.org/10.3343/alm.2019.39.1.15.Suche in Google Scholar PubMed PubMed Central
3. Fogazzi, GB, Garigali, G. The different ways to obtain digital images of urine microscopy findings: their advantages and limitations. Clin Chim Acta 2017;466:160–1. https://doi.org/10.1016/j.cca.2017.01.024.Suche in Google Scholar PubMed
4. Perazella, MA. The urine sediment as a biomarker of kidney disease. Am J Kidney Dis 2015;66:748–55. https://doi.org/10.1053/j.ajkd.2015.02.342.Suche in Google Scholar PubMed
5. Gadeholt, H. Quantitative estimation of urinary sediment, with special regard to sources of error. Br Med J 1964;1:1547–9. https://doi.org/10.1136/bmj.1.5397.1547.Suche in Google Scholar PubMed PubMed Central
6. Oyaert, M, Kouri, T, Carton, E, Deprez, S, Lambrecht, S, Speeckaert, M. Evaluation of AUTION EYE AI-4510 flow cell morphology analyzer for counting particles in urine. Clin Chem Lab Med 2025;63:1179–89. https://doi.org/10.1515/cclm-2024-1163.Suche in Google Scholar PubMed
7. Kim, H, Kim, YO, Kim, Y, Suh, JS, Cho, EJ, Lee, HK. Small red blood cell fraction on the uf-1000i urine analyzer as a screening tool to detect dysmorphic red blood cells for diagnosing glomerulonephritis. Ann Lab Med 2019;39:277–83. https://doi.org/10.3343/alm.2019.39.3.271.Suche in Google Scholar PubMed PubMed Central
8. Delanghe, JR, Kouri, TT, Huber, AR, Hannemann-Pohl, K, Guder, WG, Lun, A, et al.. The role of automated urine particle flow cytometry in clinical practice. Clin Chim Acta 2000;301:1–18. https://doi.org/10.1016/s0009-8981(00)00342-9.Suche in Google Scholar PubMed
9. Becker, GJ, Garigali, G, Fogazzi, GB. Advances in urine microscopy. Am J Kidney Dis 2016;67:954–64. https://doi.org/10.1053/j.ajkd.2015.11.011.Suche in Google Scholar PubMed
10. Aper, SJA, Gijzen, K, Luimstra, JJ, van der Valk, JTMH, Russcher, A, Koçer, RG, et al.. Evaluation of the Atellica® UAS 800: a new member of the automated urine sediment analyzer family. Scand J Clin Lab Invest 2021;81:585–92. https://doi.org/10.1080/00365513.2021.1986856.Suche in Google Scholar PubMed
11. Delanghe, J. New screening diagnostic techniques in urinalysis. Acta Clin Belg 2007;62:155–61. https://doi.org/10.1179/acb.2007.026.Suche in Google Scholar PubMed
12. Hannemann-Pohl, K, Kampf, SC. Automation of urine sediment examination: a comparison of the sysmex UF-100 automated flow-cytometer with routine manual diagnosis (microscopy, test strips, and bacterial culture). Clin Chem Lab Med 1999;37:753–64. https://doi.org/10.1515/cclm.1999.116.Suche in Google Scholar PubMed
13. Manoni, F, Tinello, A, Fornasiero, L, Hoffer, P, Temporin, V, Valverde, S, et al.. Urine particle evaluation: a comparison between the UF-1000i and quantitative microscopy. Clin Chem Lab Med 2010;48:1107–11. https://doi.org/10.1515/cclm.2010.233.Suche in Google Scholar PubMed
14. Previtali, G, Ravasio, R, Seghezzi, M, Buoro, S, Alessio, MG. Performance evaluation of the new fully automated urine particle analyser UF-5000 compared to the reference method of the fuchs-rosenthal chamber. Clin Chim Acta 2017;472:123–30. https://doi.org/10.1016/j.cca.2017.07.028.Suche in Google Scholar PubMed
15. Mizuno, G, Hoshi, M, Nakamoto, K, Sakurai, M, Nagashima, K, Fujita, T, et al.. Evaluation of red blood cell parameters provided by the UF-5000 urine auto analyser in patients with glomerulonephritis. Clin Chem Lab Med 2021;59:1547–53. https://doi.org/10.1515/cclm-2021-0287.Suche in Google Scholar PubMed
16. Seigner, S, Bogedale, K, Dorsch, R, Zablotski, Y, Weber, K. Comparison of the anvajo vet fluidlab 1 urine sediment analyzer to manual microscopy and idexx SediVue analysis for analysis of urine samples from cats and dogs. J Vet Diagn Invest 2022;34:944–54. https://doi.org/10.1177/10406387221124157.Suche in Google Scholar PubMed PubMed Central
17. Bourner, G, De la Salle, B, George, T, Tabe, Y, Baum, H, Culp, N, et al.. ICSH guidelines for the verification and performance of automated cell counters for body fluids. Int J Lab Hematol 2014;36:598–612. https://doi.org/10.1111/ijlh.12196.Suche in Google Scholar PubMed
18. Dahlberg, G. Statistical methods for medical and biological students. London: Allen and Unwin Ltd; 1948.Suche in Google Scholar
19. Kouri, T, Alagrund, K, Lehtonen, M, Tohmola, N, Pihlajamaa, T, Kouri, VP, et al.. Verification of UriSed 3 PRO automated urine microscope in regional laboratory environment. Clin Chim Acta 2021;515:96–103. https://doi.org/10.1016/j.cca.2021.01.005.Suche in Google Scholar PubMed
20. Linko, S, Kouri, TT, Toivonen, E, Ranta, PH, Chapoulaud, E, Lalla, M. Analytical performance of the iris iQ200 automated urine microscopy analyzer. Clin Chim Acta 2006;372:54–64. https://doi.org/10.1016/j.cca.2006.03.015.Suche in Google Scholar PubMed
21. Barocas, DA, Boorjian, SA, Alvarez, RD, Downs, TM, Gross, CP, Hamilton, BD, et al.. Microhematuria: AUA/SUFU guideline. J Urol 2020;204:778–86. https://doi.org/10.1097/ju.0000000000001297.Suche in Google Scholar
22. Monsen, T, Ryden, P. A new concept and a comprehensive evaluation of SYSMEX UF-1000i flow cytometer to identify culture-negative urine specimens in patients with UTI. Eur J Clin Microbiol Infect Dis 2017;36:1691–703. https://doi.org/10.1007/s10096-017-2964-1.Suche in Google Scholar PubMed PubMed Central
23. Palmieri, R, Falbo, R, Cappellini, F, Soldi, C, Limonta, G, Brambilla, P. The development of autoverification rules applied to urinalysis performed on the AutionMAX-SediMAX platform. Clin Chim Acta 2018;485:275–81. https://doi.org/10.1016/j.cca.2018.07.001.Suche in Google Scholar PubMed
24. Perazella, MA, Coca, SG. Traditional urinary biomarkers in the assessment of hospital-acquired AKI. Clin J Am Soc Nephrol 2012;7:167–74. https://doi.org/10.2215/CJN.09490911.Suche in Google Scholar PubMed
25. Oyaert, M, Delanghe, J, Brouwers, A, Bové, T, Schaubroeck, H, Delrue, C, et al.. Renal tubular epithelial cells as an easily accessible biomarker for diagnosing AKI post cardiac surgery. Intensive Care Med 2025;51:870–82. https://doi.org/10.1007/s00134-025-07909-x.Suche in Google Scholar PubMed
26. Oyaert, M, Speeckaert, M, Boelens, J, Delanghe, JR. Renal tubular epithelial cells add value in the diagnosis of upper urinary tract pathology. Clin Chem Lab Med 2020;58:597–604. https://doi.org/10.1515/cclm-2019-1068.Suche in Google Scholar PubMed
27. Daudon, M. Crystalluria. Néphrol Thérapeutique 2015;11:174–90. https://doi.org/10.1016/j.nephro.2015.03.003.Suche in Google Scholar PubMed
28. Wong, KA, Pardy, C, Pillay, S, Athanasiou, T, Rottenberg, G, Bultitude, M, et al.. Can the presence of crystalluria predict stone formation in patients with cystinuria? J Endourol 2016;30:609–14. https://doi.org/10.1089/end.2015.0692.Suche in Google Scholar PubMed
29. Šálek, T, Musil, P, Vermeersch, P, Marrington, R, Dikmen, ZG, Poláchová, R, et al.. Preservation of urine specimens for metabolic evaluation of recurrent urinary stone formers. Clin Chem Lab Med 2025;63:129–38. https://doi.org/10.1515/cclm-2024-0773.Suche in Google Scholar PubMed
30. Skolarikos, A, Geraghty, R, Somani, B, Tailly, T, Jung, H, Neisius, A, et al.. European association of urology guidelines on the diagnosis and treatment of urolithiasis. Eur Urol 2025;88:64–75. https://doi.org/10.1016/j.eururo.2025.03.011.Suche in Google Scholar PubMed
31. Fogazzi, G, Garigali, C, Croci, M, Verdesca, S. The formed elements of the urinary sediment. In: Fogazzi, G, editor. The urinary sediment an integrated view, 3 ed. Milan: Elsevier; 2010:41–158 pp.Suche in Google Scholar
32. Poloni, JAT, Garcia, CD, Rotta, LN, Perazella, MA. Calcium oxalate crystalluria points to primary hyperoxaluria type 1. Kidney Int 2016;89:250. https://doi.org/10.1016/j.kint.2015.11.001.Suche in Google Scholar PubMed
33. Luqman, A, Stanifer, J, Asif Siddiqui, OM, Naseer, A, Wall, BM. Calcium oxalate monohydrate crystals: a clue to ethylene glycol poisoning. Am J Med Sci 2011;341:338. https://doi.org/10.1097/maj.0b013e3181e15dbd.Suche in Google Scholar
34. Castiglione, V, Cavalier, E, Diop, C, Gadisseur, R. Distinction between urine crystals by automated urine analyser sediMAX conTRUST is specific but lacks sensitivity. Clin Chem Lab Med 2017;55:e288–90. https://doi.org/10.1515/cclm-2017-0228.Suche in Google Scholar PubMed
Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/cclm-2025-1202).
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