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.
Acknowledgments
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.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/cclm-2025-1202).
© 2025 Walter de Gruyter GmbH, Berlin/Boston
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