ArticleAnnals of laboratory medicine2026
Machine Learning-Based Analysis of UF-5000 Scattergrams Improves the Identification of Urinary Dysmorphic Red Blood Cells.
Article in Annals of laboratory medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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9 authors.
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Abstract
Background: Identifying dysmorphic red blood cells (RBCs) is critical for diagnosing glomerular diseases, as distinguishing glomerular from non-glomerular hematuria may reduce reliance on invasive diagnostics such as kidney biopsy. We aimed to enhance urinary RBC morphological classification by employing machine learning (ML) to analyze UF-5000 scattergram data. Methods: RBCs in urine samples (N=185) were classified as dysmorphic or isomorphic based on microscopic findings. UF-5000 scattergrams were quantified to generate 20 statistical features and used to train a ML model in DataRobot (v9.1) with an automated pipeline, five-fold cross-validation, and LogLoss-based selection. Performance was evaluated in an independent cohort (N=1,093). Accuracy was defined as concordance with microscopy findings. Areas under ROC curves (AUROCs) and diagnostic metrics are reported with 95% confidence intervals (CIs). Results: Among conventional UF-5000 parameters, the small RBC/total RBC ratio was the strongest predictor (AUROC 0.97, 95% CI 0.94-0.99). Scattergram-derived features indicated that RBC size-related parameters were crucial for identifying dysmorphic RBCs. The ML model alone demonstrated superior accuracy over UF-5000 RBC-Info alone (concordance 95.2% vs. 92.1%; AUROC 0.95 [0.94-0.97] vs. 0.92 [0.91-0.94]). Logical (OR/AND) combinations of the ML model with RBC-Info outperformed RBC-Info alone (OR: concordance 92.7%, AUROC 0.93 [0.92-0.95]; AND: concordance 94.6%, AUROC 0.94 [0.93-0.96]). Conclusions: A scattergram-based ML model improves the accuracy and reliability of urinary RBC morphological classification based on UF-5000 scattergrams and may help reduce reliance on invasive diagnostics. Prospective, multicenter studies should validate generalizability and assess integration into routine workflows.
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