Comparative evaluation of light microscopy, phase-contrast microscopy, and an automated analyzer in the detection of dysmorphic red blood cells in hematuria

Objectives: Urinalysis remains a fundamental diagnostic tool in the evaluation of renal and urological disorders. Differentiation between glomerular and non-glomerular hematuria based on red blood cell (RBC) morphology is critical for clinical decision-making. While phase-contrast microscopy (PCM) is considered the reference standard for identifying dysmorphic RBCs (dRBCs), its limited availability and operator dependency necessitate assessment of alternative methods. This study aimed to compare light microscopy (LM), PCM, and the DIRUI FUS-1000 automated urine analyzer for detecting dRBCs and identifying glomerular hematuria. Materials and Methods: This cross-sectional study included 150 patients presenting with hematuria. Fresh urine samples from each patient were examined independently using LM, PCM, and the DIRUI FUS-1000 automated urine analyzer. The origin of hematuria (glomerular or non-glomerular) was determined using a composite reference standard incorporating clinical findings, laboratory investigations, imaging studies, and histopathological data where available. Statistical analysis: Diagnostic performance parameters, including sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy, were calculated for each modality. Receiver operating characteristic (ROC) curve analysis was performed to assess diagnostic discrimination and determine optimal cut-off values for dRBC percentages. Statistical analysis was conducted using the Statistical Package for the Social Sciences version 22.0. Results: Of the 150 cases analyzed, 102 (68%) were classified as having glomerular hematuria and 48 (32%) as non-glomerular hematuria. PCM demonstrated the highest diagnostic accuracy, with a sensitivity of 100%, specificity of 75%, and an area under the ROC curve (AUC) of 0.97 at a cut-off value of 21% dRBCs. LM showed moderate diagnostic performance (sensitivity 77.4%, specificity 56.3%, and AUC 0.76). The automated urine analyzer demonstrated comparable sensitivity to LM but lower specificity, with an AUC of 0.72. Conclusions: PCM remains the reference standard for identifying dRBCs and diagnosing glomerular hematuria. However, LM and automated urine analyzers serve as practical screening tools, particularly in high-volume laboratory settings. Emerging artificial intelligence-assisted digital urine microscopy platforms hold promise for improving reproducibility, diagnostic accuracy, and workflow efficiency in routine urinalysis.