Deep Learning-Assisted Classification of Urinary Red Blood Cell Morphology for Glomerular Hematuria Screening: A Pilot Study.

BACKGROUND: Distinguishing glomerular from non-glomerular hematuria remains challenging because urinary dysmorphic red blood cells (RBCs) are morphologically heterogeneous and affected by preanalytical and physicochemical factors. This pilot study developed a deep learning-assisted system for urinary RBC morphology classification and evaluated its feasibility for expert-guided glomerular hematuria screening support. METHODS: We retrospectively analyzed 491 high-resolution urine sediment images containing 15,779 annotated RBCs or RBC-like objects from a regional teaching hospital. RBCs were labeled as isomorphic, dysmorphic, or unknown according to established morphological criteria. A YOLOv5l model was trained for RBC detection and classification. Model outputs were integrated with an operational dysmorphic RBC-based scoring system and compared with manual expert assessment. RESULTS: The model achieved a precision of 0.84, recall of 0.69, and F1-score of 0.76 for dysmorphic RBC classification, with a recall of 0.98 for isomorphic RBCs. In sample-level scoring, concordance with expert assessment was 100% in the Negative category (39/39; 95% CI, 91.0%-100%), 81.8% in the Moderate category (9/11; 95% CI, 48.2%-97.7%), and 78.1% in the Major category (25/32; 95% CI, 60.0%-90.7%). No validation samples were available in the Few category. Mean computational inference time was 0.033 s per image. CONCLUSIONS: This YOLOv5l-based pilot study demonstrates the feasibility of rapid, morphology-aware urinary RBC classification and preliminary concordance with expert microscopy. The system may support expert-guided workflow research, but should not be interpreted as standalone diagnostic performance. Multicenter validation with independent clinical reference standards is required before clinical implementation.