Deep Learning‐Assisted Classification of Urinary Red Blood Cell Morphology for Glomerular Hematuria Screening: A Pilot Study
Yih‐Lon Lin, Jung‐Sheng Chen, Ya‐Fan Chuang, Siang‐Ru Huang, Chien‐Sen LiaoABSTRACT
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.