Journal of clinical laboratory analysis
Deep Learning-Assisted Classification of Urinary Red Blood Cell Morphology for Glomerular Hematuria Screening: A Pilot Study
By Lin YL, Chen JS, Chuang YF and 2 others.
Abstract
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.
