2025/03/01 by Warren G. Hill, Bryce MacIver, Gary A. Churchill +3 · 1 voice
Medicine · Biochemistry, Genetics and Molecular Biology · #Urinary Bladder and Prostate Research #Urinary Tract Infections Management #Metabolomics and Mass Spectrometry Studies
paper · pdf · doi:10.14814/phy2.70243
openalex publication_date 2025/03/01 · openalex created_date 2025/03/20 · openalex updated_date 2026/08/02
The void spot assay has gained popularity as a way of assessing functional bladder voiding parameters in mice, but analyzing the size and distribution of urine spot patterns on filter paper with software remains problematic due to inter-laboratory differences in image contrast and resolution quality and non-void artifacts. We have developed a machine learning algorithm based on Region-based Convolutional Neural Networks (Mask-RCNN) that was trained in object recognition to detect and quantitate urine spots across a broad range of sizes-ML-UrineQuant. The model proved extremely accurate at identifying urine spots in a wide variety of illumination and contrast settings. The overwhelming advantage it offers over current algorithms will be to allow individual labs to fine-tune the model on their specific images regardless of the image characteristics. This should be a valuable tool for anyone performing lower urinary tract research using mouse models.