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Updated: May 21, 2025

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ML-UrineQuant: A machine learning program for identifying and quantifying mouse urine on absorbent paper.

Warren G Hill1, Bryce MacIver1, Gary A Churchill2

  • 1Laboratory of Voiding Dysfunction, Division of Nephrology, Department of Medicine, Beth Israel Deaconess Medical Center & Harvard Medical School, Boston, Massachusetts, USA.

Physiological Reports
|March 19, 2025
PubMed
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This summary is machine-generated.

A new machine learning algorithm, ML-UrineQuant, accurately quantitates mouse urine spots from void spot assays. This tool overcomes image variability, improving lower urinary tract research for scientists.

Area of Science:

  • Urology
  • Computational Biology
  • Animal Models

Background:

  • The void spot assay is a common method for evaluating bladder function in mice.
  • Current software analysis of urine spot patterns faces challenges due to image quality variations and artifacts.

Purpose of the Study:

  • To develop a robust machine learning algorithm for accurate urine spot quantification in mouse void spot assays.
  • To address limitations of existing software in handling diverse image characteristics.

Main Methods:

  • Development of a machine learning algorithm, ML-UrineQuant, utilizing Region-based Convolutional Neural Networks (Mask-RCNN).
  • Training the algorithm for object recognition to detect and quantify urine spots of varying sizes.
  • Testing the model across a range of illumination and contrast settings.
Keywords:
VSAVSOPartificial intelligencemicemicturitionpythonurologyvoid spot on papervoiding dysfunction

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Main Results:

  • ML-UrineQuant demonstrated high accuracy in identifying urine spots under varied imaging conditions.
  • The algorithm effectively quantitates urine spots across a broad size spectrum.
  • The model's performance was robust despite differences in image contrast and resolution.

Conclusions:

  • ML-UrineQuant offers a significant advancement over current methods for analyzing void spot assays.
  • The algorithm's adaptability allows for fine-tuning to specific laboratory image characteristics.
  • This tool is expected to be valuable for lower urinary tract research utilizing mouse models.