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Sperm Structure and Semen Composition01:22

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During ejaculation, males release around 2-5 milliliters of semen, which is a complex mixture of mature sperm and various fluids produced by accessory glands. The mature sperm cells measure approximately 60 micrometers in length and consist of a head, neck, midpiece, and tail. The head is flattened and tapered, measuring about 4 to 5 micrometers in length. It contains a nucleus with condensed chromosomes and an acrosome, a cap-like structure filled with enzymes essential for penetrating the...
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An Artificial Intelligence-Based Computer Vision Model for Human Sperm Concentration, Motility, and Kinematics

Sahar Shahali1, David Mortimer2, Moira K O'Bryan3

  • 1Department of Mechanical and Aerospace Engineering Monash University Clayton Australia.

Smart Medicine
|January 14, 2026
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Summary
This summary is machine-generated.

An AI-powered tool offers accurate sperm analysis, improving male infertility diagnosis. This advanced computer vision system provides reliable sperm concentration and motility assessment, outperforming traditional methods.

Keywords:
andrologyartificial intelligencecomputer visionsemen analysissperm motility

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Area of Science:

  • Reproductive Biology
  • Medical Technology
  • Artificial Intelligence

Background:

  • Accurate sperm analysis is crucial for diagnosing and managing male infertility.
  • Current methods like manual counting and commercial CASA systems have limitations including labor intensity, human error, and inconsistent performance.

Purpose of the Study:

  • To develop and evaluate an AI-driven computer vision tool for high-resolution, quantitative analysis of sperm concentration and motility.
  • To benchmark the AI tool against manual tracking and a commercial CASA system.

Main Methods:

  • A prospective study analyzed 26 semen samples using an AI computer vision tool, manual tracking (Fiji software), and a commercial CASA system (Hamilton Thorne IVOS II).
  • The AI tool computed various sperm motility parameters (VSL, VCL, VAP, LIN, ALHmax, BCF) and concentration.
  • Calibration with donor samples ensured accurate concentration mapping.

Main Results:

  • The AI tool showed strong linear correlation with manual tracking for VSL, VCL, and VAP (R²=0.93-0.98).
  • It outperformed commercial CASA in accuracy and consistency across all motility parameters.
  • Post-calibration, RMSE for ALHmax and BCF estimates decreased by 30%-50%.
  • AI-based motility grading closely matched manual classification, unlike CASA.
  • The AI system demonstrated high repeatability (deviations < ±2%) and robustness under variable imaging conditions.

Conclusions:

  • The AI-based tool provides a quantitative, reliable, and repeatable alternative for semen analysis.
  • It offers improved accuracy and consistency compared to existing methods.
  • This technology supports enhanced fertility diagnostics and potentially more informative treatment strategies.