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Spermatogenesis

Spermatogenesis

Spermatogenesis is the process by which haploid sperm cells are produced in the male testes. It starts with stem cells located close to the outer rim of seminiferous tubules. These spermatogonial stem cells divide asymmetrically to give rise to additional stem cells (meaning that these structures “self-renew”), as well as sperm progenitors, called spermatocytes. Importantly, this method of asymmetric mitotic division maintains a population of spermatogonial stem cells in the male reproductive...

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Related Experiment Video

Updated: May 27, 2026

Fish Sperm Assessment Using Software and Cooling Devices

Fish Sperm Assessment Using Software and Cooling Devices

Published on: July 28, 2018

Machine learning for sperm selection.

Jae Bem You^1,2, Christopher McCallum¹, Yihe Wang¹

¹Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, ON, Canada.

Nature Reviews. Urology

|May 18, 2021

Summary

This summary is machine-generated.

Machine learning (ML) offers new hope for male infertility. ML algorithms can analyze vast data to improve sperm selection, potentially increasing the success rates of assisted reproductive technologies.

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Last Updated: May 27, 2026

Fish Sperm Assessment Using Software and Cooling Devices

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Published on: July 28, 2018

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Sperm Collection of Differential Quality Using Density Gradient Centrifugation

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Published on: November 29, 2018

Area of Science:

Reproductive Medicine
Artificial Intelligence
Bioinformatics

Background:

Global infertility rates and demand for fertility treatments are rising.
Assisted reproductive technologies (ART) achieve limited success (~33%) despite millions of cycles performed annually.
Current male fertility treatments face challenges in selecting optimal sperm from millions of gametes.

Purpose of the Study:

To explore the potential of machine learning (ML) in enhancing male fertility treatments.
To address the critical challenge of sperm selection in assisted reproduction.
To leverage ML's pattern recognition for improved diagnostic and therapeutic outcomes in male infertility.

Main Methods:

Utilizing machine learning algorithms for automated data analysis and pattern inference.
Applying ML techniques to sperm analysis and selection processes.
Integrating modern data processing capabilities with ML for high-throughput gamete selection.

Main Results:

ML is currently aiding embryo selection in fertility clinics.
ML has been applied in research to improve sperm analysis and selection.
The inherent complexity of sperm selection is well-suited for ML applications.

Conclusions:

Machine learning presents significant opportunities to advance male fertility treatments.
ML-powered sperm selection can potentially improve the efficiency and success rates of ART.
The integration of ML in reproductive medicine is poised to revolutionize diagnostics and therapeutics for male infertility.