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Deep learning-based selection of human sperm with high DNA integrity.

Christopher McCallum1, Jason Riordon1, Yihe Wang1

  • 11Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, ON Canada M5S 3G8.

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This study introduces a deep learning method to predict sperm DNA quality from images, aiding fertility treatments. The AI model assists clinicians in selecting sperm with higher DNA integrity, improving reproductive outcomes.

Keywords:
BiotechnologyDNA damage and repairMachine learningReproductive biology

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Area of Science:

  • Reproductive Biology
  • Artificial Intelligence in Medicine
  • Genetics

Background:

  • Assessing individual sperm DNA quality is crucial for human reproduction but currently lacks clinical methods.
  • Sperm selection traditionally relies on morphology and motility, not direct DNA integrity assessment.

Purpose of the Study:

  • To develop a deep learning model for predicting sperm DNA quality using only brightfield microscopy images.
  • To integrate AI-driven DNA quality assessment into existing clinical sperm selection workflows.

Main Methods:

  • A deep convolutional neural network was trained on approximately 1000 sperm cells with known DNA quality.
  • The model learned to predict DNA quality from brightfield images of individual sperm cells.

Main Results:

  • A moderate correlation (bivariate correlation ~0.43) was observed between sperm cell images and DNA quality.
  • The deep learning model demonstrated an ability to identify sperm cells with higher DNA integrity compared to the median.
  • The AI system provides rapid predictions (under 10 ms per cell).

Conclusions:

  • Deep learning offers a viable method for predicting sperm DNA quality from brightfield images.
  • This AI-assisted selection can enhance the efficiency and accuracy of sperm selection in clinical settings.
  • The technology supports clinicians by providing rapid DNA quality insights, potentially improving assisted reproductive technologies outcomes.