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Sperm-cell DNA fragmentation prediction using label-free quantitative phase imaging and deep learning.

Lioz Noy1, Itay Barnea1, Simcha K Mirsky1

  • 1Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology
|November 5, 2022
PubMed
Summary

A new method predicts sperm DNA fragmentation during intracytoplasmic sperm injection (ICSI) using stain-free imaging and deep learning. This technique enhances sperm selection for improved fertilization success rates.

Keywords:
DNA fragmentationcell classificationin vitro fertilizationquantitative phase imaging

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Area of Science:

  • Reproductive biology
  • Biomedical imaging
  • Artificial intelligence in medicine

Background:

  • Sperm DNA fragmentation significantly impacts intracytoplasmic sperm injection (ICSI) success.
  • Current methods for assessing sperm DNA fragmentation require staining and are incompatible with live cell selection during ICSI.

Purpose of the Study:

  • To develop a non-invasive method for predicting sperm DNA fragmentation in live cells during ICSI.
  • To utilize stain-free imaging and deep learning for accurate DNA fragmentation assessment.

Main Methods:

  • Employed multi-layer, stain-free imaging, including quantitative phase imaging.
  • Utilized lightweight deep learning architectures, specifically MobileNet, for prediction.
  • Established DNA fragmentation ground truth using acridine orange staining and fluorescence microscopy.

Main Results:

  • Achieved a mean absolute error of 0.05 for high-confidence predictions.
  • Reported a 90th percentile mean absolute error of 0.1 on a DNA fragmentation score range of [0,1].
  • Demonstrated a reliable prediction model for sperm DNA fragmentation.

Conclusions:

  • The developed method offers a promising approach for non-invasively assessing sperm DNA fragmentation.
  • This technology has the potential to improve embryologists' cell selection during ICSI.
  • Future applications may enhance assisted reproductive technologies and fertility outcomes.