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

Lioz Noy, Itay Barnea, Simcha K. Mirsky, Dotan Kamber, Mattan Levi, Natan T. Shaked*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In intracytoplasmic sperm injection (ICSI), a single sperm cell is selected and injected into an egg. The quality of the chosen sperm and specifically its DNA fragmentation have a significant effect on the fertilization success rate. However, there is no method today to measure the DNA fragmentation of live and unstained cells during ICSI. We present a new method to predict the DNA fragmentation of sperm cells using multi-layer stain-free imaging data, including quantitative phase imaging, and lightweight deep learning architectures. The DNA fragmentation ground truth is achieved by staining the cells with acridine orange and imaging them via fluorescence microscopy. Our prediction model is based on the MobileNet convolutional neural network architecture combined with confidence measurement determined by distances between vectors in the latent space. Our results show that the mean absolute error for cells with high prediction confidence is 0.05 and the 90th percentile mean absolute error is 0.1, where the range of DNA fragmentation score is [0,1]. In the future, this model may be applied to improve cell selection by embryologists during ICSI.

Original languageEnglish
Pages (from-to)470-478
Number of pages9
JournalCytometry. Part A : the journal of the International Society for Analytical Cytology
Volume103
Issue number6
DOIs
StatePublished - Jun 2023

Keywords

  • DNA fragmentation
  • cell classification
  • in vitro fertilization
  • quantitative phase imaging

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