Kavli Affiliate: Changhuei Yang
| First 5 Authors: Cheng Shen, Adiyant Lamba, Meng Zhu, Ray Zhang, Changhuei Yang
| Summary:
Polarization of the mammalian embryo at the right developmental time is
critical for its development to term and would be valuable in assessing the
potential of human embryos. However, tracking polarization requires invasive
fluorescence staining, impermissible in the in vitro fertilization clinic.
Here, we report the use of artificial intelligence to detect polarization from
unstained time-lapse movies of mouse embryos. We assembled a dataset of
bright-field movie frames from 8-cell-stage embryos, side-by-side with
corresponding images of fluorescent markers of cell polarization. We then used
an ensemble learning model to detect whether any bright-field frame showed an
embryo before or after onset of polarization. Our resulting model has an
accuracy of 85% for detecting polarization, significantly outperforming human
volunteers trained on the same data (61% accuracy). We discovered that our
self-learning model focuses upon the angle between cells as one known cue for
compaction, which precedes polarization, but it outperforms the use of this cue
alone. By compressing three-dimensional time-lapsed image data into
two-dimensions, we are able to reduce data to an easily manageable size for
deep learning processing. In conclusion, we describe a method for detecting a
key developmental feature of embryo development that avoids clinically
impermissible fluorescence staining.
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