Stain-free Detection of Embryo Polarization using Deep Learning

TL;DR

This study develops a deep learning model to detect embryo polarization from unstained bright-field images, achieving high accuracy and avoiding invasive fluorescence staining, thus aiding embryo assessment in IVF.

Contribution

The paper introduces a novel AI-based method to identify embryo polarization using only bright-field images, outperforming human accuracy and eliminating the need for invasive staining.

Findings

Deep learning model achieves 85% accuracy in polarization detection.

Model outperforms trained human volunteers with 61% accuracy.

Focuses on cell angle cues, surpassing single-feature analysis.

Abstract

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.