An edge association graph network conforming to embryonic morphology for automated grading of day 3 human embryos

Abstract

Purpose

Embryo grading is the essential component of assisted reproductive technologies and a crucial prerequisite for ensuring successful embryo transfer. An effective embryo grading method can help embryologists automatically evaluate the quality of embryos and select high-quality embryos.

Methods

This study enrolled 5836 embryonic images from 2880 couples who have underwent assisted reproductive therapy at our hospital between September 2016 and March 2023. We proposed an edge association graph (EAG) model that contains a two-stage network: (i) a first-stage edge segmentation network that aims to quantify embryo cells and fragments edges; and (ii) a second-stage network that utilizes quantitative edge information to construct an edge relationship graph, and extracts spatial topological information by integrating the graph neural network (GNN) to accomplish the task of embryo grading. Five embryologists of varying years of experience were invited to compare embryo grading with the EAG on an independent test set.

Results and conclusions

Our EAG successfully achieved automatic embryo 4-category grading and showed higher performance compared to existing state-of-arts methods based on microscopic (accuracy = 0.8696, recall = 0.8484, precision = 0.8883 and F1-score = 0.8658) and time-lapse (accuracy = 0.7671, recall = 0.6843, precision = 0.7663 and F1-score = 0.6918) images of embryos. The performance of EAG outperformed five embryologists average, which indicates its superior for embryo grading and has good potential for clinically assisted embryo reproduction applications.