# The potential of self- supervised learning in embryo selection for IVF success

## TL;DR

This paper introduces a new AI method to improve embryo selection in IVF, potentially increasing success rates.

## Contribution

A novel multi-modal self-supervised learning framework for embryo selection is proposed with high accuracy and generalization.

## Key findings

- A self-supervised learning framework was developed for embryo selection in IVF.
- The framework demonstrated high accuracy and generalization ability.
- The method could help improve clinical decisions in embryo transfer.

## Abstract

How to select the “best” embryo for transfer is a long-standing question in clinical in vitro fertilization (IVF). Wang et al. proposed a multi-modal self-supervised learning framework for human embryo selection with a high accuracy and generalization ability.

How to select the “best” embryo for transfer is a long-standing question in clinical in vitro fertilization (IVF). Wang et al. proposed a multi-modal self-supervised learning framework for human embryo selection with a high accuracy and generalization ability.