Human Blastocyst Classification after In Vitro Fertilization Using Deep Learning

TL;DR

This paper presents a deep learning model that automates embryo quality assessment after IVF, achieving high accuracy and potentially reducing variability among embryologists.

Contribution

The study develops and evaluates a deep learning approach for embryo grading, demonstrating its effectiveness compared to expert assessments.

Findings

Achieved 91.79% accuracy in embryo grading

Used 1084 images from 1226 embryos for training and testing

Potential for automated assessment in clinical settings

Abstract

Embryo quality assessment after in vitro fertilization (IVF) is primarily done visually by embryologists. Variability among assessors, however, remains one of the main causes of the low success rate of IVF. This study aims to develop an automated embryo assessment based on a deep learning model. This study includes a total of 1084 images from 1226 embryos. The images were captured by an inverted microscope at day 3 after fertilization. The images were labelled based on Veeck criteria that differentiate embryos to grade 1 to 5 based on the size of the blastomere and the grade of fragmentation. Our deep learning grading results were compared to the grading results from trained embryologists to evaluate the model performance. Our best model from fine-tuning a pre-trained ResNet50 on the dataset results in 91.79% accuracy. The model presented could be developed into an automated embryo assessment method in point-of-care settings.