# Human Blastocyst Classification after In Vitro Fertilization Using Deep Learning

Source: [Human Blastocyst Classification after In Vitro Fertilization Using Deep Learning](<https://www.sota2.com/research/paper/68fa68331e209131cea1aba7>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:44.234Z

Authors: Ali Akbar Septiandri, Ade Jamal, Pritta Ameilia Iffanolida, Oki Riayati, Budi Wiweko

Published: 2020-08-28T04:40:55.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2008.12480>)

[PDF](<https://arxiv.org/pdf/2008.12480.pdf>)