# Towards deep learning-powered IVF: A large public benchmark for morphokinetic parameter prediction

arXiv: https://arxiv.org/abs/2203.00531 · HTML: https://mlsanity.com/paper/2203.00531/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 45 references that are in the map.
- First posted on arXiv 1 Mar 2022.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition](https://mlsanity.com/paper/1708.07632/) (2017)
- [Predicting Conversion of Mild Cognitive Impairments to Alzheimer's Disease and Exploring Impact of Neuroimaging](https://mlsanity.com/paper/1807.11228/) (2018)
- [2D and 3D Segmentation of uncertain local collagen fiber orientations in SHG microscopy](https://mlsanity.com/paper/1907.12868/) (2019)
- [Embryo staging with weakly-supervised region selection and dynamically-decoded predictions](https://mlsanity.com/paper/1904.04419/) (2019)
- [Multi-Task Deep Learning with Dynamic Programming for Embryo Early Development Stage Classification from Time-Lapse Videos](https://mlsanity.com/paper/1908.09637/) (2019)

## Abstract (arXiv)

An important limitation to the development of Artificial Intelligence (AI)-based solutions for In Vitro Fertilization (IVF) is the absence of a public reference benchmark to train and evaluate deep learning (DL) models. In this work, we describe a fully annotated dataset of 704 videos of developing embryos, for a total of 337k images. We applied ResNet, LSTM, and ResNet-3D architectures to our dataset and demonstrate that they overperform algorithmic approaches to automatically annotate stage development phases. Altogether, we propose the first public benchmark that will allow the community to evaluate morphokinetic models. This is the first step towards deep learning-powered IVF. Of note, we propose highly detailed annotations with 16 different development phases, including early cell division phases, but also late cell divisions, phases after morulation, and very early phases, which have never been used before. We postulate that this original approach will help improve the overall performance of deep learning approaches on time-lapse videos of embryo development, ultimately benefiting infertile patients with improved clinical success rates (Code and data are available at https://gitlab.univ-nantes.fr/E144069X/bench_mk_pred.git).