# Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences

arXiv: https://arxiv.org/abs/2103.07262 · HTML: https://mlsanity.com/paper/2103.07262/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 63 references that are in the map.
- First posted on arXiv 12 Mar 2021.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates](https://mlsanity.com/paper/1708.07120/) (2017)

## Built on by (most cited first, 7 of 7)

- [Early prediction of the transferability of bovine embryos from videomicroscopy](https://mlsanity.com/paper/2501.07945/) (2025)
- [Development and validation of deep learning based embryo selection across multiple days of transfer](https://mlsanity.com/paper/2210.02120/) (2022)
- [DeFusion: An Effective Decoupling Fusion Network for Multi-Modal Pregnancy Prediction](https://mlsanity.com/paper/2501.04353/) (2025)
- [Supervised contrastive learning for cell stage classification of animal embryos](https://mlsanity.com/paper/2502.07360/) (2025)
- [Multi-Modal Artificial Intelligence of Embryo Grading and Pregnancy Prediction in Assisted Reproductive Technology: A Review](https://mlsanity.com/paper/2505.20306/) (2025)
- [Spatial-Temporal Pre-Training for Embryo Viability Prediction Using Time-Lapse Videos](https://mlsanity.com/paper/2506.17403/) (2025)
- [From Division to Decision: Leveraging Temporal Cell-Stage Segmentation for Embryo Transferability Prediction](https://mlsanity.com/paper/2605.18923/) (2026)

## Abstract (arXiv)

Assessing and selecting the most viable embryos for transfer is an essential part of in vitro fertilization (IVF). In recent years, several approaches have been made to improve and automate the procedure using artificial intelligence (AI) and deep learning. Based on images of embryos with known implantation data (KID), AI models have been trained to automatically score embryos related to their chance of achieving a successful implantation. However, as of now, only limited research has been conducted to evaluate how embryo selection models generalize to new clinics and how they perform in subgroup analyses across various conditions. In this paper, we investigate how a deep learning-based embryo selection model using only time-lapse image sequences performs across different patient ages and clinical conditions, and how it correlates with traditional morphokinetic parameters. The model was trained and evaluated based on a large dataset from 18 IVF centers consisting of 115,832 embryos, of which 14,644 embryos were transferred KID embryos. In an independent test set, the AI model sorted KID embryos with an area under the curve (AUC) of a receiver operating characteristic curve of 0.67 and all embryos with an AUC of 0.95. A clinic hold-out test showed that the model generalized to new clinics with an AUC range of 0.60-0.75 for KID embryos. Across different subgroups of age, insemination method, incubation time, and transfer protocol, the AUC ranged between 0.63 and 0.69. Furthermore, model predictions correlated positively with blastocyst grading and negatively with direct cleavages. The fully automated iDAScore v1.0 model was shown to perform at least as good as a state-of-the-art manual embryo selection model. Moreover, full automatization of embryo scoring implies fewer manual evaluations and eliminates biases due to inter- and intraobserver variation.