Embryo Development Stage Onset Detection by Time Lapse Monitoring Based on Deep Learning

Wided Miled, Wided Miled, Sana Chtourou, Nozha Chakroun, Khadija Berjeb

2024

Abstract

In Vitro Fertilisation (IVF) is a procedure used to overcome a range of fertility issues, giving many couples the chance of having a baby. Accurate selection of embryos with the highest implantation potentials is a necessary step toward enhancing the effectiveness of IVF. The detection and determination of pronuclei number during the early stages of embryo development in IVF treatments help embryologists with decision-making regarding valuable embryo selection for implantation. Current manual visual assessment is prone to observer subjectivity and is a long and difficult process. In this study, we build a CNN-LSTM deep learning model to automatically detect pronuclear-stage in IVF embryos, based on Time-Lapse Images (TLI) of their early development stages. The experimental results proved possible the automation of pronuclei determination as the proposed deep learning based method achieved a high accuracy of 85% in the detection of pronuclear-stage embryo.

Paper Citation

in Harvard Style

Miled W., Chtourou S., Chakroun N. and Berjeb K. (2024). Embryo Development Stage Onset Detection by Time Lapse Monitoring Based on Deep Learning. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 368-375. DOI: 10.5220/0012390600003636

in Bibtex Style

@conference{icaart24,
author={Wided Miled and Sana Chtourou and Nozha Chakroun and Khadija Berjeb},
title={Embryo Development Stage Onset Detection by Time Lapse Monitoring Based on Deep Learning},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2024},
pages={368-375},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012390600003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Embryo Development Stage Onset Detection by Time Lapse Monitoring Based on Deep Learning
SN - 978-989-758-680-4
AU - Miled W.
AU - Chtourou S.
AU - Chakroun N.
AU - Berjeb K.
PY - 2024
SP - 368
EP - 375
DO - 10.5220/0012390600003636
PB - SciTePress