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Artificial Intelligence in Health

REVIEW ARTICLE
Advancing embryo selection in artificial

intelligence-assisted reproductive technologies:
A systematic review

Md. Abul Basar Roky , Anonno Singha Ray , and Asim Moin Saad*

Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Kazla,
Rajshahi, Bangladesh

Abstract

For couples encountering infertility challenges, assisted reproductive technologies
(ARTs) offer a path to parenthood. ART procedures, such as in vitro fertilization
(IVF), intracytoplasmic sperm injection (ICSI), and embryo implantation, involve
the handling of sperm or embryos outside the body. However, the success of ART
depends on the accurate selection of viable embryos. Artificial intelligence (AI) is
a promising tool with the potential to revolutionize these procedures. This review
explores the transformative potential of AI in ART, providing valuable insights into
enhanced embryo selection and unlocking new possibilities for the field. Four
electronic databases were systematically searched under the Preferred Reporting
Items for Systematic Reviews and Meta-Analyses guidelines. From an initial pool
of 914 papers, 30 studies were selected for further evaluation. While noting the
limitations inherent in the existing body of research, this review offers a broad
*Corresponding author: analysis of AI’s transformative role in embryo selection. It highlights the significant
Asim Moin Saad
(1808036@student.ruet.ac.bd) potential of AI to enhance precision, consistency, and efficiency in ART. This review
also emphasizes the importance of addressing technical, ethical, and regulatory
Citation: Roky MAB, Ray AS,
Saad AM. Advancing embryo aspects to ensure responsible and effective integration of these technologies. The
selection in artificial intelligence- findings indicate that AI-based models, such as the iDAScore v2.0, have demonstrated
assisted reproductive technologies: promising results in accurately predicting embryo viability and evaluating the effects
A systematic review. Artif Intell
Health. 2025;2(3):1-23. of maternal age on embryo viability. Specifically, Bayesian network modeling, with
doi: 10.36922/aih.7170 an accuracy rate of 91.3%, aims to optimize IVF and ICSI procedures. In summary,
Received: December 10, 2024 AI stands at the forefront of innovation in ART, offering new hope through more
accurate and efficient embryo selection.
Revised: February 21, 2025
Accepted: March 3, 2025
Keywords: Artificial intelligence; Machine learning; Deep learning; Embryo selection;
Published online: May 2, 2025 Assisted reproductive technologies
Copyright: © 2025 Author(s).
This is an Open-Access article
distributed under the terms of the
Creative Commons Attribution 1. Introduction
License, permitting distribution,
and reproduction in any medium, Once considered a private matter, infertility has become a globally recognized issue,
provided the original work is
properly cited. affecting millions of couples worldwide. The burden of infertility has been increasing
globally and regionally for both males and females. Infertility affects one in six adults
Publisher’s Note: AccScience
Publishing remains neutral with worldwide, with higher rates reported in the Americas and the Western Pacific region. 1
regard to jurisdictional claims in
published maps and institutional The global infertility rate has shown fluctuating trends over the past few decades.
affiliations. In high-income and developed countries, the prevalence of primary and secondary

Volume 2 Issue 3 (2025) 1 https://doi.org/10.36922/aih.7170