Palmer, Giles A, Chavez-Badiola, Alejandro, Valencia-Murillo, Roberto, Harvey, Simon C ORCID: https://orcid.org/0000-0002-7504-2227, Mendizabal-Ruiz, Gerardo, Farıas, Adolfo Flores-Saiffe, Paredes, Omar and Griffin, Darren K (2025) Can AI-guided feedback improve embryologists' selection of euploid embryos based on morphology alone? Reproductive Biomedicine Online (RBMO):104990. ISSN 1472-6483 (Print), 1472-6491 (Online) (doi:10.1016/j.rbmo.2025.104990)

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Abstract

Research Question
This study investigates whether embryologists can reliably differentiate euploid from aneuploid embryos based on morphology alone, and examines the effectiveness of AI-assisted selection, specifically using ERICA (Embryo Ranking Intelligent Classification Algorithm), in improving embryo selection outcomes compared to embryologists alone.
Design
A training tool was developed and employed in which 19 embryologists (comprising junior, intermediate, and experienced practitioners) evaluated the ploidy status of embryo images. They were subsequently provided with rankings generated by ERICA for the same embryos and asked to make a final judgment combining both sources of information. This process was conducted over between 20 to 150 simulated IVF cycles to assess performance in identifying euploid embryos.
Results
Both embryologists and ERICA demonstrated a statistically significant ability to identify aneuploidy better than random selection. ERICA outperformed embryologists in selecting euploid embryos on the first attempt. However, the combination of embryologist judgment augmented by ERICA did not result in a superior outcome compared to either approach individually.
Conclusion
The study highlights that AI tools like ERICA can enhance the reliability of embryo selection by reducing subjectivity and bias, yet the combination of human and AI judgment does not always provide a clear advantage over using either method independently. These findings emphasize the need to better understand the influence of AI on human decision-making and the trust placed in automated processes in IVF settings.

Item Type:	Article
Uncontrolled Keywords:	Artificial Intelligence, embryo selection, aneuploid, detection, embryologist, decision-making
Subjects:	Q Science > Q Science (General) Q Science > QP Physiology T Technology > T Technology (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Engineering (ENG)
Last Modified:	04 Apr 2025 14:10
URI:	http://gala.gre.ac.uk/id/eprint/50151

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