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Multi-Modal Artificial Intelligence of Embryo Grading and Pregnancy Prediction in Assisted Reproductive Technology: A Review

arXiv 2505.20306 ↗First posted 19 May 2025cs.AI eess.IV q-bio.QMcited by 0

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Abstract (arXiv)

Infertility, a pressing global health concern, affects a substantial proportion of individuals worldwide. While advancements in assisted reproductive technology (ART) have offered effective interventions, conventional in vitro fertilization-embryo transfer (IVF-ET) procedures still encounter significant hurdles in enhancing pregnancy success rates. Key challenges include the inherent subjectivity in embryo grading and the inefficiency of multi-modal data integration. Against this backdrop, the adoption of AI-driven technologies has emerged as a pivotal strategy to address these issues. This article presents a comprehensive review of the progress in AI applications for embryo grading and pregnancy prediction from a novel perspective, with a specific focus on the utilization of different modal data, such as static images, time-lapse videos, and structured tabular data. The reason for this perspective is that reorganizing tasks based on data sources can not only more accurately depict the essence of the problem but also help clarify the rationality and limitations of model design. Furthermore, this review critically examines the core challenges in contemporary research, encompassing the intricacies of multi-modal feature fusion, constraints imposed by data scarcity, limitations in model generalization capabilities, and the dynamically evolving legal and regulatory frameworks. On this basis, it explicitly identifies potential avenues for future research, aiming to provide actionable guidance for advancing the application of multi-modal AI in the field of ART.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 41 of its 201 references that are in the map. First posted on arXiv 19 May 2025.

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41 of 201 references.

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