Endometrial Receptivity: An Integrated Evaluation of Genetic, Epigenetic and Artificial Intelligence-Assisted Diagnostic Approaches

Abstract

Endometrial receptivity plays a key role in successful embryo implantation and is regulated by a combination of genetic and epigenetic mechanisms. This review aims to provide an overview of these regulatory processes and to highlight their relevance in clinical practice. In addition, current genomics-based diagnostic methods and emerging artificial intelligence-assisted approaches are discussed in relation to their potential use in evaluating endometrial receptivity. A literature review was conducted using the Scopus, PubMed, Dergipark, Web of Science, and national thesis databases, including peer-reviewed articles and systematic reviews published between 2000 and 2025. Key molecular components such as gene expression patterns, DNA methylation, and non-coding RNAs were evaluated together with available clinical validation data.

The findings indicate that endometrial receptivity is associated with the regulated expression of genes such as HOXA10, LIF, and integrin αvβ3, supported by epigenetic mechanisms including DNA methylation and miRNAs. Molecular diagnostic tools such as ERA and beREADY may help identify the individual implantation window. In addition, artificial intelligence-based models may support the integration of complex molecular data and contribute to clinical decision-making.

Overall, understanding endometrial receptivity through genetic and epigenetic perspectives may help improve prediction and support personalized approaches in assisted reproductive technologies. With ongoing technological developments, these approaches may become increasingly relevant in infertility management.

Keywords: Endometrial Receptivity; Epigenetics; Implantation Failure; Assisted Reproductive Technologies; Artificial Intelligence

Citation

Kübra Kolburan Gürol. Endometrial Receptivity: An Integrated Evaluation of Genetic, Epigenetic and Artificial Intelligence-Assisted Diagnostic Approaches. WebLog J Reprod Med. wjrm.2026.d1701. https://doi.org/10.5281/zenodo.19689168