Non-Invasive Approaches for the Assessment of Human Preimplantation Embryos

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About this Research Topic

Submission deadlines

  1. Manuscript Summary Submission Deadline 30 June 2026 | Manuscript Submission Deadline 31 July 2026

  2. This Research Topic is currently accepting articles.

Background

Assisted Reproductive Technology (ART) has advanced rapidly, yet challenges in selecting the most viable embryos for transfer persist. Historically, embryo selection relied on basic morphological grading. More recently, invasive preimplantation genetic testing (PGT) has become the gold standard, but it requires a biopsy of the trophectoderm—a procedure that carries some risk of harm to the embryo and may not always reflect the overall genetic makeup of the embryo due to mosaicism.

This collection focuses on the emerging field of non-invasive embryo assessment, which aims to enhance embryo selection and ultimately improve clinical outcomes without the need for invasive biopsy. This approach leverages technologies that analyse the embryo's surrounding environment, such as spent culture media (SCM), or utilises advanced imaging techniques. We seek to highlight research that explores and validates these non-invasive methods and their correlation with successful pregnancies and live births.

Topics of Interest

We welcome submissions on different topics, including but not limited to:

- Non-invasive Preimplantation Genetic Testing (niPGT): Studies using cell-free DNA from SCM or blastocoel fluid to assess chromosomal ploidy and screen for genetic disorders.

- Embryo Metabolomics and Proteomics: Research that analyzes the metabolic and protein signatures in SCM as biomarkers for embryo viability, developmental potential, and clinical outcomes.

- Time-Lapse Imaging and Morphokinetics: The use of advanced imaging to monitor embryo development in real-time. We welcome studies that correlate specific kinetic parameters and morphological events with implantation rates and pregnancy outcomes.

- Artificial Intelligence (AI) and Machine Learning in Embryology: Application of AI algorithms to analyze large datasets from time-lapse imaging, -omics data, and clinical records to predict embryo viability and improve selection criteria.

- Correlation with Clinical Outcomes: Clinical studies that validate the effectiveness of these non-invasive methods by demonstrating a positive correlation with key clinical outcomes, such as sustained implantation rates, ongoing pregnancy rates, and live birth rates.

- Ethical and Regulatory Considerations: Discussions on the ethical implications of implementing new non-invasive technologies in clinical practice and the regulatory frameworks required for their safe and effective use.

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Keywords: preimplantation embryos, metabolomics, proteomics, embryology, morphokinetics

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