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New frontiers in embryo selection

Glatstein, Isaac, Chavez-Badiola, Alejandro, Curchoe, Carol Lynn (2023) New frontiers in embryo selection. Journal of Assisted Reproduction and Genetics, . ISSN 1573-7330. (doi:10.1007/s10815-022-02708-5) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:99619)

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Human infertility is a major global public health issue estimated to affect one out of six couples, while the number of assisted reproduction cycles grows impressively year over year. Efforts to alleviate infertility using advanced technology are gaining traction rapidly as infertility has an enormous impact on couples and the potential to destabilize entire societies if replacement birthrates are not achieved. Artificial intelligence (AI) technologies, leveraged by the highly advanced assisted reproductive technology (ART) industry, are a promising addition to the armamentarium of tools available to combat global infertility. This review provides a background for current methodologies in embryo selection, which is a manual, time-consuming, and poorly reproducible task. AI has the potential to improve this process (among many others) in both the clinician's office and the IVF laboratory. Embryo selection is evolving through digital methodologies into an automated procedure, with superior reliability and reproducibility, that is likely to result in higher pregnancy rates for patients. There is an emerging body of data demonstrating the utility of AI applications in multiple areas in the IVF laboratory. AI platforms have been developed to evaluate individual embryologist performance; to provide quality assurance for culture systems; to correlate embryologist's assessments and AI systems; to predict embryo ploidy, implantation, fetal heartbeat, and live birth outcome; and to replace the current "analogue" system of embryo selection with a digital paradigm. AI capability will distinguish high performing, high profit margin, low-cost, and highly successful IVF clinic business models. We think it will become the standard, "new normal" in IVF labs, as rapidly and thoroughly as vitrification, blastocyst culture, and intracytoplasmic sperm injection replaced their predecessor technologies. At the time of this review, the AI technology to automate embryo evaluation and selection has robustly matured, and therefore, it is the main focus of this review.

Item Type: Article
DOI/Identification number: 10.1007/s10815-022-02708-5
Uncontrolled keywords: AI, Embryo ranking, Machine learning, Embryo selection, Artificial intelligence
Subjects: Q Science > QH Natural history > QH301 Biology
Divisions: Divisions > Division of Natural Sciences > Biosciences
SWORD Depositor: JISC Publications Router
Depositing User: JISC Publications Router
Date Deposited: 27 Jan 2023 16:36 UTC
Last Modified: 30 Jan 2023 13:29 UTC
Resource URI: (The current URI for this page, for reference purposes)

University of Kent Author Information

Chavez-Badiola, Alejandro.

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