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Artificial intelligence in the embryology laboratory: a review

Published:November 11, 2021DOI:https://doi.org/10.1016/j.rbmo.2021.11.003

      Abstract

      The goal of an IVF cycle is a healthy live-born baby. Despite the many advances in the field of assisted reproductive technologies, accurately predicting the outcome of an IVF cycle has yet to be achieved. One reason for this is the method of selecting an embryo for transfer. Morphological assessment of embryos is the traditional method of evaluating embryo quality and selecting which embryo to transfer. However, this subjective method of assessing embryos leads to inter- and intra-observer variability, resulting in less than optimal IVF success rates. To overcome this, it is common practice to transfer more than one embryo, potentially resulting in high-risk multiple pregnancies. Although time-lapse incubators and preimplantation genetic testing for aneuploidy have been introduced to help increase the chances of live birth, the outcomes remain less than ideal. Utilization of artificial intelligence (AI) has become increasingly popular in the medical field and is increasingly being leveraged in the embryology laboratory to help improve IVF outcomes. Many studies have been published investigating the use of AI as an unbiased, automated approach to embryo assessment. This review summarizes recent AI advancements in the embryology laboratory.

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      Biography

      Irene Dimitriadis, MD, is a fellowship-trained reproductive endocrinologist and infertility (REI) specialist at Massachusetts General Fertility Center. A native of Greece, she received her medical degree from the University of Athens and completed her OB/GYN residency at Tufts Medical Center and her REI fellowship at Massachusetts General Hospital in Boston.
      Key message
      Artificial intelligence (AI) has the potential to be used as a tool to assist embryologists in daily activities such as performing morphological assessments and in selecting embryos for transfer. AI also has the potential to help clinicians make decisions and help patients achieve their goal of having a healthy baby.