Journal
JOURNAL OF ASSISTED REPRODUCTION AND GENETICS
Volume 38, Issue 7, Pages 1627-1639Publisher
SPRINGER/PLENUM PUBLISHERS
DOI: 10.1007/s10815-021-02123-2
Keywords
In vitro fertilization; Deep learning; Artificial intelligence; Embryo assessment; Embryo selection
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In vitro fertilization has been facing challenges due to the outdated method of embryo evaluation, which is subjective and time-consuming. However, the introduction of AI solutions in IVF has shown potential in automating tasks and reducing bias, thereby improving efficiency and success rates.
In vitro fertilization has been regarded as a forefront solution in treating infertility for over four decades, yet its effectiveness has remained relatively low. This could be attributed to the lack of advancements for the method of observing and selecting the most viable embryos for implantation. The conventional morphological assessment of embryos exhibits inevitable drawbacks which include time- and effort-consuming, and imminent risks of bias associated with subjective assessments performed by individual embryologists. A combination of these disadvantages, undeterred by the introduction of the time-lapse incubator technology, has been considered as a prominent contributor to the less preferable success rate of IVF cycles. Nonetheless, a recent surge of AI-based solutions for tasks automation in IVF has been observed. An AI-powered assistant could improve the efficiency of performing certain tasks in addition to offering accurate algorithms that behave as baselines to minimize the subjectivity of the decision-making process. Through a comprehensive review, we have discovered multiple approaches of implementing deep learning technology, each with varying degrees of success, for constructing the automated systems in IVF which could evaluate and even annotate the developmental stages of an embryo.
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