Open AccessReview of computer vision application in in vitro fertilization: the application of deep learning-based computer vision technology in the world of IVF
Author(s) -
Claudio Michael Louis,
Alva Erwin,
Nining Handayani,
Arie A Polim,
Arief Boediono,
Ivan Sini
Publication year - 2021
Publication title -
journal of assisted reproduction and genetics
Language(s) - English
Resource type - Journals
eISSN - 1573-7330
pISSN - 1058-0468
DOI - 10.1007/s10815-021-02123-2
Subject(s) - reproductive medicine , in vitro fertilisation , artificial intelligence , assisted reproductive technology , automation , computer science , process (computing) , medical physics , infertility , embryo , medicine , biology , pregnancy , engineering , mechanical engineering , genetics , microbiology and biotechnology , operating system
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.