2018
DOI: 10.3791/56668
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A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

Abstract: Infertility clinics would benefit from the ability to select developmentally competent vs. incompetent oocytes using non-invasive procedures, thus improving the overall pregnancy outcome. We recently developed a classification method based on microscopic live observations of mouse oocytes during their in vitro maturation from the germinal vesicle (GV) to the metaphase II stage, followed by the analysis of the cytoplasmic movements occurring during this time-lapse period. Here, we present detailed protocols of … Show more

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Cited by 15 publications
(13 citation statements)
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References 13 publications
(7 reference statements)
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“…The A.I. application in reproductive medicine has focused mainly on oocytes evaluation and selection ( 43 ), sperm analysis and selection ( 44 ), and embryo selection ( 45 ). A few studies have attempted to establish models for IVF outcome prediction ( 23 , 46 ).…”
Section: Discussionmentioning
confidence: 99%
“…The A.I. application in reproductive medicine has focused mainly on oocytes evaluation and selection ( 43 ), sperm analysis and selection ( 44 ), and embryo selection ( 45 ). A few studies have attempted to establish models for IVF outcome prediction ( 23 , 46 ).…”
Section: Discussionmentioning
confidence: 99%
“…The hereby work focuses on the use of deep learning methods. Approaches to automatic classification of oocytes and embryos involving this kind of methods are known in the literature [ 14 , 22 , 32 , 33 ]. This approach consists of providing a picture to the network which then classifies and assigns the picture to a given quality group.…”
Section: Methodsmentioning
confidence: 99%
“…Another research also relates to the appearance of oocytes. For instance, Cavalera et al [ 14 ] combine time-lapse analysis with image anemometry and with use of artificial neural network to determine the movement of cytoplasm in maturing mouse oocytes, thus determining also their development potential with 91.03 accuracy. Research studies are also underway to develop a method for detecting embryos in the image.…”
Section: Introductionmentioning
confidence: 99%
“…They reached an excellent result to forecast treatment failure with almost a 90% probability rate. Recently, (Cavalera et al 2018) developed a novel classification method. They predicted the developmental ability of mouse gametes using various models.…”
Section: Overview Of the Ai In Reproductive Medicinementioning
confidence: 99%
“…Thus, the use of AI methods for oocyte selection in IVF programs may bring new opportunities. Cavalera et al (2018) observed mouse oocytes during their in vitro maturation from the germinal vesicle (GV) to the metaphase II stage and took pictures for time-lapse analysis. They calculated the profile of cytoplasmic movement velocities by analyzing the images using the particle image velocimetry (PIV) method, and then the data were analyzed with a feed-forward artificial neural network to identify the competent or incompetent oocytes with an accuracy of 91.03%.…”
Section: Ai Applications In Reproductive Medicinementioning
confidence: 99%