Automatic grading of human blastocysts from time-lapse imaging

Kragh, Mikkel Fly; Rimestad, Jens; Berntsen, Jørgen; Karstoft, Henrik

doi:10.1016/j.compbiomed.2019.103494

Cited by 87 publications

(62 citation statements)

References 24 publications

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“…The decision tree developed on the basis of those observations was associated with pregnancy outcomes. 79 Kragh and colleagues 80 proposed a fully automated method, based on deep learning, able to predict trophectoderm and inner cell mass quality. This model, built on 8664 blastocysts, performed better than individual human embryologists in predicting embryo quality and implantability.…”

Section: Future Perspectivesmentioning

confidence: 99%

The clinical use of time-lapse in human-assisted reproduction

Minasi

Greco

Varricchio

et al. 2020

Clin Med�Insights�Reprod�Health

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A major challenge in the assisted reproduction laboratory is to set up reproducible and efficient criteria to identify the embryo with the highest developmental potential. Over the years, several methods have been used worldwide with this purpose. Initially, standard morphology assessment was the only available strategy. It is now universally recognized that besides being a very subjective embryo selection strategy, morphology evaluation alone has a very poor prognostic value. More recently, the availability of time-lapse incubators allowed a continuous monitoring of human embryo development. This technology has spread quickly and many fertility clinics over the world produced a remarkable amount of data. To date, however, a general consensus on which variables, or combination of variables, should play a central role in embryo selection is still lacking. Many confounding factors, concerning both patient features and clinical and biological procedures, have been observed to influence embryo development. In addition, several studies have reported unexpected positive outcomes, even in the presence of abnormal developmental criteria. While it does not seem that time-lapse technology is ready to entirely replace the more invasive preimplantation genetic testing in identifying the embryo with the highest implantation potential, it is certainly true that its application is rapidly growing, becoming progressively more accurate. Studies involving artificial intelligence and deep-learning models as well as combining morphokinetic with other non-invasive markers of embryo development, are currently ongoing, raising hopes for its successful applicability for clinical purpose in the near future. The present review mainly focuses on data published starting from the first decade of 2000, when time-lapse technology was introduced as a routine clinical practice in the infertility centers.

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Section: Future Perspectivesmentioning

confidence: 99%

The clinical use of time-lapse in human-assisted reproduction

Minasi

Greco

Varricchio

et al. 2020

Clin Med�Insights�Reprod�Health

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“…In human ART, several parameters such as embryo development stage, blastocoel volume, ICM, and TE thickness are recognized as assessment criteria. The operator subjectively determines the scores of these parameters by microscopic observation [17,18]. The present study shows that a stage-top OCT system could serve as a new way of objectively evaluating the quality of bovine embryos.…”

Section: Textmentioning

confidence: 74%

Three-dimensional live imaging of bovine embryos by optical coherence tomography

Masuda¹,

Hasebe

Kuromi

et al. 2021

J. Reprod. Dev.

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While embryo transfer (ET) is widely practiced, many of the transferred embryos fail to develop in cattle. To establish a more effective method for selecting bovine embryos for ET, here we quantified morphological parameters of living embryos using three-dimensional (3D) images non-invasively captured by optical coherence tomography (OCT). Seven Japanese Black embryos produced by in vitro fertilization that had reached the expanded blastocyst stage after 7 days of culture were transferred after imaged by OCT. Twenty-two parameters, including thickness and volumes of the inner cell mass, trophectoderm, and zona pellucida, and volumes

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“…Kragh performed inspiring work in combining the temporal information with three fixed focal length images for embryo evaluation. [12] It will be very interesting to combine our model with temporal information in the future.…”

Section: Discussionmentioning

confidence: 99%

“…Kragh showed us an approach to automate blastocyst morphology grading by incorporating temporal information available with TLI. [12] Although deep learning technology has made some progress in the evaluation of embryo images, it is still difficult to explain exactly how and why that algorithm works, which has become one of the major obstacles for its clinical application. Computer scientists have developed different kinds of visualization to explain deep learning technology, such as deconvolution [13] and class activation maps (CAMs) [14].…”

Section: Introductionmentioning

confidence: 99%