Cohort embryo selection (CES): a quick and simple method for selecting cleavage stage embryos that will become high quality blastocysts (HQB)

Dimitriadis, Irene; Christou, G.; Dickinson, K.; McLellan, S.; Brock, Mikaela; Souter, Irene; Bormann, Charles L.

doi:10.1016/j.fertnstert.2017.07.488

Cited by 8 publications

(3 citation statements)

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“…In 2017, we reported that the simultaneous evaluation of patients' entire embryo cohort at a single time-point could improve the identification of embryos with the highest potential to form a blastocyst [37]. We observed a higher overall blastocyst formation prediction rate with the cohort selection method (CM), compared to standard morphology grading by expert embryologists and time-lapse scoring (95% vs. 86% and 89%; p = 0.06 and p = 0.21, respectively).…”

Section: Evaluating Resultsmentioning

confidence: 99%

Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018

Curchoe

Bormann

2019

J Assist Reprod Genet

110

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Sixteen artificial intelligence (AI) and machine learning (ML) approaches were reported at the 2018 annual congresses of the American Society for Reproductive Biology (9) and European Society for Human Reproduction and Embryology (7). Nearly every aspect of patient care was investigated, including sperm morphology, sperm identification, identification of empty or oocyte containing follicles, predicting embryo cell stages, predicting blastocyst formation from oocytes, assessing human blastocyst quality, predicting live birth from blastocysts, improving embryo selection, and for developing optimal IVF stimulation protocols. This represents a substantial increase in reports over 2017, where just one abstract each was reported at ASRM (AI) and ESHRE (ML). Our analysis reveals wide variability in how AI and ML methods are described (from not at all or very generic to fully describing the architectural framework) and large variability on accepted dataset sizes (from just 3 patients with 16 follicles in the smallest dataset to 661,060 images of 11,898 human embryos in one of the largest). AI and ML are clearly burgeoning methodologies in human reproduction and embryology and would benefit from early application of reporting standards.

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Section: Evaluating Resultsmentioning

confidence: 99%

Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018

Curchoe

Bormann

2019

J Assist Reprod Genet

110

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“…This study demonstrated the ability of embryologists to quickly identify high-quality cleavage-stage embryos when all embryos in the cohort were simultaneously compared in a single image. This method of selection outperformed traditional methods of cleavage-stage embryo ranking based on both morphology and adjunctive morphokinetic TLI parameters (Dimitriadis et al, 2017). This method is excellent at identifying high-quality embryos from a cohort; however, this method of selection is subjective and lacks consistency between operators.…”

Section: Ai Application On Cleavage Stage Embryosmentioning

confidence: 97%

Artificial intelligence in the embryology laboratory: a review

Dimitriadis

Zaninović

Badiola

et al. 2022

Reproductive BioMedicine Online

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“…Embryoselectionisthemostimportantphaseafterthegradingisdone.Theselection ofembryomainlydependsonwhichdaytheembryoisbeingconsideredsothatithas thehighestchancesoffertility.CohortEmbryoSelectionmethodisbeingusedforthe assessmentanditusesthetimepointsandthedayofembryosthathavethehighest chance of implantation (Dimitriadis, 2017). In analyzing the high-quality embryo, thisisasimpleandnovelmethod.…”

Section: Ai In Embryo Classificationmentioning

confidence: 99%

Application of Machine Learning and Artificial Intelligence Techniques for IVF Analysis and Prediction

Tadepalli

Lakshmi

2019

International Journal of Big Data and Analytics in Healthcare

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Infertility is the combination of factors that prevent pregnancy. It involves a lot of care and expertise while selecting the best embryo to lead to a successful pregnancy. Assistive reproductive technology (ART) helps to solve this issue. In vitro fertilization (IVF) is one of the methods of ART which is very popular. Artificial intelligence will have digital revolution and manifold advances in the field of reproductive medicine and will eventually provide immense benefits to infertile patients. The main aim of this article is to focus on the methods that can predict the accuracy of pregnancy without human intervention. It provides successful studies conducted by using machine learning techniques. This easily enables doctors to understand the behavior of the attributes which are suitable for the treatment. Blastocyst images can be deployed for the detection and prediction of the best embryo which has the maximum chance of a successful pregnancy. This pioneering work gives one a view into how this field could benefit the future generation.

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Cohort embryo selection (CES): a quick and simple method for selecting cleavage stage embryos that will become high quality blastocysts (HQB)

Cited by 8 publications

References 0 publications

Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018

Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018

Artificial intelligence in the embryology laboratory: a review

Application of Machine Learning and Artificial Intelligence Techniques for IVF Analysis and Prediction

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