Blastocyst classification systems used in Latin America: is a consensus
            possible?

Puga-Torres, Tatiana; Blum-Rojas, Xavier; Blum-Narváez, Medardo

doi:10.5935/1518-0557.20170043

Cited by 13 publications

(11 citation statements)

References 14 publications

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“…We converted various quantitative grades related to other data resources to the Veeck and Zaninovic 38 scoring system before testing our trained algorithm with other clinical resources ( Supplementary Table 3). For instance, the 3AA grade in our WCM-NY dataset is equivalent to the BEaa grade in the Universidad de Valencia dataset and the 4AA grade in the IRDB-IC dataset ( Supplementary Table 5), which is based on the Gardner system 9,24 . Notably, these two datasets are less accurate compared to the WCM-NY dataset due to variations in the grading systems.…”

Section: Appendix A: Embryologists Split and Merge The Quantity Gradesmentioning

confidence: 99%

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Robust Automated Assessment of Human Blastocyst Quality using Deep Learning

Khosravi

Kazemi

Toschi

et al. 2018

Preprint

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Morphology assessment has become the standard method for evaluation of embryo quality and selecting human blastocysts for transfer in in vitro fertilization (IVF). This process is highly subjective for some embryos and thus prone to human bias. As a result, morphological assessment results may vary extensively between embryologists and in some cases may fail to accurately predict embryo implantation and live birth potential. Here we postulated that an artificial intelligence (AI) approach trained on thousands of embryos can reliably predict embryo quality without human intervention.To test this hypothesis, we implemented an AI approach based on deep neural networks (DNNs). Our approach called STORK accurately predicts the morphological quality of blastocysts based on raw digital images of embryos with 98% accuracy. These results indicate that a DNN can automatically and accurately grade embryos based on raw images.Using clinical data for 2,182 embryos, we then created a decision tree that integrates 2 clinical parameters such as embryo quality and patient age to identify scenarios associated with increased or decreased pregnancy chance. This IVF data-driven analysis shows that the chance of pregnancy varies from 13.8% to 66.3%.In conclusion, our AI-driven approach provides a novel way to assess embryo quality and uncovers new, potentially personalized strategies to select embryos with an improved likelihood of pregnancy outcome.

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Section: Appendix A: Embryologists Split and Merge The Quantity Gradesmentioning

confidence: 99%

“…This makes it difficult and challenging to compare selection methodologies and analyze patients undergoing treatments in different clinics. Indeed, attempts to establish a universal grading and selection system have thus far failed to catch on 9 .…”

Section: Introductionmentioning

confidence: 99%

Robust Automated Assessment of Human Blastocyst Quality using Deep Learning

Khosravi

Kazemi

Toschi

et al. 2018

Preprint

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show abstract

“…Indeed, attempts to establish a universal grading and selection system have thus far failed to catch on. 8…”

Section: Introductionmentioning

confidence: 99%

Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization

et al. 2019

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Visual morphology assessment is routinely used for evaluating of embryo quality and selecting human blastocysts for transfer after in vitro fertilization (IVF). However, the assessment produces different results between embryologists and as a result, the success rate of IVF remains low. To overcome uncertainties in embryo quality, multiple embryos are often implanted resulting in undesired multiple pregnancies and complications. Unlike in other imaging fields, human embryology and IVF have not yet leveraged artificial intelligence (AI) for unbiased, automated embryo assessment. We postulated that an AI approach trained on thousands of embryos can reliably predict embryo quality without human intervention. We implemented an AI approach based on deep neural networks (DNNs) to select highest quality embryos using a large collection of human embryo time-lapse images (about 50,000 images) from a high-volume fertility center in the United States. We developed a framework (STORK) based on Google’s Inception model. STORK predicts blastocyst quality with an AUC of >0.98 and generalizes well to images from other clinics outside the US and outperforms individual embryologists. Using clinical data for 2182 embryos, we created a decision tree to integrate embryo quality and patient age to identify scenarios associated with pregnancy likelihood. Our analysis shows that the chance of pregnancy based on individual embryos varies from 13.8% (age ≥41 and poor-quality) to 66.3% (age <37 and good-quality) depending on automated blastocyst quality assessment and patient age. In conclusion, our AI-driven approach provides a reproducible way to assess embryo quality and uncovers new, potentially personalized strategies to select embryos.

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“…The main morphological parameters analyzed are based on blastocyst stage and, currently, the most wide classification system used is based on morphological parameters graded by Gardner & Schoolcraft (1999) , which involves expansion and hatching (EH) stage, from 1 (the least grade of expansion) to 6 (totally hatched); inner cell mass (ICM) grades, classified from A (many tightly packed cells) to C (very few cells) and trophectoderm (TE) grades, classified from A (many cells forming cohesive epithelium) to C (large and scarce cells). Despite the constant improvement of embryo selection systems, morphological evaluation observed through light microscopy remains the most used in assisted reproduction clinics worldwide ( Alpha & ESHRE, 2011 ; Nasiri & Eftekhari-Yazdi, 2015 ; Puga-Torres et al ., 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Mining of variables from embryo morphokinetics, blastocyst’s morphology and patient parameters: an approach to predict the live birth in the assisted reproduction service

Chéles

Molin

Rocha

et al. 2020

JBRA Assisted Reproduction

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Based on growing demand for assisted reproduction technology, improved predictive models are required to optimize in vitro fertilization/intracytoplasmatic sperm injection strategies, prioritizing single embryo transfer. There are still several obstacles to overcome for the purpose of improving assisted reproductive success, such as intra-and inter-observer subjectivity in embryonic selection, high occurrence of multiple pregnancies, maternal and neonatal complications. Here, we compare studies that used several variables that impact the success of assisted reproduction, such as blastocyst morphology and morphokinetic aspects of embryo development as well as characteristics of the patients submitted to assisted reproduction, in order to predict embryo quality, implantation or live birth. Thereby, we emphasize the proposal of an artificial intelligence-based platform for a more objective method to predict live birth.

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Blastocyst classification systems used in Latin America: is a consensus possible?

Abstract: Objective: To identify different blastocyst classification systems used by embryologists in Latin American countries and evaluate the possibility of establishing a consensus among these countries.

Cited by 13 publications

References 14 publications

Robust Automated Assessment of Human Blastocyst Quality using Deep Learning

Robust Automated Assessment of Human Blastocyst Quality using Deep Learning

Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization

Mining of variables from embryo morphokinetics, blastocyst’s morphology and patient parameters: an approach to predict the live birth in the assisted reproduction service

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