Sequential embryo scoring as a predictor of aneuploidy in poor-prognosis patients

Finn, A.; Scott, Lynette; O’Leary, Thomas; Davies, D.W.; Hill, Joseph A.

doi:10.1016/j.rbmo.2010.05.004

Cited by 38 publications

(22 citation statements)

References 86 publications

(130 reference statements)

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“…Even before the application of time-lapse machines, various parameters of pre-compaction stage development were suggested to predict in-vitro developmental compentence (Scott et al, 2007), implantation potential (Lawler et al, 2007) or euploidy (Finn et al, 2010;Magli et al, 2007). Since the first reported baby born after embryo selection based on timelapse parameters (Pribenszky et al, 2010), extensive research has been conducted to establish the most relevant morphokinetic parameters to predict embryo viability.…”

Section: Article In Pressmentioning

confidence: 99%

No evidence of association between blastocyst aneuploidy and morphokinetic assessment in a selected population of poor-prognosis patients: a longitudinal cohort study

Rienzi

Capalbo

Stoppa

et al. 2015

Reproductive BioMedicine Online

117

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Section: Article In Pressmentioning

confidence: 99%

No evidence of association between blastocyst aneuploidy and morphokinetic assessment in a selected population of poor-prognosis patients: a longitudinal cohort study

Rienzi

Capalbo

Stoppa

et al. 2015

Reproductive BioMedicine Online

117

View full text Add to dashboard Cite

“…Morphological structures, such as meiotic spindles, zona pellucidae, vacuoles or refractile bodies, polar body shapes, oocyte shapes, dark cytoplasm or diffuse granulation, the perivitelline space, central cytoplasmic granulation, cumulus‐oocyte complexes, cytoplasmic viscosity, and membrane resistance characteristics, have been investigated, but none of these features have been conclusively found to have prognostic value for the further developmental competence of oocytes . Additionally, conventional morphological evaluation has had limited success in identifying aneuploid embryos . Some investigations have been able to predict aneuploidy.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of deep learning for predicting live birth from a blastocyst image in patients classified by age

Miyagi

Habara

Hirata

et al. 2019

Reprod Medicine & Biology

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Purpose To identify artificial intelligence (AI) classifiers in images of blastocysts to predict the probability of achieving a live birth in patients classified by age. Results are compared to those obtained by conventional embryo (CE) evaluation. Methods A total of 5691 blastocysts were retrospectively enrolled. Images captured 115 hours after insemination (or 139 hours if not yet large enough) were classified according to maternal age as follows: <35, 35‐37, 38‐39, 40‐41, and ≥42 years. The classifiers for each category and a classifier for all ages were related to convolutional neural networks associated with deep learning. Then, the live birth functions predicted by the AI and the multivariate logistic model functions predicted by CE were tested. The feasibility of the AI was investigated. Results The accuracies of AI/CE for predicting live birth were 0.64/0.61, 0.71/0.70, 0.78/0.77, 0.81/0.83, 0.88/0.94, and 0.72/0.74 for the age categories <35, 35‐37, 38‐39, 40‐41, and ≥42 years and all ages, respectively. The sum value of the sensitivity and specificity revealed that AI performed better than CE ( P = 0.01). Conclusions AI classifiers categorized by age can predict the probability of live birth from an image of the blastocyst and produced better results than were achieved using CE.

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“…Morphological structures such as meiotic spindles, zona pellucidae, vacuoles or refractile bodies, and polar body shapes have been investigated, but none of these features have been conclusively assessed as having prognostic value for the further developmental competence of oocytes . Conventional morphological evaluation has had limited success at identifying aneuploid embryos . Several observational studies have proposed time‐lapse parameters as predictive of aneuploidy, though these have had diverging conclusions.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of artificial intelligence for predicting live birth without aneuploidy from a blastocyst image

Miyagi

Habara

Hirata

et al. 2019

Reprod Medicine & Biology

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Purpose To make the artificial intelligence (AI) classifiers of the image of the blastocyst implanted later in order to predict the probability of achieving live birth. Methods A system for using the machine learning approaches, which are logistic regression, naive Bayes, nearest neighbors, random forest, neural network, and support vector machine, of artificial intelligence to predict the probability of live birth from a blastocyst image was developed. Eighty images of blastocysts that led to live births and 80 images of blastocysts that led to aneuploid miscarriages were used to create an AI‐based method with 5‐fold cross‐validation retrospectively for classifying embryos. Results The logistic regression method showed the best results. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.65, 0.60, 0.70, 0.67, and 0.64, respectively. Area under the curve was 0.65 ± 0.04 (mean ± SE). Estimated probability of belonging to the live birth category was found significantly related to the probability of live birth ( P < 0.005). Conclusions Classifiers using artificial intelligence applied toward a blastocyst image have a potential to show the probability of live birth being the outcome.

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Sequential embryo scoring as a predictor of aneuploidy in poor-prognosis patients

Cited by 38 publications

References 86 publications

No evidence of association between blastocyst aneuploidy and morphokinetic assessment in a selected population of poor-prognosis patients: a longitudinal cohort study

No evidence of association between blastocyst aneuploidy and morphokinetic assessment in a selected population of poor-prognosis patients: a longitudinal cohort study

Feasibility of deep learning for predicting live birth from a blastocyst image in patients classified by age

Feasibility of artificial intelligence for predicting live birth without aneuploidy from a blastocyst image

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