scholarly journals The ART of selecting the best embryo: A review of early embryonic mortality and bovine embryo viability assessment methods

2015 ◽  
Vol 82 (11) ◽  
pp. 822-838 ◽  
Author(s):  
Kayla J. Perkel ◽  
Allison Tscherner ◽  
Casandra Merrill ◽  
Jonathan Lamarre ◽  
Pavneesh Madan
Keyword(s):  
Assessment Methods ◽  
Embryonic Mortality ◽  
Bovine Embryo ◽  
Embryo Viability ◽  
Viability Assessment ◽  
Early Embryonic Mortality

scholarly journals Evaluation of Bovine Embryo Biopsy Techniques according to Their Ability to Preserve Embryo Viability

2012 ◽  
Vol 2012 ◽  
pp. 1-5 ◽  
Author(s):  
M. Cenariu ◽  
E. Pall ◽  
C. Cernea ◽  
I. Groza
Keyword(s):  
Pregnancy Rate ◽  
Genetic Diagnosis ◽  
Needle Aspiration ◽  
Embryo Biopsy ◽  
Embryo Cryopreservation ◽  
Bovine Embryo ◽  
Bovine Embryos ◽  
Embryo Viability ◽  
Biopsy Methods ◽  
Biopsy Method

The purpose of this research was to evaluate three embryo biopsy techniques used for preimplantation genetic diagnosis (PGD) in cattle and to recommend the least invasive one for current use, especially when PGD is followed by embryo cryopreservation. Three hundred bovine embryos were biopsied by either one of the needle, aspiration or microblade method, and then checked for viability by freezing/thawing and transplantation to recipient cows. The number of pregnancies obtained after the transfer of biopsied frozen/thawed embryos was assessed 30 days later using ultrasounds. The results were significantly different between the three biopsy methods: the pregnancy rate was of 57% in cows that received embryos biopsied by needle, 43% in cows that received embryos biopsied by aspiration, and 31% in cows that received embryos biopsied by microblade. Choosing an adequate biopsy method is therefore of great importance in embryos that will undergo subsequent cryopreservation, as it significantly influences their viability after thawing.

scholarly journals Effects of storage period and egg weight of Japanese quail eggs on hatching results (short communication)

2005 ◽  
Vol 48 (5) ◽  
pp. 518-526
Author(s):  
I. Seker ◽  
S. Kul ◽  
M. Bayraktar
Keyword(s):  
Storage Period ◽  
Fertility Rate ◽  
Embryonic Mortality ◽  
Light Weight ◽  
Egg Weight ◽  
Heavy Weight ◽  
Japanese Quails ◽  
Early Embryonic Mortality ◽  
Hatching Eggs ◽  
Weight Groups

Abstract. This study was undertaken to determine the effects of storage period and egg weight of hatching eggs of Japanese quails on fertility, hatchability results. Eggs were obtained 150 females quails, all at 15 weeks of age. A total of 1942 hatching eggs were separated into 3 groups as light-weight (9.50-10.50 g), medium-weight (10.51-11.50 g), and heavy-weight (11.51-12.50 g). Based on storage period, eggs were divided into 5 groups as group 1 (0-3 days), group 2 (4-6 days), group 3 (7-9 days), group 4 (10-12 days), and group 5 (13-15 days). The influence of storage period on hatchability of fertile eggs and early, middle, and late period embryonic mortality rates was found significant (P<0.01). The effect of egg weight on fertility rate, hatchability of fertile eggs and early embryonic mortality was significant (P<0.05, P<0.01). The significant differences between storage period groups were observed in hatchability of fertile eggs. The differences between egg weight groups for fertility rate, hatchability of fertile eggs and early embryonic mortality was significantly higher in light weight group than the other egg weight groups. Results of this study concluded that a 12 day pre-incubation storage of hatching eggs of Japanese quails did not appreciably affect hatching parameters. Use of medium or heavy weight eggs for hatching may reduce early embryonic mortality rate.

P–202 Past embryo viability is not always a good predictor of future pregnancy: dynamic viability suggests video has limited benefit over static images for AI assessment

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
J M M Hall ◽  
M A Dakka ◽  
D Perugini ◽  
S Diakiw ◽  
T Nguyen ◽  
...  
Keyword(s):  
Pregnancy Outcome ◽  
Pregnancy Outcomes ◽  
Embryo Quality ◽  
Time Lapse ◽  
Embryo Viability ◽  
Viability Assessment ◽  
Time Points ◽  
Static Images ◽  
Future Pregnancy ◽  
Over Time

Abstract Study question Does embryo quality/viability change over time, suggesting the use of video for AI-based embryo quality assessment has limited benefit over single point-in-time images? Summary answer AI assessment of single static embryo images at multiple time-points indicates embryo viability is dynamic, and past viability is a limited predictor of future pregnancy. What is known already Artificial Intelligence (AI) has been applied to the problem of embryo quality (viability) assessment using either video or single static images. However, whether historical data within video provide an additional advantage over single static images of embryos (at the time of transfer) for assessing embryo viability is not known. This applies to both manual and AI-based embryo assessment. If embryo viability changes over time prior to transfer, then the implication is that the assessment of future pregnancy using historical embryo data from videos would provide limited additional value over single static images taken immediately prior to transfer. Study design, size, duration Retrospective dataset of single embryo images taken at up-to three time-points prior to transfer: Early Day 5, Late Day 5 (8 hours later), and Early Day 6 (16 hours later), with corresponding fetal heartbeat (pregnancy) outcomes. The AI assessed the viability of each embryo at its available timepoints. Viability prediction was compared with pregnancy outcome to assess viability predictiveness at each timepoint prior to transfer, and assess the variability of viability over time. Participants/materials, setting, methods Single static images of 173 embryos were taken using time-lapse incubators from a single IVF clinic. 116 embryos were viable (led to a pregnancy) and 57 were non-viable (did not lead to a pregnancy). The AI was trained on thousands of Day 5 static embryo images taken from multiple IVF laboratories and countries, but was not trained on data from this clinic. Main results and the role of chance When embryos were assessed as viable by the AI immediately prior to transfer (no delay), the AI accuracy (sensitivity) in predicting pregnancy was 88.1% (59/67) for Early Day 5, 84.8% (28/33) for Late Day 5 and 87.5% (14/16) for Early Day 6. When the delay between AI assessment and transfer is 8 hours, 16 hours and 24 hours, the the accuracy drops to 66.7% (22/33), 31.3% (5/16) and 12.5% (2/16), respectively. These results indicate that the viability of the embryo is dynamic, and therefore time series analysis, i.e. using video, may not be well suited for embryo viability assessment because past viability is not necessarily a good predictor of future viability or pregnancy outcome. The viability of the embryo immediately prior to transfer, from a single static image, is a reliable predictor of viability. This is consistent with the current clinical practice of using Gardner score end-point assessment for embryo quality. Results also suggest significant benefits from using time-lapse with AI, where AI continually assesses embryo viability over time using static images. The time point at which the embryo should be transferred to maximize pregnancy outcome is when the embryo has the greatest AI viability score. Limitations, reasons for caution Although evidence suggests past embryo viability is a limited predictor of future pregnancy, a side-by-side comparison of video versus single static image AI assessment would further verify that the historical or change in embryo development or viability has minimal impact on embryo viability assessment at the time prior to transfer. Wider implications of the findings: Time-lapse and AI can beneficially change the way embryos are assessed. Continual AI monitoring of embryos enables optimization of which embryo to transfer and when, to ultimately improve pregnancy outcomes for patients. The findings also suggest that static end-point AI assessment is sufficient for predicting embryo implantation potential. Trial registration number Not applicable

O-222 An artificial intelligence model that was trained on pregnancy outcomes for embryo viability assessment is highly correlated with Gardner Score

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
S Diakiw ◽  
M VerMilyea ◽  
J M M Hall ◽  
K Sorby ◽  
T Nguyen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Pregnancy Outcomes ◽  
Embryo Quality ◽  
Cell Mass ◽  
Clinical Pregnancy ◽  
Morphological Features ◽  
Embryo Morphology ◽  
Embryo Viability ◽  
Viability Assessment ◽  
Highly Correlated

Abstract Study question Do artificial intelligence (AI) models used to assess embryo viability (based on pregnancy outcomes) also correlate with known embryo quality measures such as Gardner score? Summary answer An AI for embryo viability assessment also correlated with Gardner score, further substantiating the use of AI for assessment and selection of good quality embryos. What is known already The Gardner score consists of three separate components of embryo morphology that are graded individually, then combined to give a final score describing Day 5 embryo (blastocyst) quality. Evidence suggests the Gardner score has some correlation with clinical pregnancy. We hypothesized that an AI model trained to evaluate likelihood of clinical pregnancy based on fetal heartbeat (in clinical use globally) would also correlate with components of the Gardner score itself. We also compared the ability of the AI and Gardner score to predict pregnancy outcomes. Study design, size, duration This study involved analysis of a prospectively collected dataset of single static Day 5 embryo images with associated Gardner scores and AI viability scores. The dataset comprised time-lapse images of 1,485 embryos (EmbryoScope) from 638 patients treated at a single in vitro fertilization (IVF) clinic between November 2019 and December 2020. The AI model was not trained on data from this clinic. Participants/materials, setting, methods Average patient age was 35.4 years. Embryologists manually graded each embryo using the Gardner method, then subsequently used the AI to obtain a score between 0 (predicted non-viable, unlikely to lead to a pregnancy) and 10 (predicted viable, likely to lead to a pregnancy). Correlation between the AI viability score and Gardner score was then assessed. Main results and the role of chance The average AI score was significantly correlated with the three components of the Gardner score: expansion grade, inner cell mass (ICM) grade, and trophectoderm grade. Average AI score generally increased with advancing blastocyst developmental stage. Blastocysts with expansion grades of ≥ 3 are generally considered suitable for transfer. This study showed that embryos with expansion grade 3 had lower AI scores than those with grades 4-6, consistent with a reduced pregnancy rate. AI correlation with trophectoderm grade was more significant than with ICM grade, consistent with studies demonstrating that trophectoderm grade is more important than ICM in determining clinical pregnancy likelihood. The AI predicted Gardner scores of ≥ 2BB with an accuracy of 71.7% (sensitivity 75.1%, specificity 45.9%), and an AUC of 0.68. However, when used to predict pregnancy outcome, the AI performed 27.9% better than the Gardner score (accuracies of 49.8% and 39.0% respectively). Even though the AI was highly correlated with the Gardner score, the improved efficacy for predicting pregnancy suggests that a) the AI provides an advantage in standardization of scoring over the manual and subjective Gardner method, and b) the AI is likely identifying and evaluating morphological features of embryo quality that are not captured by the Gardner method. Limitations, reasons for caution The Gardner score is not a linear score, creating challenges with setting a suitable threshold relating to the prediction of pregnancy. The 2BB treshold was chosen based on literature (Munné et al 2019) and verified by experienced embryologists. This correlative study may also require additional confirmatory studies on independent datasets. Wider implications of the findings The correlation between AI scores and known features of embryo quality (Gardner score) substantiates the use of the AI for embryo assessment. The AI score provides further insight into components of the Gardner score, and may detect morphological features related to clinical pregnancy beyond those evaluated by the Gardner method. Trial registration number Not applicable

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