P–228 AI-based assessment of embryo viability correlates with features of embryo ploidy

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
M VerMilyea ◽  
S Diakiw ◽  
J Hall ◽  
M Dakka ◽  
T Nguyen ◽  
...  
Keyword(s):  
Pregnancy Outcome ◽  
Confidence Score ◽  
Linear Increase ◽  
Morphological Features ◽  
Image Correlation ◽  
Average Score ◽  
Embryo Viability ◽  
Viability Assessment ◽  
Ploidy Status ◽  
Viability Score

Abstract Study question Do AI models used to assess embryo viability (based on pregnancy outcome) also correlate with known embryo quality measures such as ploidy status? Summary answer An AI for embryo viability assessment correlated with ploidy status, and with karyotypic features of aneuploidy, supporting its use for embryo selection. What is known already One factor that can influence pregnancy success is the genetic status of the embryo. PGT-A is commonly used to test for embryo ploidy, with the aim of identifying karyotypically normal embryos (euploid embryos), for preferential transfer. There is evidence suggesting that transfer of euploid embryos produces favorable clinical outcomes over aneuploid embryos. Given the AI model was trained to evaluate clinical pregnancy, it was hypothesized that the score might also correlate with ploidy status, and with different types of aneuploidies. Little is known about morphological correlations with embryo ploidy status, so we also sought to explore this relationship. Study design, size, duration This study involved analysis of a retrospective dataset of single static Day 5 embryo (blastocyst) images with associated PGT-A results and AI viability scores. The dataset comprised images of 5,469 embryos from 2,615 consecutive patients treated at five US IVF clinics between February 2015 and April 2020. The AI was trained on thousands of Day 5 embryo images from multiple IVF laboratories in multiple countries, but was not trained on data used in this study. Participants/materials, setting, methods Average patient age was 36.2 years, and average embryo cohort size was 2.1/patient. PGT-A analysis was performed on embryos at time of evaluation. The dataset comprised 3,251 (59.4%) euploid embryos, 1,815 (33.2%) aneuploid embryos, and 403 (7.4%) mosaic embryos. The AI was retrospectively used to provide a score between 0 (predicted non-viable) and 10 (predicted viable) for each image. Correlation between the AI viability score and euploid, mosaic and aneuploid embryos was then assessed. Main results and the role of chance Results showed a statistically significant correlation between AI viability score and PGT-A outcome, consistent with a relationship between pregnancy outcome and ploidy status. The average score for euploid embryos was 8.20, which was significantly higher than the average score for aneuploid embryos of 7.80 (p < 0.0001). There was a significant linear increase in confidence score from full aneuploid embryos, through mosaic embryos (average score 7.97), to full euploid embryos (mosaic threshold of 20–80%). High mosaic embryos tended to have a lower average score (7.60) than low mosaic embryos (7.96), consistent with correlation of viability (pregnancy outcome) with the degree of mosaicism. AI viability score also correlated with ploidy features believed to affect pregnancy outcomes. Trisomic changes had higher average scores than monosomic changes. Segmental changes had higher average scores than full gain or loss. The AI score differentiated euploid from aneuploid status more efficiently in embryos with poorer morphology than those with good morphology. Whilst there was an evident correlation between pregnancy outcome and ploidy status, the AI was only weakly predictive of euploidy, with an accuracy of 57.3% using an AI viability score threshold of 7.5/10.This suggests pregnancy-related morphological features are somewhat correlated with embryo ploidy, but not completely. Limitations, reasons for caution The PGT-A technique is held to have some limitations for evaluating ploidy status, therefore it would be of benefit to perform additional confirmatory studies on independent datasets. It would be of interest to conduct prospective studies evaluating correlations between the AI’s evaluation of morphology and pregnancy outcome with ploidy status. Wider implications of the findings: The AI score correlated with genetic features of embryos that are known to correlate with pregnancy, which further supports the efficacy and use of AI for embryo viability assessment. The AI identified morphological features that are somewhat predictive of ploidy status, with potential application to embryos of poorer Gardner score. Trial registration number none

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

scholarly journals Modification of the methodology for ssessing the viability of introduced species in accordance with the conditions of dry steppe zone of the Orenburg region

2021 ◽  
Vol 54 (2) ◽  
pp. 57-62
Author(s):  
Daria G. Fedorova ◽  
Natalia M. Nazarova ◽  
Yulia F. Kuhlevskaya
Keyword(s):  
Steppe Zone ◽  
Climatic Conditions ◽  
Limiting Factors ◽  
Seasonal Development ◽  
Average Score ◽  
Introduced Plants ◽  
Dry Steppe ◽  
Height Increase ◽  
Observation Period ◽  
Viability Score

Abstract. The work was carried out to modify the method of assessing the viability of plants, taking into account the limiting factors of the climatic conditions of the Orenburg Preduralie. Are studied several species of plants, during the introduction at the steppe zone (on example of Оrenburg). Introduction study of all taxons was carried out for 8 years (20122020). The results of visual and laboratory observations of the seasonal development of species. During the entire observation period such indicators, as lignification of shoots, heat resistance, drought resistance, winter hardiness, shoot-forming capacity, height increase, generative development, and possible ways of reproduction in culture were evaluated annually. For each indicator numerical values in points corresponding to a certain state of the plant were selected. Based on the integrated assessment, the total viability score was calculated separately for each year of observations and the average score for the observation period. The sum of the average scores is an integral numerical expression of the viability of the introduced plants. Was established that the studied species belong to the I and II group relatively the criterion of life skills. The most species and sorts are among the most promising plants for introduction.

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 Clinical Ability, Learning-Attitudes, and Self-Confidence among Nursing Students in Their First High-Fidelity Simulation Training

2017 ◽  
Vol 2 (3) ◽  
Keyword(s):  
Clinical Practice ◽  
Nursing Students ◽  
Simulation Training ◽  
Confidence Score ◽  
Practical Experience ◽  
Average Score ◽  
Training Methods ◽  
Self Confidence ◽  
Learning Attitudes ◽  
Simulation Scenario

Objectives: This is a descriptive study to investigate the clinical ability, learning attitudes, and self-confidence in nursing students after simulation training. Methods: The participants of this study were 54 third-year nursing students in a city in Korea who had never received simulation-based education. Simulation training was conducted during the ‘adult nursing practice’ curriculum in the third year at the beginning of clinical practice, during the 10 hours of ‘surgical system nursing’. The topic of the simulation scenario was ‘nursing care for patients after abdominal operations’. Data were analyzed with SPSS 22.0, using mean, standard deviation, and percentage. Results: The results of the simulation training showed that all the groups were able to perform the ‘hand washing’ items for the clinical ability. In addition, the average score of learning-attitude after simulation training was 4.0 points (out of 5 points). Among the average scores, ‘recognition of my weaknesses and strengths’ averaged 4.4 points, ‘active discussions and opinions shared through debriefing’ averaged 4.3 points, and the lowest scoring item, ‘decreased anxiety in clinical practice’, averaged 3.4 points. The confidence score after the simulation training was 6.5 (out of 10). Conclusions: If simulation training were carried out continuously rather than once, it could help nursing students have confidence in learning attitudes and patient care. In order for nursing students to have practical experience with clinical situations, simulation training needs to be continuous.

843 Improving Practical Skills Training with In-Situ Trauma Simulation

2021 ◽  
Vol 108 (Supplement_2) ◽  
Author(s):  
R Fisher ◽  
A Nambiar ◽  
R Subramanian
Keyword(s):  
Training Session ◽  
Skills Training ◽  
Confidence Score ◽  
Average Score ◽  
Trauma Patients ◽  
Practical Skills ◽  
In Situ Simulation ◽  
Before And After ◽  
Average Confidence

Abstract Introduction Safe and effective management of trauma patients requires numerous practical skills. Our in-situ trauma simulation identified key areas requiring increased training and exposure. This enabled improvement to education, patient safety and efficiency when managing these emergencies. Method We carried out a simulated trauma call according to ATLS principles, recording the time and person completing each task. Key areas for improvement were identified; most notably the application of Femoral Traction Splints (FTS). 0/7 doctors present were not able to do this. Subsequently, a formal training day was delivered, with 38 attendees across specialties, assessing confidence before and after the session. Results Prior to the training session, 52.6% of attendees did not have formal teaching using FTS and 65.8% had never used one. Confidence with FTS application was measured on a scale of 1 (not confident) to 5 (very confident), with an average score of 2.6/5. After training, the average confidence score was 4.7/5 (p < 0.01). 100% of participants found the session very useful. Conclusions In-situ simulation allows identification of key areas for improvement in training of practical skills. Essential tailored teaching can then be delivered to increase exposure and confidence for these necessary practical skills.

P–247 Application of deep learning for automated measurement of key morphological features of human zygotes for IVF

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
A Le ◽  
I Miyatsuka ◽  
J Otsuki ◽  
M Shiotani ◽  
N Enatsu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Confidence Score ◽  
Time Lapse ◽  
Morphological Features ◽  
Training Dataset ◽  
Percentage Error ◽  
Male And Female ◽  
The Mean ◽  
Over Time ◽  
Human Zygotes

Abstract Study question Can deep learning (DL) algorithms trained on time-lapse videos be used to detect and track the size and gender of pronuclei in developing human zygotes? Summary answer Our DL algorithm not only outperforms state-of-the-art models in detecting the pronuclei but can also accurately identify and track its gender and size over time. What is known already Recent researches have explored the use of DL to extract key morphological features of human embryos. Existing studies, however, focus either on blastocysts’ morphological measurements (Au et al. 2020) or on embryos’ general developmental stages classification (Gingold et al. 2018, Liu et al. 2019, Lau et al. 2019). So far, only one paper attempted to evaluate zygotes’ morphological components but stopped short of identifying the existence and location of their pronuclei (Leahy et al. 2020). We address this research gap by training a DL model that can detect, classify the gender, and quantify the size of zygotes’ pronuclei over time. Study design, size, duration A retrospective analysis using 91 fertilized oocytes from infertile patients undergoing IVF or ICSI treatment at Hanabusa Women’s Clinic between January 2011 and August 2019 was conducted. Each embryo was time-lapse monitored using Vitrolife which records an image every 15 minutes at 7 focal planes. For our study, we used videos of the first 1–2 days of the embryo from its 3 central focal planes, corresponding to 70–150 images per focal plane. Participants/materials, setting, methods All 273 timelapse videos were split into 30,387 grayscale still images at a 15-minute interval. Each image was checked and annotated by experienced embryologists where every pixel of the image was classified into 3 categories: male pronuclei, female pronuclei, and others. Images were converted into grayscale, resized into 500x500 pixels, and then fed into a neural network with the Mask R-CNN architecture and a ResNet101 backbone to produce a pronuclei instance segmentation model. Main results and the role of chance The 91 embryos were split into training (∼70% or 63 embryos) and validation (∼30% or 28 embryos). Our pronuclei model takes as input a single image and outputs a bounding box, mask, category, confidence score, and size measured in terms of pixel for each detected candidate. For prediction, we run the model on the 3 middle focal planes and merge candidates by using the one with the highest confidence score. We used the mean-average precision (mAP) score to evaluate our model’s ability to detect pronuclei and used the mean absolute percentage error (MAPE) between the actual size (as annotated by the embryologist) and the predicted one to check the model’s performance in tracking the pronuclei’s size. The mAP for detecting pronuclei, regardless of its gender, achieved by our model was 0.698, higher than the 0.680 value reported in the Leahy et al. paper (2020). Breakdown by gender, our model’s mAP for male and female pronuclei are 0.734 and 0.661 respectively. The overall MAPE for tracking pronuclei’s size is 21.8%. Breakdown by gender, our model’s MAPE for male and female pronuclei are 19.4% and 24.3% respectively. Limitations, reasons for caution Samples were collected from one clinic with videos recorded from one time-lapse system which can limit our results’ reproducibility. The accuracy of our DL model is also limited by the small number of embryos that we used. Wider implications of the findings: Even with a limited training dataset, our results indicate that we can accurately detect and track the gender and the size of zygotes’ pronuclei using time-lapse videos. In future models, we will increase our training dataset as well as include other time-lapse systems to improve our models’ accuracy and reproducibility. Trial registration number Not applicable

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