Dataset Information

Time-lapse imaging derived morphokinetic variables reveal association with implantation and live birth following in vitro fertilization: A retrospective study using data from transferred human embryos.

ABSTRACT: The purpose of this retrospective time-lapse data analysis from transferred preimplantation human embryos was to identify early morphokinetic cleavage variables that are related to implantation and live birth following in vitro fertilization (IVF). All embryos were monitored from fertilization check until embryo transfer for a minimum of 44 hours. The study was designed to assess the association between day 2 embryo morphokinetic variables with implantation and live birth based on Known Implantation Data (KID). The kinetic variables were subjected to quartile-based analysis. The predictive ability for implantation and live birth was studied using receiver operator characteristic (ROC) curves. Three morphokinetic variables, time to 2-cells (t2), duration of second cell cycle (cc2) below one threshold and cc2 above another threshold had the highest predictive value with regards to implantation and live birth following IVF treatment. The predictive pre-transfer information has little divergence between fetal heartbeat and live birth data and therefore, at least for early morphokinetic variables up to the four-cell stage (t4), conclusions and models based on fetal heartbeat data can be expected to be valid for live birth datasets as well. The three above mentioned variables (t2, cc2 below one threshold and cc2 above another threshold) may supplement morphological evaluation in embryo selection and thereby improve the outcome of in vitro fertilization treatments.

SUBMITTER: Sayed S

PROVIDER: S-EPMC7676704 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Time-lapse imaging derived morphokinetic variables reveal association with implantation and live birth following in vitro fertilization: A retrospective study using data from transferred human embryos.

Sayed Shabana S Reigstad Marte Myhre MM Petersen Bjørn Molt BM Schwennicke Arne A Wegner Hausken Jon J Storeng Ritsa R

PloS one 20201119 11

The purpose of this retrospective time-lapse data analysis from transferred preimplantation human embryos was to identify early morphokinetic cleavage variables that are related to implantation and live birth following in vitro fertilization (IVF). All embryos were monitored from fertilization check until embryo transfer for a minimum of 44 hours. The study was designed to assess the association between day 2 embryo morphokinetic variables with implantation and live birth based on Known Implanta ...[more]

PMID: 33211770

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Project description:The likelihood of achieving a live birth with repeat in vitro fertilization (IVF) is unclear, yet treatment is commonly limited to 3 or 4 embryo transfers.To determine the live-birth rate per initiated ovarian stimulation IVF cycle and with repeated cycles.Prospective study of 156,947 UK women who received 257,398 IVF ovarian stimulation cycles between 2003 and 2010 and were followed up until June 2012.In vitro fertilization, with a cycle defined as an episode of ovarian stimulation and all subsequent separate fresh and frozen embryo transfers.Live-birth rate per IVF cycle and the cumulative live-birth rates across all cycles in all women and by age and treatment type. Optimal, prognosis-adjusted, and conservative cumulative live-birth rates were estimated, reflecting 0%, 30%, and 100%, respectively, of women who discontinued due to poor prognosis and having a live-birth rate of 0 had they continued.Among the 156,947 women, the median age at start of treatment was 35 years (interquartile range, 32-38; range, 18-55), and the median duration of infertility for all 257,398 cycles was 4 years (interquartile range, 2-6; range, <1-29). In all women, the live-birth rate for the first cycle was 29.5% (95% CI, 29.3%-29.7%). This remained above 20% up to and including the fourth cycle. The cumulative prognosis-adjusted live-birth rate across all cycles continued to increase up to the ninth cycle, with 65.3% (95% CI, 64.8%-65.8%) of women achieving a live birth by the sixth cycle. In women younger than 40 years using their own oocytes, the live-birth rate for the first cycle was 32.3% (95% CI, 32.0%-32.5%) and remained above 20% up to and including the fourth cycle. Six cycles achieved a cumulative prognosis-adjusted live-birth rate of 68.4% (95% CI, 67.8%-68.9%). For women aged 40 to 42 years, the live-birth rate for the first cycle was 12.3% (95% CI, 11.8%-12.8%), with 6 cycles achieving a cumulative prognosis-adjusted live-birth rate of 31.5% (95% CI, 29.7%-33.3%). For women older than 42 years, all rates within each cycle were less than 4%. No age differential was observed among women using donor oocytes. Rates were lower for women with untreated male partner-related infertility compared with those with any other cause, but treatment with either intracytoplasmic sperm injection or sperm donation removed this difference.Among women in the United Kingdom undergoing IVF, the cumulative prognosis-adjusted live-birth rate after 6 cycles was 65.3%, with variations by age and treatment type. These findings support the efficacy of extending the number of IVF cycles beyond 3 or 4.

Project description:In-vitro fertilization (IVF) is a popular method of resolving complications such as endometriosis, poor egg quality, a genetic disease of mother or father, problems with ovulation, antibody problems that harm sperm or eggs, the inability of sperm to penetrate or survive in the cervical mucus and low sperm counts, resulting human infertility. Nevertheless, IVF does not guarantee success in the fertilization. Choosing IVF is burdensome for the reason of high cost and uncertainty in the result. As the complications and fertilization factors are numerous in the IVF process, it is a cumbersome task for fertility doctors to give an accurate prediction of a successful birth. Artificial Intelligence (AI) has been employed in this study for predicting the live-birth occurrence. This work mainly focuses on making predictions of live-birth occurrence when an embryo forms from a couple and not a donor. Here, we compare various AI algorithms, including both classical Machine Learning, deep learning architecture, and an ensemble of algorithms on the publicly available dataset provided by Human Fertilisation and Embryology Authority (HFEA). Insights on data and metrics such as confusion matrices, F1-score, precision, recall, receiver operating characteristic (ROC) curves are demonstrated in the subsequent sections. The training process has two settings Without feature selection and With feature selection to train classifier models. Machine Learning, Deep learning, ensemble models classification paradigms have been trained in both settings. The Random Forest model achieves the highest F1-score of 76.49% in without feature selection setting. For the same model, the precision, recall, and area under the ROC Curve (ROC AUC) scores are 77%, 76%, and 84.60%, respectively. The success of the pregnancy depends on both male and female traits and living conditions. This study predicts a successful pregnancy through the clinically relevant parameters in In-vitro fertilization. Thus artificial intelligence plays a promising role in decision making process to support the diagnosis, prognosis, treatment etc.

Dataset Information

Time-lapse imaging derived morphokinetic variables reveal association with implantation and live birth following in vitro fertilization: A retrospective study using data from transferred human embryos.

Publications

Time-lapse imaging derived morphokinetic variables reveal association with implantation and live birth following in vitro fertilization: A retrospective study using data from transferred human embryos.

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