Development of a dynamic machine learning algorithm to predict clinical pregnancy and live birth rate with embryo morphokinetics.
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ABSTRACT: Objective
To evaluate the feasibility of generating a center-specific embryo morphokinetic algorithm by time-lapse microscopy to predict clinical pregnancy rates.Design
A retrospective cohort analysis.Setting
Academic fertility clinic in a tertiary hospital setting.Patients
Patients who underwent in vitro fertilization with embryos that underwent EmbryoScope time-lapse microscopy and subsequent transfer between 2014 and 2018.Interventions
None.Main outcome measures
Clinical pregnancy.Results
A supervised, random forest learning algorithm from 367 embryos successfully predicted clinical pregnancy from a training set with overall 65% sensitivity and 74% positive predictive value, with an area under the curve of 0.7 for the test set. Similar results were achieved for live birth outcomes. For the secondary analysis, embryo growth morphokinetics were grouped into five clusters using unsupervised clustering. The clusters that had the fastest morphokinetics (time to blastocyst = 97 hours) had pregnancy rates of 54%, whereas a cluster that had the slowest morphokinetics (time to blastocyst = 122 hours) had a pregnancy rate of 71%, although the differences were not statistically significant (P=.356). Other clusters had pregnancy rates of 51%-60%.Conclusions
This study shows the feasibility of a clinic-specific, noninvasive embryo morphokinetic simple machine learning model to predict clinical pregnancy rates.
SUBMITTER: Yang L
PROVIDER: S-EPMC9250114 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveTo determine the best-fit live birth rate per embryo based on maternal age, embryo stage, and embryo morphology.DesignRetrospective data analysis.SettingFertility clinics.PatientsThe patients included were treated with in vitro fertilization in the United States at clinics reporting data to the Society for Assisted Reproductive Technology Clinic Outcomes Reporting System. We analyzed live birth data of unbiopsied autologous cleavage and blastocyst stage embryos for cycles started from 2016 through 2018. The analysis included 223,377 embryo transfers with a total of 336,888 embryos.InterventionsNone.Main outcome measuresLive birth rate per embryo and rate of multiple gestations per pregnancy.ResultsAt the mean maternal age of 34 years, fresh embryos produced live birth rates of 19%, 38%, 26%, and 27% for embryos aged 3, 5, 6, and 7 days, respectively. At the age 34 years, live birth rates for day 5 fresh embryos by overall morphology grade were 43% for good, 30% for fair, and 21% for poor. For the transfer of 2 fresh day 5 blastocysts, the rate of multiple gestations per pregnancy was 47% at 25 years old, 44% at 30 years old, 35% at 35 years old, and 23% at 40 years old.ConclusionsThe analysis of pregnancy data in the Society for Assisted Reproductive Technology database can be used to calculate live birth rates per embryo based on maternal age, embryo age, and morphology. This information can be used for evidence-based decision making, quality control, and planning multicenter studies.
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