Development of a dynamic machine learning algorithm to predict clinical pregnancy and live birth rate with embryo morphokinetics

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. Patient(s): Patients who underwent in vitro fertilization with embryos that underwent EmbryoScope time-lapse microscopy and subsequent transfer between 2014 and 2018. Intervention(s): None. Main Outcome Measure(s): Clinical pregnancy. Result(s): 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%. Conclusion(s): This study shows the feasibility of a clinic-specific, noninvasive embryo morphokinetic simple machine learning model to predict clinical pregnancy rates.