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Development of a dynamic machine learning algorithm to predict clinical pregnancy and live birth rate with embryo
Liubin Yang1, Mary Peavey1, Khalied Kaskar1
1Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Baylor College of Medicine, Huston, Texas.
F&S Reports
|July 5, 2022
Summary
A machine learning model using embryo morphokinetics from time-lapse microscopy can predict clinical pregnancy rates. This noninvasive approach shows feasibility for clinic-specific algorithms in fertility treatments.
Area of Science:
- Reproductive Medicine
- Embryology
- Artificial Intelligence in Healthcare
Background:
- Time-lapse microscopy (TLM) provides continuous embryo development monitoring.
- Predicting clinical pregnancy from embryo development is crucial for in vitro fertilization (IVF) success.
- Developing clinic-specific predictive algorithms can enhance IVF outcomes.
Purpose of the Study:
- To assess the feasibility of creating a center-specific embryo morphokinetic algorithm using TLM.
- To predict clinical pregnancy rates based on embryo development patterns.
- To evaluate the noninvasive prediction of IVF success.
Main Methods:
- Retrospective cohort analysis of IVF patients (2014-2018).
- Utilized EmbryoScope TLM data for embryo morphokinetics.
- Developed a supervised random forest learning algorithm for prediction.
Main Results:
- The algorithm predicted clinical pregnancy with 65% sensitivity and 74% positive predictive value (AUC 0.7).
- Similar predictive accuracy was observed for live birth outcomes.
- Secondary analysis showed varied pregnancy rates across morphokinetic clusters, though not statistically significant.
Conclusions:
- A clinic-specific, noninvasive machine learning model for embryo morphokinetics is feasible.
- This approach can aid in predicting clinical pregnancy rates in IVF.
- Embryo morphokinetic analysis offers a valuable tool for fertility treatment optimization.
