In Fertility and sterility ; h5-index 78.0
OBJECTIVE : To determine whether a machine learning causal inference model can optimize trigger injection timing to maximize the yield of fertilized oocytes (2PNs) and total usable blastocysts for a given cohort of stimulated follicles.
DESIGN : Descriptive and comparative study of new technology.
SETTING : Tertiary academic medical center.
PATIENT(S) : Patients undergoing IVF with intracytoplasmic sperm injection from 2008 to 2019 (n = 7,866).
INTERVENTION(S) : Causal inference was performed with the use of a T-learner. Bagged decision trees were used to perform inference. The decision was framed as either triggering on that day or waiting another day. All patient characteristics and stimulation parameters on a given day were used to determine the recommendation.
MAIN OUTCOME MEASURE(S) : Average outcome improvement in total 2PNs and usable blastocysts compared with the physician's decision.
RESULT(S) : For evaluation of average outcome improvement on 2PNs, the benefit of following the model's recommendation was 3.015 (95% CI 2.626, 3.371) more 2PNs. For total usable blastocysts, the benefit was 1.515 (95% CI 1.134, 1.871) more usable blastocysts. Given that the physicians-model agreement was 52.57% and 61.89%, respectively, algorithm-assisted trigger decisions yield, on average, 1.430 more 2PNs and 0.577 more total usable blastocysts per stimulation. The most important features weighted in the model's decision were the number of follicles 16-20 mm in diameter, the number of follicles 11-15 mm in diameter, and estradiol level, in that order.
CONCLUSION(S) : The use of this machine learning algorithm to optimize trigger injection timing may lead to a significant increase in the number of 2PNs and total usable blastocysts obtained from an IVF stimulation cycle when compared with physician decisions. Future research is required to confirm these findings prospectively.
Hariton Eduardo, Chi Ethan A, Chi Gordon, Morris Jerrine R, Braatz Jon, Rajpurkar Pranav, Rosen Mitchell
Artificial intelligence, IVF, decision support systems, machine learning, predictive analytics