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

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. Sim

SUBMITTER: Yang L 

PROVIDER: S-EPMC9250114 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature