Machine-learning algorithm incorporating capacitated sperm intracellular pH predicts conventional in vitro fertilization success in normospermic patients.

ABSTRACT:

Objective

To measure human sperm intracellular pH (pH_i) and develop a machine-learning algorithm to predict successful conventional in vitro fertilization (IVF) in normospermic patients.

Design

Spermatozoa from 76 IVF patients were capacitated in vitro. Flow cytometry was used to measure sperm pH_i, and computer-assisted semen analysis was used to measure hyperactivated motility. A gradient-boosted machine-learning algorithm was trained on clinical data and sperm pH_i and membrane potential from 58 patients to predict successful conventional IVF, defined as a fertilization ratio (number of fertilized oocytes [2 pronuclei]/number of mature oocytes) greater than 0.66. The algorithm was validated on an independent set of data from 18 patients.

SUBMITTER: Gunderson SJ 

PROVIDER: S-EPMC9110269 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature