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

In normospermic patients, sperm intracellular pH (pHi) positively correlated with conventional in vitro fertilization (IVF) success rates, and a machine-learning algorithm incorporating sperm pHi accurately predicted successful conventional IVF.

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VOLUME 115, ISSUE 4, P930-939

Authors:

Stephanie Jean Gunderson, M.D., Lis Carmen Puga Molina, Ph.D., Nicholas Spies, M.D., Paula Ania Balestrini, B.S., Mariano Gabriel Buffone, Ph.D., Emily Susan Jungheim, M.D., M.S.C.I., Joan Riley, Ph.D., Celia Maria Santi, M.D., Ph.D. 

Abstract:

Objective

To measure human sperm intracellular pH (pHi) 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 pHi, and computer-assisted semen analysis was used to measure hyperactivated motility. A gradient-boosted machine-learning algorithm was trained on clinical data and sperm pHi 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.


Setting

Academic medical center.


Patient(s)

Normospermic men undergoing IVF. Patients were excluded if they used frozen sperm, had known male factor infertility, or used intracytoplasmic sperm injection only.


Intervention(s)

None.


Main Outcome Measure(s)

Successful conventional IVF.


Result(s)

Sperm pHi positively correlated with hyperactivated motility and with conventional IVF ratio (n = 76) but not with intracytoplasmic sperm injection fertilization ratio (n = 38). In receiver operating curve analysis of data from the test set (n = 58), the machine-learning algorithm predicted successful conventional IVF with a mean accuracy of 0.72 (n = 18), a mean area under the curve of 0.81, a mean sensitivity of 0.65, and a mean specificity of 0.80.


Conclusion(s)

Sperm pHi correlates with conventional fertilization outcomes in normospermic patients undergoing IVF. A machine-learning algorithm can use clinical parameters and markers of capacitation to accurately predict successful fertilization in normospermic men undergoing conventional IVF.

Fertility and Sterility

Editorial Office, American Society for Reproductive Medicine

Fertility and Sterility® is an international journal for obstetricians, gynecologists, reproductive endocrinologists, urologists, basic scientists and others who treat and investigate problems of infertility and human reproductive disorders. The journal publishes juried original scientific articles in clinical and laboratory research relevant to reproductive endocrinology, urology, andrology, physiology, immunology, genetics, contraception, and menopause. Fertility and Sterility® encourages and supports meaningful basic and clinical research, and facilitates and promotes excellence in professional education, in the field of reproductive medicine.