In Fertility and sterility ; h5-index 78.0
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.
Gunderson Stephanie Jean, Puga Molina Lis Carmen, Spies Nicholas, Balestrini Paula Ania, Buffone Mariano Gabriel, Jungheim Emily Susan, Riley Joan, Santi Celia Maria
Human sperm, capacitation, conventional IVF, intracellular pH, machine learning