Fertility and sterility · 2021
Machine-learning algorithm incorporating capacitated sperm intracellular pH predicts conventional in vitro fertilization success in normospermic patients.
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Gunderson Stephanie Jean, Puga Molina Lis Carmen, Spies Nicholas, Balestrini Paula Ania, Buffone Mariano Gabriel, Jungheim Emily Susan, Riley Joan, Santi Celia Maria
Аннотация
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.
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.