Machine learning center-specific models show improved IVF live birth predictions over US national registry-based model

Mylene W. M. Yao (), Elizabeth T. Nguyen, Matthew G. Retzloff, L. April Gago, John E. Nichols, John F. Payne, Barry A. Ripps, Michael Opsahl, Jeremy Groll, Ronald Beesley, Gregory Neal, Jaye Adams, Lorie Nowak, Trevor Swanson and Xiaocong Chen
Additional contact information
Mylene W. M. Yao: Univfy
Elizabeth T. Nguyen: Univfy
Matthew G. Retzloff: Fertility Center of San Antonio
L. April Gago: Gago Center for Fertility
John E. Nichols: Piedmont Reproductive Endocrinology Group
John F. Payne: Piedmont Reproductive Endocrinology Group
Barry A. Ripps: NewLIFE Fertility
Michael Opsahl: Poma Fertility
Jeremy Groll: SpringCreek Fertility
Ronald Beesley: Poma Fertility
Gregory Neal: Fertility Center of San Antonio
Jaye Adams: Fertility Center of San Antonio
Lorie Nowak: SpringCreek Fertility
Trevor Swanson: Univfy
Xiaocong Chen: Univfy

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract Expanding in vitro fertilization (IVF) access requires improved patient counseling and affordability via cost-success transparency. Clinicians ask how two types of live birth prediction (LBP) models perform: machine learning, center-specific (MLCS) models and the multicenter, US national registry-based model produced by Society for Assisted Reproductive Technology (SART). In a retrospective model validation study, we tested whether MLCS performs better than SART using 4635 patients’ first-IVF cycle data from 6 centers. MLCS significantly improved minimization of false positives and negatives overall (precision recall area-under-the-curve) and at the 50% LBP threshold (F1 score) compared to SART (p

Date: 2025
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DOI: 10.1038/s41467-025-58744-z

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