Author
Listed:
- 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)
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
Suggested Citation
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 A, 2025.
"Machine learning center-specific models show improved IVF live birth predictions over US national registry-based model,"
Nature Communications, Nature, vol. 16(1), pages 1-14, December.
Handle:
RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58744-z
DOI: 10.1038/s41467-025-58744-z
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