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

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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    References listed on IDEAS

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    1. Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
    2. Suraj Rajendran & Matthew Brendel & Josue Barnes & Qiansheng Zhan & Jonas E. Malmsten & Pantelis Zisimopoulos & Alexandros Sigaras & Kwabena Ofori-Atta & Marcos Meseguer & Kathleen A. Miller & David H, 2024. "Automatic ploidy prediction and quality assessment of human blastocysts using time-lapse imaging," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
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