AI-based analysis of fetal growth restriction in a prospective obstetric cohort quantifies compound risks for perinatal morbidity and mortality and identifies previously unrecognized high risk clinical scenarios
PGMs are able to capture and quantify context-specific risk relationships in FGR and identify latent variable interactions that are associated with large differences in risk. FGR scenarios that are separated by nearly 10-fold perinatal morbidity risk would be managed similarly under current FGR clinical guidelines, highlighting the need for more precise approaches to risk estimation in FGR.
Authors
- Edgar Javier Hernandez (ORCID: https://orcid.org/0000-0003-1455-8251)
- Jon G. Steller (ORCID: https://orcid.org/0000-0002-2164-1080)
- Nathan R. Blue (ORCID: https://orcid.org/0000-0003-2187-3428)
- George A. Saade
- David M. Haas (ORCID: https://orcid.org/0000-0002-8379-0743)
- Martin Tristani‐Firouzi (ORCID: https://orcid.org/0000-0001-5916-442X)
- Robert M. Silver (ORCID: https://orcid.org/0000-0002-5794-3152)
- William A. Grobman
- Raquel M. Zimmerman
- Mark Yandell
Institutions
- Eastern Virginia Medical School (US)
- University of California, Irvine (US)
- University of Utah (US)
- Brown University (US)
- Utah Department of Health (US)
- Indiana University – Purdue University Indianapolis (US)
Publication Details
- Journal
- BMC Pregnancy and Childbirth
- Published
- 2025-01-29
- DOI
- https://doi.org/10.1186/s12884-024-07095-6
- Citations
- 6
- Primary Topic
- Birth, Development, and Health
- Type
- article
- Field-Weighted Citation Impact
- 20.96
Funders
- R Baby Foundation
- Eunice Kennedy Shriver National Institute of Child Health and Human Development