Hematologic markers and machine learning in predicting placenta accreta: A case–control study
Machine learning models combining patient history, imaging, and hematologic markers detect PAS and predict hemorrhage with up to 90% accuracy. These tools improve antenatal diagnosis of PAS, which enhances maternal outcomes by enabling early identification and better resource allocation.
Authors
- Brian Burnett (ORCID: https://orcid.org/0009-0000-1601-3517)
- Christina C. Reed
- Jessian Louis Munoz (ORCID: https://orcid.org/0000-0003-0431-5280)
- K. D. Fox
- Kelly Albrecht
- M. A. Belfort
- Michael D. Jochum
- Sanmay Sarada
- Yamely Mendez Martinez
- Victoria Zhang
- Amir A. Shamshirsaz
Institutions
- Texas Children's Hospital (US)
- The University of Texas Medical Branch at Galveston (US)
Publication Details
- Journal
- International Journal of Gynecology & Obstetrics
- Published
- 2026-02-02
- DOI
- https://doi.org/10.1002/ijgo.70782
- Primary Topic
- Maternal and fetal healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00