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Hematologic markers and machine learning in predicting placenta accreta: A case-control study.

Michael D Jochum1, Kelly D Albrecht1, Yamely Mendez Martinez1

  • 1Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Baylor College of Medicine and Texas Children's Hospital, Houston, Texas, USA.

International Journal of Gynaecology and Obstetrics: the Official Organ of the International Federation of Gynaecology and Obstetrics
|February 2, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning models accurately detect placenta accreta spectrum (PAS) and predict severe hemorrhage using patient history, imaging, and hematologic markers. These tools improve antenatal diagnosis, leading to better maternal outcomes through early identification and resource allocation.

Keywords:
antenatal diagnosishematologic markershemorrhagemachine learningplacenta accreta spectrumquantitative blood lossultrasound imaging

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Area of Science:

  • Obstetrics and Gynecology
  • Maternal-Fetal Medicine
  • Medical Imaging
  • Machine Learning in Healthcare

Background:

  • Placenta accreta spectrum (PAS) poses significant risks during pregnancy, often leading to severe hemorrhage.
  • Accurate antenatal detection of PAS is crucial for improving maternal outcomes and managing delivery complications.
  • Current diagnostic methods can be improved by integrating diverse data sources.

Purpose of the Study:

  • To enhance antenatal detection of placenta accreta spectrum (PAS).
  • To predict severe hemorrhage at delivery using machine learning.
  • To evaluate the association between antenatal hematologic index trends, imaging markers, and patient history with PAS and hemorrhage.

Main Methods:

  • Retrospective analysis of 2017-2023 data from a PAS referral center.
  • Comparison of confirmed PAS cases with controls lacking histopathologic PAS evidence.
  • Development of machine learning models to predict PAS and severe hemorrhage using demographics, laboratory results, ultrasounds, and patient history.

Main Results:

  • Machine learning models achieved high accuracy in predicting PAS (up to 90%) and severe hemorrhage (74.3%).
  • Prior cesarean deliveries and second/third trimester ultrasound markers were strong predictors of PAS.
  • Third trimester mean platelet volume showed an inverse association with PAS.

Conclusions:

  • Machine learning models integrating patient history, imaging, and hematologic markers effectively detect PAS and predict hemorrhage.
  • These predictive tools enhance antenatal diagnosis of PAS, enabling better resource allocation and improved maternal outcomes.
  • Early identification of PAS through advanced analytics can significantly mitigate delivery-related risks.