Predicting placental causes of stillbirth with machine learning
Official title: Building machine learning models to identify placental causes of stillbirth
Funded at Northwestern University · NIH-11408476 · Data updated 2026-11-12
A team at Northwestern University is building AI tools to help doctors find placental explanations for stillbirth in pregnancies that end in loss.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Northwestern University NIH-funded |
| Lab location | 1 site (Chicago, United States) |
| Project start | September 1, 2025 |
| Project end | May 31, 2030 |
| Funding year | 2026 |
| Award (this year) | $547,187 |
| NIH project number | 5R01HD116699-02 |
| Project ID | NIH-11408476 on NIH RePORTER |
Who could benefit from this research
✓Could help
Parents and pregnancies at risk of stillbirth who have placental tissue data or related clinical information available.
✕Less likely to help
Pregnancies without accessible placental pathology or where histopathology does not contribute to the diagnosis.
What this research studies
This project will gather clinical, obstetric, and pathology data from more than 1,500 pregnancies that ended in stillbirth and apply machine learning to identify placental histopathology patterns linked to cause of death. Expert pathologists and maternal-fetal medicine specialists will work together to classify the demise and guide model testing. The goal is to make placental diagnoses more accessible and timely, potentially improving understanding and management of stillbirth risk in the future. Results could help clinicians use placental findings to inform care in subsequent pregnancies and refine diagnostic pathways.
Why it matters
Potential benefit: If successful, the models could assist clinicians in identifying placental causes of stillbirth more quickly and accurately, potentially informing prevention and management in future pregnancies.
How similar studies have gone: Previous work shows histopathology is highly informative for stillbirth, and AI approaches are increasingly used to interpret pathology data.
Where this research is happening
-
Northwestern University
Chicago, United States Active
Researchers
- Principal investigator: Goldstein, Jeffery a — Northwestern University
- Study coordinator: Goldstein, Jeffery a
Related research
Other NIH-funded projects on the same topics.
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Stillbirth Data Hub: Coordinating Center for Big-Data and AI in Stillbirth Research
A national data hub is being built to coordinate data, biorepository, and analytics for stillbirth research using advanced AI and machine learning.
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Genetic roots of stillbirth and developmental disorders
A genetic study from Massachusetts General Hospital will look for inherited and new DNA changes that contribute to stillbirth and related developmental disorders in families.
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Innovative Data Core for Stillbirth Research Center
A data and software core is being built to harmonize and analyze information from multiple sites to uncover why stillbirth happens and to support future prevention.
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Administrative Core for Stillbirth Research Coordinating Center
A coordination core is being funded to organize and manage data, collaborations, and administration to help stillbirth research move forward.
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Stillbirth Data Coordinating Center focusing on AI-powered insights
A national data hub is using advanced AI and machine learning to analyze stillbirth data and help researchers find patterns that could prevent or understand stillbirth.
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NOURISH: nutrition and health factors linked to pregnancy outcomes and stillbirth risk
Aimed at understanding how nutrition and stress-related health factors affect placental function to prevent stillbirths in pregnancies.
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.