Foundations for next-generation intrapartum decision support using smart, real-time fetal monitoring
Official title: Foundations for Next-Generation Intrapartum Decision Support via Fusion of Selective State-Space Models
Funded at State University New York Stony Brook · NIH-11434387 · Data updated 2026-11-12
A new method will combine advanced statistical models to give real-time, trusted estimates of fetal well-being during labor for use by clinicians and families.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | State University New York Stony Brook NIH-funded |
| Lab location | 1 site (Stony Brook, United States) |
| Project start | September 15, 2026 |
| Project end | August 31, 2031 |
| Funding year | 2026 |
| Award (this year) | $677,340 |
| NIH project number | 1R01HD123035-01 |
| Project ID | NIH-11434387 on NIH RePORTER |
Who could benefit from this research
✓Could help
Pregnant individuals in labor and their newborns at risk of delivery complications where electronic fetal monitoring is used.
✕Less likely to help
Patients not experiencing delivery complications or not using fetal monitoring may see no direct benefit.
What this research studies
This project aims to improve electronic fetal monitoring by delivering calibrated, real-time probabilities of fetal well-being with clear explanations for clinicians at the bedside. It uses selective state-space models to process long streams of fetal heart rate and uterine activity data quickly, even when inputs are noisy or incomplete. The approach focuses on making the information interpretable and actionable so decisions during delivery can be more precise and timely. The work is designed to fit into the delivery room workflow without adding excessive computational burden. If successful, it could reduce unnecessary interventions and improve neonatal outcomes by supporting more informed, rapid decisions.
Why it matters
Potential benefit: If successful, it could enhance the safety of childbirth by providing trustworthy, real-time guidance to clinicians.
How similar studies have gone: Early work suggests probabilistic monitoring can improve interpretation, but this approach seeks stronger calibration and interpretability in real time.
Where this research is happening
-
State University New York Stony Brook
Stony Brook, United States Active
Researchers
- Principal investigator: Djuric, Petar M — State University New York Stony Brook
- Study coordinator: Djuric, Petar M
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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.