AI-guided IV nutrition for premature newborns
Official title: An AI-driven total parenteral nutrition platform for cost-effective and scientifically personalized nutrition for premature newborns
Funded at Takeoff41, INC. · NIH-11335827 · Data updated 2026-11-12
This project will use artificial intelligence to create standardized yet personalized IV nutrition (TPN) for premature babies in neonatal intensive care units.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Takeoff41, INC. NIH-funded |
| Lab location | 1 site (Portola Valley, United States) |
| Project start | September 13, 2024 |
| Project end | August 31, 2027 |
| Funding year | 2025 |
| Award (this year) | $1,061,217 |
| NIH project number | 4R42HD115517-02 |
| Project ID | NIH-11335827 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates are premature newborns in the NICU who require total parenteral nutrition (typically very-low-birthweight or medically unable to feed by mouth).
✕Less likely to help
Full-term babies or infants who do not need intravenous nutrition, and babies whose care requires highly unusual or bespoke formulas, are unlikely to benefit directly.
What this research studies
As a parent, you should know this project builds an AI platform from ten years of clinical data linking TPN formulas to infant outcomes so nutrition can be tailored safely. The team plans to group similar TPN compositions into a manageable set of standardized units that can be centrally manufactured while keeping personalization for each baby. The AI model also uses mother and baby electronic health records to predict risks and suggest appropriate TPN choices. Finally, the platform will be tested in real NICU settings to show it works at least as well as current care.
Why it matters
Potential benefit: If successful, this could lower medication errors, reduce costs, and provide better-tailored nutrition that may improve growth and health outcomes for premature infants.
How similar studies have gone: Prior work exists using EHR-based AI to predict newborn risks, but using AI to create standardized, centrally manufactured TPN units is a novel approach with limited prior clinical testing.
Where this research is happening
-
Takeoff41, INC.
Portola Valley, United States Active
Researchers
- Principal investigator: Phongpreecha, Thanaphong — Takeoff41, INC.
- Study coordinator: Phongpreecha, Thanaphong
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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.