Deep Learning to Improve Interpretation of Intrapartum Fetal Monitoring
Funded at Dartmouth-Hitchcock Clinic · NIH-11402250 · Data updated 2026-11-12
A data-driven AI approach is being developed to help clinicians interpret fetal monitoring during labor to improve decisions in delivery care.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Dartmouth-Hitchcock Clinic NIH-funded |
| Lab location | 1 site (Lebanon, United States) |
| Project start | August 25, 2025 |
| Project end | April 30, 2030 |
| Funding year | 2026 |
| Award (this year) | $591,792 |
| NIH project number | 5R01HD119210-02 |
| Project ID | NIH-11402250 on NIH RePORTER |
Who could benefit from this research
✓Could help
Pregnant individuals undergoing labor with electronic fetal monitoring at participating sites who consent to data use and potential future integration of AI-assisted interpretations.
✕Less likely to help
Patients whose labor outcomes are not influenced by interpretation changes or who are not managed with electronic fetal monitoring may not benefit.
What this research studies
This project aims to use a novel deep learning method to extract meaningful patterns from electronic fetal monitoring data that may be missed by traditional interpretation. By focusing on intrapartum signals, the researchers hope to better identify when interventions are needed to protect the fetus. The work builds on decades of EFM use and attempts to translate complex signals into clearer guidance for obstetric teams. If successful, the method could reduce unnecessary cesarean deliveries while safeguarding neonatal outcomes. The study is conducted with patients at a large teaching hospital, using real-world obstetric data to train and test the model.
Why it matters
Potential benefit: If successful, the approach could lead to more accurate fetal assessment during labor, potentially reducing unnecessary cesareans and improving neonatal outcomes.
How similar studies have gone: Prior software tools for EFM interpretation have not shown clear clinical benefit in trials, making this AI-driven approach novel and promising but not yet proven.
Where this research is happening
-
Dartmouth-Hitchcock Clinic
Lebanon, United States Active
Researchers
- Principal investigator: Mccoy, Jennifer — Dartmouth-Hitchcock Clinic
- Study coordinator: Mccoy, Jennifer
Related research
Other NIH-funded projects on the same topics.
-
Integrative histo-genomic analysis to guide cancer risk and targeted therapies with interpretable AI
Researchers are developing an advanced AI-powered approach to map genetic and cellular patterns across breast and cervical cancers to help tailor treatments.
-
Predicting how antimalarial drug resistance spreads with AI
A powerful AI model will be built to forecast how resistance to antimalarial drugs might spread in Africa and beyond, helping clinicians and public health teams stay ahead of outbreaks.
-
Self-supervised deep learning for biological sequences
A deep learning project uses self-supervised methods to better understand biological sequences and related data, with the goal of advancing biology and medicine.
-
Mobile phone imaging and AI to screen oral cancers in low-resource settings
A low-cost mobile phone device with artificial intelligence is being developed to help frontline health workers screen for oral cancer early in low-resource areas.
-
Linking Genetic Variants to Real-Life Health Effects with Smart AI
A new AI-powered approach will predict how genetic differences affect health across multiple levels to help doctors understand diseases and tailor care.
-
Integrative AI tools to map protein sequences to structures and functions
A scientist team will develop advanced AI methods to better connect protein sequences, their 3D shapes, and how they work, using large biological data from sequencing and structures.
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.