A tool for analyzing skull shape in children with craniosynostosis
Official title: CranioRate: An imaging-based, deep-phenotyping analysis toolset, repository, and online clinician interface for craniosynostosis
Funded at University of Utah · NIH-10893926 · Data updated 2026-11-12
This study is working on a new tool that uses advanced technology to help doctors better understand and measure skull shape issues in babies with craniosynostosis, so they can improve treatment and outcomes for your little one.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Utah NIH-funded |
| Lab location | 1 site (Salt Lake City, United States) |
| Project start | September 1, 2023 |
| Project end | August 31, 2028 |
| Funding year | 2024 |
| Award (this year) | $627,642 |
| NIH project number | 5R01DE032366-02 |
| Project ID | NIH-10893926 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are infants diagnosed with metopic craniosynostosis or other forms of craniosynostosis requiring surgical intervention.
✕Less likely to help
Patients with craniosynostosis who are not eligible for surgery or those with other unrelated cranial conditions may not benefit from this research.
What this research studies
This research focuses on developing an advanced imaging tool that uses machine learning to analyze and quantify skull shape abnormalities in infants with craniosynostosis, a condition where skull sutures fuse too early. By utilizing CT scans and 3D photography, the project aims to create a comprehensive database and online interface for clinicians to better assess and manage the condition. The goal is to improve surgical outcomes and prevent complications such as neurocognitive impairment and visual disturbances. This innovative approach seeks to provide objective measurements that can enhance clinical decision-making.
Why it matters
Potential benefit: If successful, this research could lead to improved diagnosis and treatment strategies for infants with craniosynostosis, ultimately enhancing their quality of life.
How similar studies have gone: Previous research has shown success in using machine learning for medical imaging analysis, indicating a promising potential for this novel approach.
Where this research is happening
-
University of Utah
Salt Lake City, United States Active
Researchers
- Principal investigator: Elhabian, Shireen Youssef — University of Utah
- Study coordinator: Elhabian, Shireen Youssef
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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.