Predicting Children's Facial Growth: Building a Landmark Database and Individualized Models
Official title: Predicting Human Facial Growth: Building a Landmark Database and Clinically Applicable, Individualized Prediction Models
Funded at University of the Pacific-Stockton · NIH-11424818 · Data updated 2026-11-12
Aimed at predicting how a person’s face grows over time using a big landmark database and smart computer models to guide orthodontic care.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of the Pacific-Stockton NIH-funded |
| Lab location | 1 site (Stockton, United States) |
| Project start | September 8, 2026 |
| Project end | August 31, 2030 |
| Funding year | 2026 |
| Award (this year) | $134,581 |
| NIH project number | 1R16DE036385-01A1 |
| Project ID | NIH-11424818 on NIH RePORTER |
Who could benefit from this research
✓Could help
Orthodontic patients around 18 years old or nearing skeletal maturity who are undergoing planning for facial growth and dentofacial development.
✕Less likely to help
Patients not undergoing facial growth-related orthodontic planning or those outside the 18-year-old age range may see limited benefit.
What this research studies
This project will create a large collection of standardized facial landmarks from hundreds of X-ray images to map how faces grow. Researchers will train machine learning models on lateral and frontal images to forecast future growth in the face, focusing on the sagittal and coronal planes. The work will combine data from existing orthodontic image collections with real-time patient data from the University of the Pacific clinic to build and test prediction tools that clinicians can use in planning treatments. The goal is to provide clinicians with individualized, data-driven growth predictions to improve timing and outcomes of orthodontic care.
Why it matters
Potential benefit: If successful, patients could receive more precise, personalized orthodontic treatment plans based on reliable growth forecasts.
How similar studies have gone: Precedents exist for applying machine learning to cephalometric analysis, with growing evidence that data-driven models can aid planning, though individualized growth prediction is still emerging.
Where this research is happening
-
University of the Pacific-Stockton
Stockton, United States Active
Researchers
- Principal investigator: Suh, Heeyeon — University of the Pacific-Stockton
- Study coordinator: Suh, Heeyeon
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.