Creating a machine learning tool to analyze the velopharyngeal mechanism using MRI
Development of an MRI Guided Machine Learning Algorithm to Assess the Velopharyngeal Mechanism
This study is working on a new way to use MRI scans to help doctors better understand how the throat works in kids with cleft palate, so they can improve surgery and speech outcomes for those who have trouble with their speech.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Virginia NIH-funded |
| Lab location | 1 site (Charlottesville, United States) |
| Project ID | NIH-10992677 on NIH RePORTER |
What this research studies
This research aims to develop an advanced machine learning algorithm that utilizes MRI technology to assess the velopharyngeal mechanism in children with conditions like cleft palate. By analyzing 3D images, the project seeks to better understand the anatomical features that contribute to velopharyngeal dysfunction (VPD), which can lead to speech issues. The goal is to improve pre-operative assessments and surgical outcomes by providing a more objective and patient-specific evaluation of the VP anatomy. This innovative approach addresses the limitations of previous studies that relied on smaller sample sizes and less efficient analysis methods.
Who could benefit from this research
Good fit: Ideal candidates for this research are children aged 0-11 years who have velopharyngeal or palatal anomalies, particularly those with repaired cleft palates.
Not a fit: Patients who do not have velopharyngeal dysfunction or related anatomical issues may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to more effective surgical interventions for children with velopharyngeal dysfunction, improving their speech and overall quality of life.
How similar studies have performed: While previous research has explored the anatomy of the velopharyngeal mechanism, this project represents a novel approach by integrating machine learning with advanced imaging techniques.
Where this research is happening
Charlottesville, United States
- University of Virginia — Charlottesville, United States (Active)
Researchers
- Principal investigator: Mason, Kazlin — University of Virginia
- Study coordinator: Mason, Kazlin
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 below.