Improving dental data analysis to reduce healthcare disparities.
Official title: FullMouth: Enhancing Dental Clinical Data and Reducing Disparities through Innovative ML Approaches.
Funded at University of Texas Hlth Sci Ctr Houston · NIH-11137246 · Data updated 2026-11-12
This study is looking at how to use smart computer techniques to better understand dental records, so we can improve dental care and make it more accessible for everyone.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Texas Hlth Sci Ctr Houston NIH-funded |
| Lab location | 1 site (Houston, United States) |
| Project start | September 1, 2024 |
| Project end | August 31, 2025 |
| Funding year | 2024 |
| Award (this year) | $722,040 |
| NIH project number | 1R56DE034086-01 |
| Project ID | NIH-11137246 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include patients receiving dental care who may benefit from improved data analysis and treatment recommendations.
✕Less likely to help
Patients who do not receive dental care or have no access to electronic health records may not benefit from this research.
What this research studies
This research focuses on enhancing the analysis of dental clinical data by utilizing advanced machine learning techniques. It aims to extract valuable insights from both structured and unstructured electronic health records (EHRs), which include detailed clinical notes and treatment plans. By addressing the challenges of incomplete and inconsistent data, the project seeks to improve the quality of dental care and reduce disparities in access and outcomes for patients. The methodology involves leveraging large language models to process and analyze vast amounts of dental data efficiently.
Why it matters
Potential benefit: If successful, this research could lead to improved dental care quality and reduced disparities in treatment access and outcomes for underserved populations.
How similar studies have gone: Other research has shown success in utilizing machine learning and natural language processing to analyze healthcare data, indicating a promising approach for this project.
Where this research is happening
-
University of Texas Hlth Sci Ctr Houston
Houston, United States Active
Researchers
- Principal investigator: Tokede, Oluwabunmi — University of Texas Hlth Sci Ctr Houston
- Study coordinator: Tokede, Oluwabunmi
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.