Using infrared imaging and machine learning to assess oral precancerous lesions
Official title: Infrared Spectroscopic Imaging and Machine Learning for Risk Stratification of Oral Epithelial Dysplasia
Funded at University of Missouri Kansas City · NIH-10755721 · Data updated 2026-11-12
This study is testing a new way to help doctors diagnose a condition called oral epithelial dysplasia, which can sometimes lead to cancer, by using special imaging technology and computer analysis to get more accurate results, so patients can get better care and support.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Missouri Kansas City NIH-funded |
| Lab location | 1 site (Kansas City, United States) |
| Project start | January 1, 2023 |
| Project end | December 31, 2025 |
| Funding year | 2024 |
| Award (this year) | $198,716 |
| NIH project number | 5R21DE032560-02 |
| Project ID | NIH-10755721 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals diagnosed with oral epithelial dysplasia or those at risk for developing oral squamous cell carcinoma.
✕Less likely to help
Patients without any oral lesions or those with established oral squamous cell carcinoma may not benefit from this research.
What this research studies
This research investigates a new method for diagnosing oral epithelial dysplasia (OED), a precancerous condition that can lead to oral squamous cell carcinoma (OSCC). By utilizing Fourier transform infrared (FTIR) spectroscopic imaging combined with machine learning algorithms, the study aims to provide a more objective and quantitative assessment of OED risk. This approach seeks to improve the accuracy of diagnosis and help identify patients at higher risk for malignant transformation, ultimately leading to better management of oral lesions. Patients will undergo biopsies, and their tissue samples will be analyzed using advanced imaging techniques to gather detailed biochemical information.
Why it matters
Potential benefit: If successful, this research could lead to earlier and more accurate diagnoses of precancerous oral lesions, improving patient outcomes and treatment options.
How similar studies have gone: While the use of machine learning in medical diagnostics is gaining traction, this specific application of FTIR imaging for OED risk stratification is relatively novel and has not been extensively tested.
Where this research is happening
-
University of Missouri Kansas City
Kansas City, United States Active
Researchers
- Principal investigator: Wang, Rong — University of Missouri Kansas City
- Study coordinator: Wang, Rong
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.