Using deep learning to assess the risk of oral cancer in patients with potentially malignant disorders
Official title: Deep Learning Image Analysis Algorithms to Improve Oral Cancer Risk Assessment for Oral Potentially Malignant Disorders
Funded at Yale University · NIH-11120922 · Data updated 2026-11-12
This study is looking to make it easier for doctors to understand the risk of oral cancer in patients with conditions like leukoplakia by using advanced technology to analyze tissue images, which could help improve how we prevent and treat these issues.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Yale University NIH-funded |
| Lab location | 1 site (New Haven, United States) |
| Project start | May 1, 2023 |
| Project end | April 30, 2026 |
| Funding year | 2025 |
| Award (this year) | $684,002 |
| NIH project number | 5R01DE030656-05 |
| Project ID | NIH-11120922 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals diagnosed with oral potentially malignant disorders, particularly those with leukoplakia.
✕Less likely to help
Patients without oral potentially malignant disorders or those with conditions unrelated to oral cancer risk may not benefit from this research.
What this research studies
This research focuses on improving the assessment of oral cancer risk in patients with oral potentially malignant disorders (OPMD) like leukoplakia. By utilizing advanced deep learning algorithms on histological images, the study aims to create a more consistent and accurate risk scoring system. This approach will analyze various cellular and tissue features to better predict the likelihood of progression to oral squamous cell carcinoma. The findings will be validated through a large retrospective study and prospective trials, potentially leading to enhanced treatment and prevention strategies.
Why it matters
Potential benefit: If successful, this research could lead to more accurate risk assessments and better treatment options for patients with oral potentially malignant disorders.
How similar studies have gone: Previous research has shown promise in using deep learning for medical image analysis, suggesting that this approach could yield significant advancements in risk assessment for oral cancer.
Where this research is happening
-
Yale University
New Haven, United States Active
Researchers
- Principal investigator: Pickering, Curtis — Yale University
- Study coordinator: Pickering, Curtis
Related research
Other NIH-funded projects on the same topics.
-
Integrative histo-genomic analysis to guide cancer risk and targeted therapies with interpretable AI
Researchers are developing an advanced AI-powered approach to map genetic and cellular patterns across breast and cervical cancers to help tailor treatments.
-
Mobile platform using AI to screen for oral cancer in low-resource communities
A low-cost, AI-powered mobile tool helps frontline healthcare workers screen for oral cancer and triage patients in communities with limited access to specialists.
-
Predicting how antimalarial drug resistance spreads with AI
A powerful AI model will be built to forecast how resistance to antimalarial drugs might spread in Africa and beyond, helping clinicians and public health teams stay ahead of outbreaks.
-
Endosomal signaling and cancer pain control in oral cancer
A study to understand how signals inside nerve cells and supportive cells contribute to pain in people with oral cancer.
-
Self-supervised deep learning for biological sequences
A deep learning project uses self-supervised methods to better understand biological sequences and related data, with the goal of advancing biology and medicine.
-
Affordable AI-enabled point-of-care tool for quick oral cancer checks
A low-cost AI-powered imaging module helps clinicians screen for oral cancer at the point of care, especially in community and resource-limited settings.
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.