Understanding ankle osteoarthritis severity using advanced imaging and machine learning
Classification of Ankle Osteoarthritis Severity from Weightbearing Computed Tomography Using Statistical Shape Modeling and Machine Learning
['FUNDING_CAREER'] · UNIVERSITY OF UTAH · NIH-10893330
This study is looking to make it easier for doctors to understand how severe ankle osteoarthritis is by using special 3D imaging techniques, which could help improve treatment options for patients with advanced stages of the condition.
Quick facts
| Phase | ['FUNDING_CAREER'] |
|---|---|
| Study type | Nih_funding |
| Sex | All |
| Sponsor | UNIVERSITY OF UTAH (nih funded) |
| Locations | 1 site (SALT LAKE CITY, UNITED STATES) |
| Trial ID | NIH-10893330 on ClinicalTrials.gov |
What this research studies
This research focuses on improving the classification of ankle osteoarthritis (OA) severity by utilizing weightbearing computed tomography and advanced statistical shape modeling techniques. The approach involves analyzing 3D anatomical variations in the ankle joint to better understand the disease's progression and impact on patients. By integrating machine learning, the study aims to develop a more accurate and quantitative clinical tool for assessing ankle OA, which could lead to improved treatment strategies. Patients with end-stage ankle OA will be the primary focus, as the research seeks to enhance clinical evaluations beyond traditional 2D imaging methods.
Who could benefit from this research
Good fit: Ideal candidates for this research are individuals aged 65 and older who are experiencing end-stage ankle osteoarthritis.
Not a fit: Patients with early-stage ankle osteoarthritis or those without significant joint issues may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to more precise assessments of ankle osteoarthritis, ultimately improving treatment outcomes for patients.
How similar studies have performed: Other research has shown promise in using advanced imaging and machine learning for similar orthopedic conditions, indicating potential for success in this approach.
Where this research is happening
SALT LAKE CITY, UNITED STATES
- UNIVERSITY OF UTAH — SALT LAKE CITY, UNITED STATES (ACTIVE)
Researchers
- Principal investigator: LENZ, AMY L. — UNIVERSITY OF UTAH
- Study coordinator: LENZ, AMY L.
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.