Using machine learning to detect shoulder muscle problems
Official title: Clinical evaluation of a commercially viable machine learning algorithm to automatically detect shoulder muscle pathology
Funded at Springbok, INC. · NIH-10918167 · Data updated 2026-11-12
This study is working on a smart computer program that helps doctors check the health of shoulder muscles for people getting surgery to fix their rotator cuffs, making it easier to see who will benefit most from the surgery.
Quick facts
| Grant type | Sbir 2 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Springbok, INC. NIH-funded |
| Lab location | 1 site (Charlottesville, UNITED STATES) |
| Project start | September 27, 2021 |
| Project end | August 31, 2025 |
| Funding year | 2024 |
| Award (this year) | $796,748 |
| NIH project number | 5R44AR078720-04 |
| Project ID | NIH-10918167 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals scheduled for rotator cuff repair surgery who may have varying degrees of muscle atrophy or fat infiltration.
✕Less likely to help
Patients who do not require rotator cuff surgery or have no issues related to shoulder muscle pathology may not benefit from this research.
What this research studies
This research focuses on developing a machine learning algorithm that can automatically assess shoulder muscle health, particularly for patients undergoing rotator cuff repair surgeries. By analyzing imaging data, the algorithm aims to provide a more accurate evaluation of muscle atrophy and fat infiltration, which are critical factors influencing surgical outcomes. This approach seeks to replace traditional qualitative scoring methods that are often unreliable, thereby improving pre-operative decision-making for patients. The goal is to create a clinically viable tool that can be used in routine practice to better predict which patients will benefit from surgery.
Why it matters
Potential benefit: If successful, this research could lead to more personalized treatment plans for patients undergoing shoulder surgeries, potentially improving their recovery outcomes.
How similar studies have gone: Other research has shown promise in using machine learning for medical imaging analysis, indicating that this approach could be effective in improving diagnostic accuracy.
Where this research is happening
-
Springbok, INC.
Charlottesville, United States Active
Researchers
- Principal investigator: Riem, Lara Isabella — Springbok, INC.
- Study coordinator: Riem, Lara Isabella
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.