Using deep learning to predict outcomes of uterine fibroid embolization from MRI scans
Official title: Predicting Outcomes for Uterine Fibroid Embolization by using Deep Learning of Paired MRI Scans
Funded at Weill Medical Coll of Cornell Univ · NIH-10724513 · Data updated 2026-11-12
This study is looking at how advanced computer programs can help doctors understand how well a treatment for uterine fibroids works by comparing MRI scans taken before and after the procedure, with the hope of making it easier for everyone, especially those from underserved communities, to get the right care.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Weill Medical Coll of Cornell Univ NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project start | August 21, 2023 |
| Project end | July 31, 2025 |
| Funding year | 2023 |
| Award (this year) | $466,125 |
| NIH project number | 1R21HD112975-01 |
| Project ID | NIH-10724513 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are women diagnosed with uterine fibroids who are considering or have undergone uterine fibroid embolization.
✕Less likely to help
Patients who do not have uterine fibroids or are not candidates for embolization procedures may not benefit from this research.
What this research studies
This research investigates how deep learning algorithms can analyze paired MRI scans of patients before and after uterine fibroid embolization (UFE) to predict treatment outcomes. By leveraging a dataset of up to 700 patients, the study aims to develop a scoring system that objectively assesses the effectiveness of UFE. This approach seeks to address biases in referrals for minimally invasive procedures, particularly among minority and lower socio-economic groups. The goal is to improve patient access to appropriate treatments and enhance decision-making in clinical settings.
Why it matters
Potential benefit: If successful, this research could lead to more accurate predictions of treatment outcomes for women with uterine fibroids, potentially improving their quality of life.
How similar studies have gone: Similar research using machine learning in medical imaging has shown promise in improving treatment outcomes and reducing biases in healthcare referrals.
Where this research is happening
-
Weill Medical Coll of Cornell Univ
New York, United States Active
Researchers
- Principal investigator: Mosadegh, Bobak — Weill Medical Coll of Cornell Univ
- Study coordinator: Mosadegh, Bobak
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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.