Using deep learning to improve lung ultrasound assessments for heart failure in emergency patients
Deep Learning Assisted Scoring of Point of Care Lung Ultrasound for Acute Decompensated Heart Failure in the Emergency Department
This study is looking at how to make it easier and faster for doctors in the Emergency Department to check for heart failure in patients by using smart technology to analyze lung ultrasound images, helping you get the care you need more quickly.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Brigham and Women's Hospital NIH-funded |
| Lab location | 1 site (Boston, United States) |
| Project ID | NIH-10888388 on NIH RePORTER |
What this research studies
This research focuses on enhancing the assessment of acute decompensated heart failure (ADHF) in patients who are waiting in the Emergency Department (ED) by utilizing deep learning technology. The goal is to automate the scoring of lung ultrasounds, which are critical for diagnosing pulmonary congestion in these patients. By streamlining the process, the research aims to reduce the time patients spend in the ED and improve their overall care and outcomes. The approach involves developing algorithms that can accurately count B-lines in lung ultrasound images, which are indicators of heart failure, thereby assisting busy ED physicians in making quicker and more accurate decisions.
Who could benefit from this research
Good fit: Ideal candidates for this research are patients presenting with symptoms of acute decompensated heart failure in the Emergency Department.
Not a fit: Patients who do not present with acute decompensated heart failure or those who are not admitted to the Emergency Department may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to faster and more accurate diagnoses of heart failure, ultimately improving patient outcomes and reducing wait times in emergency settings.
How similar studies have performed: Other research has shown promise in using automated imaging techniques for diagnostic purposes, indicating that this approach could be effective.
Where this research is happening
Boston, United States
- Brigham and Women's Hospital — Boston, United States (Active)
Researchers
- Principal investigator: Kapur, Tina — Brigham and Women's Hospital
- Study coordinator: Kapur, Tina
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.