Using deep learning to improve lung ultrasound assessments for heart failure in emergency patients
Official title: Deep Learning Assisted Scoring of Point of Care Lung Ultrasound for Acute Decompensated Heart Failure in the Emergency Department
Funded at Brigham and Women's Hospital · NIH-10888388 · Data updated 2026-11-12
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 start | July 15, 2023 |
| Project end | June 30, 2026 |
| Funding year | 2024 |
| Award (this year) | $358,000 |
| NIH project number | 5R21EB034075-02 |
| Project ID | NIH-10888388 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients presenting with symptoms of acute decompensated heart failure in the Emergency Department.
✕Less likely to help
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.
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.
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 gone: 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
-
Brigham and Women's Hospital
Boston, United States Active
Researchers
- Principal investigator: Kapur, Tina — Brigham and Women's Hospital
- Study coordinator: Kapur, Tina
Related research
Other NIH-funded projects on the same topics.
-
Personalized CRT planning using advanced heart imaging and AI
A scientist is testing a new AI-assisted imaging approach to help doctors choose the best CRT device (BIVP or LBBAP) for heart failure patients with specific conduction problems.
-
Portable ultrasound that detects brain emboli with AI for stroke risk monitoring
A new compact ultrasound device uses deep learning to automatically detect small clots in the brain, helping people at risk of stroke monitor themselves outside the clinic.
-
Predicting future heart failure with health records and wearables
This project uses medical records, clinic ECGs, and smartwatch heart-rate data to try to predict who may develop heart failure within the next year.
-
Using AI to Understand and Improve Treatment for a Type of Heart Failure
This project uses advanced computer methods to better understand different forms of heart failure with preserved ejection fraction, aiming to find the most effective treatments for each person.
-
Using deep learning to improve heart failure care quality and reduce disparities.
This study is looking at how to improve care for people with heart failure, especially for Black individuals who often face more challenges, by using advanced technology to better understand their health records and find ways to provide better support and treatment.
-
Predicting when heart failure might occur using data from wearables and health records
This study is looking to help people at risk of heart failure by using smart technology and health records to predict when it might happen, giving doctors a chance to step in and help up to a year in advance.
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.