Improving ultrasound diagnostics for injured children using machine learning
Official title: Accurate and Reliable Diagnostics for Injured Children: Machine Learning for Ultrasound
Funded at University of California, San Francisco · NIH-11040998 · Data updated 2026-11-12
This study is working on using smart computer technology to make ultrasound tests better for doctors who are treating kids with belly injuries, so they can quickly and accurately decide how to help them.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California, San Francisco NIH-funded |
| Lab location | 1 site (San Francisco, United States) |
| Project start | March 15, 2023 |
| Project end | February 29, 2028 |
| Funding year | 2025 |
| Award (this year) | $162,432 |
| NIH project number | 5K23HD110716-03 |
| Project ID | NIH-11040998 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include children aged 0-11 years who have experienced abdominal trauma.
✕Less likely to help
Patients who do not have abdominal injuries or are outside the age range of 0-11 years may not benefit from this research.
What this research studies
This research focuses on enhancing the accuracy of ultrasound diagnostics for children with abdominal injuries by utilizing advanced machine learning techniques. The project aims to develop and validate new clinical decision rules that can be applied at the bedside, making it easier for healthcare providers to assess and treat injured children quickly and effectively. By integrating machine learning models with ultrasound technology, the research seeks to improve the reliability of diagnoses in emergency situations. The principal investigator, Dr. Aaron Kornblith, is working with a team of experts to ensure that the methods developed are both innovative and practical for clinical use.
Why it matters
Potential benefit: If successful, this research could lead to faster and more accurate diagnoses of abdominal injuries in children, potentially saving lives and improving treatment outcomes.
How similar studies have gone: Other research has shown promise in using machine learning for medical imaging, indicating that this approach could be effective in improving diagnostic accuracy.
Where this research is happening
-
University of California, San Francisco
San Francisco, United States Active
Researchers
- Principal investigator: Kornblith, Aaron Edward — University of California, San Francisco
- Study coordinator: Kornblith, Aaron Edward
Related research
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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.