Using AI to improve kidney imaging in children with prenatal hydronephrosis
Official title: Deep Learning of Renal Scans and Kidney Ultrasounds in Children with Antenatal Hydronephrosis
Funded at Cleveland Clinic Lerner Com-Cwru · NIH-11054980 · Data updated 2026-11-12
This study is working on using smart computer technology to make kidney ultrasounds for kids with antenatal hydronephrosis clearer and more accurate, helping doctors better spot any problems and avoid unnecessary surgeries or delays in treatment.
Quick facts
| Grant type | Career grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Cleveland Clinic Lerner Com-Cwru NIH-funded |
| Lab location | 1 site (Cleveland, United States) |
| Project start | March 15, 2025 |
| Project end | January 31, 2030 |
| Funding year | 2025 |
| Award (this year) | $155,011 |
| NIH project number | 1K08DK137042-01A1 |
| Project ID | NIH-11054980 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are infants and young children under 11 years old who have been diagnosed with antenatal hydronephrosis.
✕Less likely to help
Patients who do not have antenatal hydronephrosis or related urinary tract issues may not benefit from this research.
What this research studies
This research focuses on enhancing the interpretation of kidney ultrasounds and renal scans in children diagnosed with antenatal hydronephrosis (ANH). By applying advanced machine learning techniques, the project aims to create clearer and more accurate anatomic signatures from imaging data, which can help in identifying urinary tract obstructions. This is particularly important as current methods can lead to confusion and variability in diagnosis, potentially resulting in unnecessary surgeries or delayed treatments. The goal is to improve clinical decision-making and patient outcomes through better imaging analysis.
Why it matters
Potential benefit: If successful, this research could lead to more accurate diagnoses and treatment plans for children with kidney issues, ultimately preserving kidney function and improving health outcomes.
How similar studies have gone: Previous research has shown promise in using machine learning for medical imaging, indicating potential success for this novel approach in pediatric urology.
Where this research is happening
-
Cleveland Clinic Lerner Com-Cwru
Cleveland, United States Active
Researchers
- Principal investigator: Weaver, John — Cleveland Clinic Lerner Com-Cwru
- Study coordinator: Weaver, John
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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.