Using advanced modeling to improve nerve stimulation treatments for bladder issues
Official title: A new hybrid modeling framework combining biophysics and deep learning to predict and optimize peripheral neuromodulation outcomes in lower urinary tract disease
Funded at Emory University · NIH-11066155 · Data updated 2026-11-12
This study is working on a new way to improve treatments for bladder problems by using a mix of science and smart computer technology to find the best nerve stimulation therapies, which could help patients get better care faster without relying too much on animal testing.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Emory University NIH-funded |
| Lab location | 1 site (Atlanta, United States) |
| Project start | April 4, 2024 |
| Project end | June 30, 2027 |
| Funding year | 2024 |
| Award (this year) | $634,078 |
| NIH project number | 7R01DK133605-03 |
| Project ID | NIH-11066155 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals experiencing lower urinary tract dysfunction who may benefit from neuromodulation therapies.
✕Less likely to help
Patients with urinary tract issues not related to neuromodulation or those who do not respond to nerve stimulation therapies may not benefit from this research.
What this research studies
This research aims to enhance treatments for lower urinary tract dysfunction by developing a new hybrid modeling framework that combines biophysics and deep learning. By creating a predictive model, the researchers hope to simulate various nerve stimulation therapies more efficiently, allowing them to identify the most promising treatment options without the need for extensive animal testing. This approach could significantly speed up the development of targeted neuromodulation therapies, ultimately improving patient outcomes. The project will integrate detailed physiological models with advanced AI techniques to better understand and predict how the bladder responds to different stimulation parameters.
Why it matters
Potential benefit: If successful, this research could lead to more effective and personalized treatments for patients suffering from lower urinary tract dysfunction.
How similar studies have gone: While the integration of biophysics and deep learning in this context is innovative, similar modeling approaches have shown promise in other areas of medical research.
Where this research is happening
-
Emory University
Atlanta, United States Active
Researchers
- Principal investigator: Danziger, Zachary C — Emory University
- Study coordinator: Danziger, Zachary C
Related research
Other NIH-funded projects on the same topics.
-
Integrative histo-genomic analysis to guide cancer risk and targeted therapies with interpretable AI
Researchers are developing an advanced AI-powered approach to map genetic and cellular patterns across breast and cervical cancers to help tailor treatments.
-
Predicting how antimalarial drug resistance spreads with AI
A powerful AI model will be built to forecast how resistance to antimalarial drugs might spread in Africa and beyond, helping clinicians and public health teams stay ahead of outbreaks.
-
Self-supervised deep learning for biological sequences
A deep learning project uses self-supervised methods to better understand biological sequences and related data, with the goal of advancing biology and medicine.
-
Mobile phone imaging and AI to screen oral cancers in low-resource settings
A low-cost mobile phone device with artificial intelligence is being developed to help frontline health workers screen for oral cancer early in low-resource areas.
-
Linking Genetic Variants to Real-Life Health Effects with Smart AI
A new AI-powered approach will predict how genetic differences affect health across multiple levels to help doctors understand diseases and tailor care.
-
Integrative AI tools to map protein sequences to structures and functions
A scientist team will develop advanced AI methods to better connect protein sequences, their 3D shapes, and how they work, using large biological data from sequencing and structures.
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.