AI-powered imaging and visualization to support safer surgery and ICU care
TRD3: Data Analytics and Intelligent Systems (AI-ML-DL-Visualization)
['FUNDING_OTHER'] · UNIVERSITY OF CALIFORNIA AT DAVIS · NIH-11333885
This project builds AI tools and improved imaging devices to give surgeons and intensive-care teams clearer real-time information to make faster decisions.
Quick facts
| Phase | ['FUNDING_OTHER'] |
|---|---|
| Study type | Nih_funding |
| Sex | All |
| Sponsor | UNIVERSITY OF CALIFORNIA AT DAVIS (nih funded) |
| Locations | 1 site (DAVIS, UNITED STATES) |
| Trial ID | NIH-11333885 on ClinicalTrials.gov |
What this research studies
From my point of view as a patient, the team is creating new imaging devices and training AI to combine pictures, pathology, and other medical data so doctors can see what matters during procedures. They will jointly optimize the camera and the machine-learning software to produce higher-quality signals for clinical use, including improved fluorescence lifetime imaging and optical spectroscopy instruments. The team will design easy-to-understand visual displays so clinical teams can grasp complex, multimodal information quickly. Finally, they will develop AI that links different data types to predict outcomes and work to fit these tools into everyday clinical workflow.
Who could benefit from this research
Good fit: Patients undergoing surgery or receiving intensive care who might benefit from real-time imaging and decision support are the most relevant candidates.
Not a fit: People receiving routine outpatient care without need for intraprocedural imaging or intensive monitoring are unlikely to benefit directly.
Why it matters
Potential benefit: If successful, these tools could help clinicians make faster, more accurate decisions during surgery and in intensive care, potentially lowering complications and improving recovery.
How similar studies have performed: Related AI and imaging approaches have shown promise in pilot studies, but combining optimized hardware, multimodal integration, and clinician-facing visualization at scale is relatively new.
Where this research is happening
DAVIS, UNITED STATES
- UNIVERSITY OF CALIFORNIA AT DAVIS — DAVIS, UNITED STATES (ACTIVE)
Researchers
- Principal investigator: QI, JINYI — UNIVERSITY OF CALIFORNIA AT DAVIS
- Study coordinator: QI, JINYI
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.