About the research

UNOS researchers explored whether artificial intelligence (AI) can help identify anatomical issues in donor kidneys before transplant. The system analyzes photos taken during kidney procurement to see whether the organs appear normal or have structural issues that could affect transplant success.

Using existing OPTN data, the UNOS team built a dataset where each donor had a set of photos, filtering out low-quality or irrelevant images. Donors were labeled as follows:

• No anatomical issues = both kidneys were successfully transplanted
• Anatomical issues = at least one organ was refused due to anatomy concerns

UNOS researchers then trained the AI model to review sets of donor photos and predict whether anatomical issues were present. The subject matter experts did not provide the model with any direct guidance–it learned from the images and outcomes alone.

Key findings

The AI model showed promising performance in identifying anatomical issues.

Performance metrics:

Why this matters

Deciding whether a donor kidney is suitable for transplant is a critical step in the transplant process.

This research shows that AI could help support clinical decision-making by automatically detecting anatomy issues and improving the consistency of identification.

Improvements in how organs are evaluated could help ensure more patients receive successful transplants.

What’s next

This work showed promising results, but the model still has room for improvement. Potential future work may include:

• Improving how the AI model is trained so it becomes more accurate
• Working with clinicians in the transplant community to better define and label anatomical issues

With further development, tools like this could help improve the pre-transplant organ evaluation process and support better outcomes for patients.

About UNOS research

UNOS conducts research as part of its mission to save and transform lives through research, innovation and collaboration.

The data reported here have been supplied by UNOS as the contractor for the Organ Procurement and Transplantation Network (OPTN). The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy of or interpretation by the OPTN or the U.S. Government.

This study used data from the Organ Procurement and Transplantation Network (OPTN). The OPTN data system includes data on all donors, wait-listed candidates, and transplant recipients in the US, submitted by the members of the Organ Procurement and Transplantation Network (OPTN). The Health Resources and Services Administration (HRSA), U.S. Department of Health and Human Services provides oversight to the activities of the OPTN contractor.

Julie Rodriguez knows firsthand how challenging it can be to staff a transplant quality team. As Transplant Quality and Patient Safety Manager at Intermountain Transplant Services, she kept hearing the same concern echoed by peers across the country: quality teams were structured and used differently from one transplant center to another. “There was a lot of chatter on the transplant listserv regarding how transplant teams are being used [at different centers],” Rodriguez says.

A shared challenge across transplant centers

Without a clear picture of how other transplant programs operated, it was difficult to know whether a program was appropriately staffed, or to advocate for change.

Rodriguez teamed up with colleagues from the Organ Donation and Transplantation Alliance’s Transplant Quality Staffing Collaborative to address this gap. The Collaborative partnered with UNOS to design a survey that captures how transplant quality teams are staffed and how they operate.

Turning survey results into actionable data

UNOS invited all quality teams to complete the Transplant Quality Staffing Survey^SM. Participating centers can access the results through a new interactive Transplant Quality Dashboard^SM, designed to help transplant programs benchmark their staffing models against peers across the country.

Rodriguez says the survey and dashboard have changed how her team evaluates staffing, identifies gaps and talks with leadership about the resources they need to support their program. “It gives you some good tools to go to your leadership and say, ‘We need to grow our quality program based on how the quality teams are being used throughout the nation for transplant centers our size or larger,'” Rodriguez says. “It helps me to have those crucial conversations with leadership.”

Setting goals and tracking progress

Benchmarking data also has helped Intermountain set clearer goals and track progress. Being able to benchmark against other transplant centers is critical, Rodriguez says. “If we didn’t have that data … It’s hard to have a goal.” One of Rodriguez’s favorite features of the dashboard is how easy it is to use the visuals. Charts and graphics can be downloaded and dropped directly into presentations, making it simple to communicate key points. “A picture is worth 1,000 words,” she says. “We can go in and snip this data and then put it into our PowerPoints to be able to drive home a particular point or to explain why we need to change a metric.” Rodriguez credits UNOS Tools for helping Intermountain’s transplant program to grow. “When you know that you’re being benchmarked against programs that you [admire], I think that’s helped us extensively,” she says. “Our kidney program is now fourth in the nation, and our liver program is third. So, we’re doing something right and we’re using the UNOS Tools to be able to help us to create those goals for our team.”

Background

U.S. transplant programs are evaluated by the OPTN on several key metrics developed by the Scientific Registry for Transplant Recipients (SRTR). These metrics include:

• How often a transplanted organ is still functioning 90 days and one year after transplant
• Patient deaths while waiting for a transplant per time waiting on the list
• How often a transplant program accepts an organ offer compared to the number of offers they could have accepted and transplanted

What’s new in UNOS Predict 2.0

UNOS researchers and data analysts work to develop enhancements to Predict to better support transplants programs. Predict 2.0 launched in early 2026 including:

• Access to individual patient-level data for post-transplant survival and pre-transplant mortality
• Offer acceptance ratios for heart and kidney
• “What if” simulator to explore how different patient scenarios can affect program performance

Why this matters

Shaped by user feedback, these enhancements allow transplant programs to identify trends in their data, prepare for potential challenges, advocate for additional resources, and make informed, data-driven decisions to drive continuous improvement to help save and transform more lives.

Looking ahead

“Predict provides our transplant programs with the essential forward-looking view we need, moving beyond the rearview mirror. It delivers crucial risk-adjusted results, empowering the transplant teams to respond proactively and grow effectively. This allows us to appropriately expand our program’s reach and ultimately, helps us to serve more patients and families.”

— Colleen O’Donnell Flores, MHA, Director of Transplant Quality & Regulatory Affairs, Northwell Health Transplant Institute

UNOS is committed to continuing to improve UNOS Predict, and the team is continuing to review feedback from the community. Feedback and questions can be submitted to [email protected].