Established by the International Society for Heart and Lung Transplantation (ISHLT) in 1983, the International Thoracic Organ Transplant Registry is the world’s largest source of thoracic transplant data. UNOS has served as the Registry’s Data Coordinating Center since 1992.

After the European Union implemented its General Data Protection Regulation (GDPR) in 2018, Registry data collection was temporarily paused while a new international data-sharing framework was developed. The Registry was later relaunched under a new agreement, and the 2025 and 2026 ISHLT annual reports are based on data collected through this updated framework.

The Registry’s relaunch has re-established an important source of international transplant data and enabled new analyses based on contemporary transplant practices.

UNOS’ role

As the Registry’s Data Coordinating Center, UNOS:

• Maintains the system used by Registry participants to submit data
• Oversees Registry operations and data quality
• Conducts analyses for annual reports, publications and research

Why this matters

UNOS Research helps turn global transplant data into insights that improve patient care and support research. UNOS collaborates closely with clinicians, researchers and Registry leaders worldwide to support participation and strengthen collaboration. Key analyses include:

• Tracking long-term survival after heart and lung transplantation
• Comparing outcomes across regions and patient populations
• Monitoring trends in donor and recipient characteristics
• Supporting international research studies and clinical guidelines
• Producing the annual ISHLT Registry reports used by transplant professionals around the world

Key takeaway

For more than 30 years, UNOS has managed the world’s largest heart and lung transplant registry. Through global data coordination, quality oversight, and analytics, UNOS helps generate evidence that improves transplant care and advances research.

Since the Registry’s relaunch, UNOS has supported the collection and analysis of the data used in the 2025 and 2026 ISHLT annual reports. The 2025 report includes the Registry’s largest international cohort analyses of donation after circulatory death (DCD) heart and lung transplant recipients, providing important insights into this growing donor population.

Looking ahead

UNOS will continue serving as the Registry’s Data Coordinating Center through 2026 and is expected to provide analytical and publication support for the 2027 ISHLT Registry reports. 

About UNOS research

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

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.