Bridging Genomics and Real-World Data for AI/ML based prediction of Kidney graft longevity

The Challenge

Long-term graft survival after kidney transplantation remains challenging despite advances in immunosuppressive therapies. The client needed a solution to predict kidney graft rejection earlier, assess the dynamic immune environment post-transplant, and accurately evaluate donor-recipient compatibility, factoring in the importance of identifying non-invasive biomarkers and risk factors for personalized treatment.

‍

Our Solution

Agilisium developed an AI/ML-powered solution designed to bridge genomics and Real-World Data (RWD) for predictive insights into kidney graft longevity. This solution focuses on the following key aspects:

Risk Score Prediction

AI/ML models were developed to predict risk scores and summarize kidney graft survival across various time points.

Biomarker Definition

Identified and defined key biomarkers responsible for graft rejection, providing actionable insights into their biological roles.

‍

Donor-Recipient Compatibility

Leveraged AI/ML to recommend optimal donor-recipient pairs, reducing likelihood of rejection & improving long-term outcomes.

‍

Predictive Tool

Developed a comprehensive tool to predict transplant outcomes before, during, and after kidney transplantation, enabling clinicians to intervene earlier.

Key Impact

11%

Improvement in donor-recipient match prediction

The customer

A prestigious U.S.-based research institution globally recognized for its pioneering work in translating discoveries into life-saving therapies.

The Outcomes

Enabling Personalized Treatments and Better Patient Outcomes

Personalized Treatment Plans

Paves the way for highly personalized treatments by stratifying patients based on individualized risk factors, improving long-term graft survival rates.