Abstract:
Graft rejection remains a significant challenge in organ transplantation. This study proposes a deep learning (DL)-based framework to assess graft rejection risk by analyzing CDC crossmatch imaging and FISH data. The framework aims to: Develop and optimize DL algorithms for image analysis and cell viability prediction. Train the system on a comprehensive dataset to identify patterns and image features linked to graft rejection. Validate the system’s performance through rigorous testing and comparison with clinical outcomes. Integrate the system seamlessly into the clinical workflow for efficient utilization. Evaluate the clinical impact of the system on patient outcomes, organ allocation, and graft survival rates. This DL-based approach has the potential to revolutionize graft rejection risk assessment, improving accuracy, efficiency, and ultimately, patient outcomes in organ transplantation.
