AI in Organ Matching and Kidney Transplantation 4790

Abstract Description Kidney transplantation is a treatment of choice for individuals with end stage renal disease (ESRD). Risk factors linked with 1-year graft loss are associated with genetic mismatched between recipient and donor HLA type. In addition, recipient and donor age, living vs. deceased donor, and pre-existing health status, all impact the organ survival. Thus, matching appropriate donor-recipient pairs is a key to successful organ transplant outcome. The United Network for Organ Sharing (UNOS), uses complex computerized programs to identify the best match recipient for the available donor organ. Artificial intelligence (AI) has provided a significant contribution in kidney match of unrelated donors. AI uses matching algorithms based on extensive database of registered donor and recipient’s profiles submitted to the UNOS registry, such as blood type, HLA match and other relevant markers to identify an optimal match. The data are used for predictive modeling based on patient health status, donor organ characteristics, surgical procedures, etc., the AI estimates postoperative complications. Furthermore, deep machine learning pathology associated with immune response markers, and response to therapies, are included in the future studies. In conclusion, the AI algorithms used from a donor-recipient registry empowers identification of compatible donor for a suitable recipient. Funding Sources None. Topic Categories Computational and Systems Immunology (COMP)