Artificial Intelligence: How AI Can Help Improve The Kidney Transplant Network
AI can make a huge difference to the speed of matching an available kidney with a patient and transplant network.
How to give desperate patients a better chance to get a kidney? Researchers at the Missouri University of Science and Technology (S&T) have started a project that utilizes AI to process data inside a kidney transplant network. It will help remove the current difficulties of kidney transplants – thousands of patients on a waiting list, while a large number of kidneys are discarded because donor matching could not be completed in time. (The Rolla Daily News)
Partnership with Saint Louis University Hospital
Dr. Casey Canfield, primary investigator and assistant professor of engineering management and system engineering at S&T, says the project started after the researchers contacted transplant experts at Saint Louis University Hospital for transplant data.
It turned out that the latter always wanted to deploy AI but didn’t know how to go about it.
As a result, a collaboration started between the two institutions. The S&T team is working with three researchers from St. Louis University: Dr. Mark Schnitzler, professor of surgery; Dr. Krista Lentine, professor of medicine; and Dr. Henry Randall, professor of surgery and executive director of the Center for Abdominal Transplantation.
Canfield’s team includes Dr. Daniel Shank, assistant professor of psychological science, Dr. Cihan Dagli, founder and director of systems engineering at Missouri S&T; Dagli’s Ph.D. student Lirim Ashiku, who is a distance student working on AI; and Canfield’s master’s student Harishankar V. Subramanian, as well as several undergraduate research assistants.
How it works
The S&T team will use algorithms and develop the AI model for the transplant data. It will also design a suitable interface for the AI system.
The system will match donors and patients in the order of the waiting list. The patients at the head of the list will get a higher priority in matching. However, there could be situations when a patient lower down the list could accept a higher-risk organ in the interest of a faster transplant.
“Our research will test people’s responses to different forms of AI recommendations under uncertain conditions similar to the organ transplant situation, such as AIs recommending car repairs or identifying specific objects in an image,” says Dr. Shank. “Ultimately, when the AI system recommends a higher-risk organ, it will need to communicate why that recommendation is being made so patients and transplant center personnel will understand both the reasoning behind and the risks associated with each recommendation.”
According to Canfield, the researchers will also invite inputs from all stakeholders currently involved in the kidney transplant process with support from Mid-America Transplant. These people could include organ procurement professionals, local transplant centers, and community stakeholders.
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