According to Dr Pedro Antonio Gutiérrez, from the research team in Córdoba, the method has been tested with liver transplants and carries a 76% reliability level.
The current method, known as MELD, only measures the severity of a patient's liver disorder and no other factors, neither does it measure the donor's own physical makeup.
Dr Gutiérrez says this can lead to 'unfair situations' – those with less-serious conditions having to wait many more years for transplants, whilst good organs are wasted when priority candidates' bodies reject them.
The new model, which is compatible with MELD, takes into account the donor's and patient's individual characteristics and features of the transplant itself, covering variables such as body-mass index (BMI), whether the transplant is partial, from a live donor, or a full liver from a donor in 'asistolia' or with no heartbeat, plus the length of time the organ is in refrigeration, and whether or not the donor and patient suffer other health conditions, including hepatitis and diabetes.
The AI system was developed using data provided by Kings' College University Hospital in London and employs mathematical formulae to reach a probability of the donor and patient being compatible.
Its implementation into common use could be slow, warns Dr Gutiérrez, however.
“The MELD system is very widespread, and replacing it will not be an easy task,” he admits.
“Our idea is to start applying it in the near future in a few pioneering hospitals in Spain, such as the Reina Sofía here in Córdoba.”
Researchers on the team come from the Reina Sofía itself, and also from the Faculty of IT and Numerical Analysis at Córdoba University (UCO).
As yet, the system has not been trialled with other organs or with substances such as bone marrow or stem cells.
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