Keywords: Gray Matter, Machine Learning/Artificial Intelligence
To evaluate the dynamic change in overall brain health in liver transplantation (LT) recipients, we constructed a deep learning-based brain age prediction model to measure the longitudinal changes of ‘brain age’ before and one, three, and six months after surgery. The LT recipients’ brain age showed an inverted U-shaped change pattern in the early stages after transplantation. In addition, brain aging was aggravated within one month after surgery, and the patients with a history of overt hepatic encephalopathy were particularly affected. Therefore, the age prediction model can be used to monitor the post-surgical brain function recovery trajectory in LT recipients.
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